FoBSim: an extensible open-source simulation tool for integrated fog-blockchain systems

Abstract and Figures

A lot of hard work and years of research are still needed for developing successful Blockchain (BC) applications. Although it is not yet standardized, BC technology was proven as to be an enhancement factor for security, decentralization, and reliability, leading to be successfully implemented in cryptocurrency industries. Fog computing (FC) is one of the recently emerged paradigms that needs to be improved to serve Internet of Things (IoT) environments of the future. As hundreds of projects, ideas, and systems were proposed, one can find a great R&D potential for integrating BC and FC technologies. Examples of organizations contributing to the R&D of these two technologies, and their integration, include Linux, IBM, Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or method implementation, before the deployment phase, a suitable and accurate simulation environment is needed. Such validation should save a great deal of costs and efforts on researchers and companies adopting this integration. Current available simulation environments facilitate Fog simulation, or BC simulation, but not both. In this paper, we introduce a Fog-Blockchain simulator, namely FoBSim, with the main goal to ease the experimentation and validation of integrated Fog-Blockchain approaches. According to our proposed workflow of simulation, we implement different Consensus Algorithms (CA), different deployment options of the BC in the FC architecture, and different functionalities of the BC in the simulation. Furthermore, technical details and algorithms on the simulated integration are provided. We validate FoBSim by describing the technologies used within FoBSim, highlighting FoBSim's novelty compared to the state-of-the-art, discussing the event validity in FoBSim, and providing a clear walk-through validation. Finally, we simulate case studies, then present and analyze the obtained results, where deploying the BC network in the fog layer shows enhanced efficiency in terms of total run time and total storage cost.
Content may be subject to copyright.
FoBSim: an extensible open-source
simulation tool for integrated
fog-blockchain systems
Hamza Baniata and Attila Kertesz
Department of Software Engineering, University of Szeged, Szeged, Hungary
A lot of hard work and years of research are still needed for developing successful
Blockchain (BC) applications. Although it is not yet standardized, BC technology
was proven as to be an enhancement factor for security, decentralization, and
reliability, leading to be successfully implemented in cryptocurrency industries.
Fog computing (FC) is one of the recently emerged paradigms that needs to be
improved to serve Internet of Things (IoT) environments of the future. As hundreds
of projects, ideas, and systems were proposed, one can nd a great R&D potential
for integrating BC and FC technologies. Examples of organizations contributing to
the R&D of these two technologies, and their integration, include Linux, IBM,
Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or
method implementation, before the deployment phase, a suitable and accurate
simulation environment is needed. Such validation should save a great deal of
costs and efforts on researchers and companies adopting this integration. Current
available simulation environments facilitate Fog simulation, or BC simulation,
but not both. In this paper, we introduce a Fog-Blockchain simulator, namely
FoBSim, with the main goal to ease the experimentation and validation of integrated
Fog-Blockchain approaches. According to our proposed workow of simulation, we
implement different Consensus Algorithms (CA), different deployment options of
the BC in the FC architecture, and different functionalities of the BC in the
simulation. Furthermore, technical details and algorithms on the simulated
integration are provided. We validate FoBSim by describing the technologies used
within FoBSim, highlighting FoBSims novelty compared to the state-of-the-art,
discussing the event validity in FoBSim, and providing a clear walk-through
validation. Finally, we simulate case studies, then present and analyze the obtained
results, where deploying the BC network in the fog layer shows enhanced efciency in
terms of total run time and total storage cost.
Subjects Algorithms and Analysis of Algorithms, Computer Networks and Communications,
Distributed and Parallel Computing
Keywords Blockchain, Fog computing, Simulation
In light of the general tendency towards skepticism around Blockchain (BC) systems being
reliable, huge research and industrial projects are being encouraged to address issues and
vulnerabilities of those systems. This is because it is believed that a successful BC
deployment would denitely advance Internet-of-Everything (IoE) applications. Dubai,
How to cite this article Baniata H, Kertesz A. 2021. FoBSim: an extensible open-source simulation tool for integrated fog-blockchain
systems. PeerJ Comput. Sci. 7:e431 DOI 10.7717/peerj-cs.431
Submitted 31 October 2020
Accepted 15 February 2021
Published 16 April 2021
Corresponding author
Hamza Baniata,
Academic editor
Stefan Schulte
Additional Information and
Declarations can be found on
page 35
DOI 10.7717/peerj-cs.431
2021 Baniata and Kertesz
Distributed under
Creative Commons CC-BY 4.0
for example, has planned for being the rst smart city powered by BC (Smart Dubai
Department, 2020). China had launched, in late 2019, a BC-based smart city ID system
(Global Times, 2019), while it is planning to have its own ofcial digital currency
(Smartcity Press, 2019). Before that, Liberstad, a private smart city in Norway, has ofcially
adopted City Coin as its ofcial currency (
BC is a Distributed Ledger Technology (DLT) in the form of a distributed transactional
database, secured by cryptography, and governed by a consensus mechanism (Beck et al.,
2017). This technology was rst introduced as the backbone of the Bitcoin ecosystem in
2009 (, 2009). As BC got high reputation and attention among research and
industry communities, as well as governments, it has proven robustness against the
disadvantages of classical centralized systems. Furthermore, different versions, uses,
paradigms, and platforms were proposed, aiming to extend the deployment of BC beyond
cash and payment purposes.
Concerning smart things, homes, and cities, Fog Computing (FC) paradigms become
reality. FC is a horizontal, physical or virtual resource paradigm that resides between
smart end-devices and traditional cloud data centers (Markakis et al., 2017). FC is
conceptually an extension of the cloud at the edge of the network. Hence, most cloud
services should be introduced by the fog layer as well, except the fog provides better latency
Different reference architectures were proposed for the FC paradigm, e.g., by Habibi
et al. (2020),Dastjerdi et al. (2016),OpenFog Consortium (2017), and Cisco (Bonomi et al.,
2014). Nevertheless, they all have the same general properties of middling between end-
users and the clouds, providing cloud services at the edge of the network, managing
mobility issues, and introducing reliable and secure communications.
We have previously investigated the integration of BC with FC in Baniata & Kertesz
(2020). Accordingly, we concluded that such integration may ease the optimization of
several current Cloud-Edge issues, such as enhancing security, credibility, and resource
efciency. Also, decentralizing FC applications decreases the appearance of single points of
failure and the control of a centralized authority. However, we found that major challenges
still need more research efforts such as:
The lack of individual standardization of both technologies, FC and BC, which leads to
the lack of standardization of the integration of them.
Many privacy issues and threats remain, such as the location awareness property of fog
components, which raises some concerns.
Ironically, as FC enhances the latency of end-user applications, BC causes the exact
opposite, if the consensus mechanisms are not properly designed. Other major
issues may also represent barriers if this latency issue was not addressed, such as
authentication, scalability, and heterogeneity problems. This is because solving the
latency problem may require waiving some advantageous protocols or mechanisms
of FC.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 2/40
The aforementioned challenges may further lead to somewhat low trust levels of the BC-
FC integration, which is the main cause of the illegalization of BC technologies in
Consequently, the research and industry communities have been working hand-in-
hand to solve these major challenges, along with other technical issues. Such efforts require
reliable and exible simulation environments that can mimic real-life scenarios with
the lowest possible costs. Old, out-dated, or somewhat close simulation tools that were
initially implemented for classical Peer-to-Peer networks, such as PeerSim (Montresor &
Jelasity, 2009), may not be able to cover all the mechanisms of a modern BC system.
Although some recently proposed systems use PeerSim, such as Petri et al. (2020), it surly
required vast amount of changes, modications, and additions to redesign it into a BC
simulation tool.
In this paper, we propose a Fog-Blockchain simulation environment, called FoBSim,
that is able to simulate different integration scenarios of FC and BC. Concerning our main
contributions, we discuss and analyze the architectural elements of FC- and BC-based
systems, and present the modules, algorithms, and strategies implemented in FoBSim.
We also describe in detail the validation, the incentivization, and the conrmation
mechanisms deployed in the current version of FoBSim. To exemplify its utilization,
we discuss possible application scenarios of FC-BC integration, and we clarify how such
applications can be simulated and optimized using FoBSim. The abbreviations we use
within our paper are declared in Table 1, while the main properties of the current version
of FoBSim are as follows:
1. FoBSim provides different Consensus Algorithms (CA), namely Proof-of-Work (PoW),
Proof-of-Stake (PoS) and Proof-of-Authority (PoA) that are ready to be deployed in any
2. FoBSim facilitates the deployment of BC miners in the fog or end-user layer.
3. FoBSim allows different services to be reliably provided by the BC network, namely Data
Management, Identity Management, Computational Services (through Smart Contracts
(SC)), and Payment/Currency transfer Services.
4. FoBSim provides both, parallel execution and non-parallel execution, of mining
processing. While gossiping is optionally and efciently available so that the distributed
chain is consistent in different possible network topologies.
5. FoBSim is the rst simulation environment whose primary goal is to mimic integration
scenarios of FC and BC technologies.
The remainder of the paper is organized as follows: Related Workpresents and
discusses state-of-the-art simulation environments that are maybe suitable to simulate FC-
BC systems. To properly introduce FoBSim, we discuss, in detail, how FC architectural
elements are deployed in FC Architectural Elements. Additionally, we discuss the
categories of BC systems, each with its properties and components in BC Architectural
Elements. Accordingly, we propose the components, the algorithms, and the functions
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 3/40
of the FoBSim environment in The Fobsim Environment. To validate FoBSim,
we simulate some use cases and present the simulation results in Case Studies. Finally,
we present our future work and conclude in Conclusions.
Searching the literature for tools specically implemented for simulating FC-BC
integration scenarios, we found that no previous work has directly targeted our objective.
That is, we found several simulation tools that mimic fog-enhanced cloud systems,
IoT-Fog-Cloud scenarios, etc., and several tools that mimic BC scenarios, each with
specic constraints on the used CAs. Nevertheless, some proposals for IoT-BC
simulation tools can be somewhat related to our work. For example, the ABSOLUT tool,
investigated in Kreku et al. (2017), models the deployment of BCs in IoT environments.
Accordingly, some critical analysis were provided regarding network latency, effects of
miners number on the overall efciency of the IoT network, and simulation errors.
Liaskos, Anand & Alimohammadi (2020) proposed a general architecture that a BC
simulation needs to follow in order to be considered comprehensive. Further, some
properties were declared as necessary for encouraging the adoption and re-usability of the
simulation. The proposed architecture includes extensible connection strategies, BC nodes,
BC chains, Transactions (TXs) and Transaction pools, users, events, Blocks, and most
importantly Consensus mechanisms. Events can include different triggers to other
eventsthat may be performed by any entity of the network(such as TX/block arrival,
TX/block validation, connection requests, etc.). Also, Events need to be handled by concise
and well implemented strategies.
In light of the lack of simulation tools similar to our proposal, we found it more suitable
to present this section in two separate groups: namely FC simulation tools, and BC
simulation tools.
FC simulation tools
Recently, our research group has started to investigate the state-of-the-art related to cloud,
IoT and fog simulation tools in Markus & Kertesz (2020). Within this study, several
Table 1 Description of abbreviations used within the manuscript.
Abbreviation Description Abbreviation Description
BC Blockchain PoW Proof of Work
FC Fog Computing PoS Proof of Stake
IoT Internet of Things PoET Proof of Elapsed Time
CA Consensus Algorithm PoA Proof of Authority
IoE Internet-of-Everything TTP Trusted Third Party
DLT Distributed Ledger Technology P2P Peer-to-Peer
SC Smart Contracts TX Transaction
GUI Graphical User Interface TTL Time To Live
QoB Quality of Blockchain DAG Directed Acyclic Graph
PoG Proof of Generation MT Merkle Tree
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 4/40
simulation tools were classied, compared, and analyzed, such as the DockerSim tool
(Nikdel, Gao & Neville, 2017), FogNetSim++ (Qayyum et al., 2018), and EdgeCloudSim
(Sonmez, Ozgovde & Ersoy, 2018). Furthermore, technical details, advantages,
vulnerabilities, and software quality issues were also discussed.
Rahman et al. (2019) surveyed 15 simulation tools for cloud and data centers
networks scenarios. The tools were discussed and compared according to several criteria,
such as the Graphical User Interface (GUI) availability, the language with which the
simulator was implemented, and the communications model. Consequently, they
proposed the Nutshell tool which addresses some drawbacks that were ignored by most of
the surveyed simulators. For example, most surveyed simulators had abstract network
implementation and low-level details were missing. Further, non of the studied tools
provided an addressing scheme, a congestion control mechanism, or a trafc pattern
recognition mechanism. Out of those 15 presented simulation tools, seven were dened as
extensions of the CloudSim toolkit (Calheiros et al., 2011).
Yousefpour et al. (2019) presented a complete survey about FC, referencing 450
publications specically concerned with FC development and applications. Within their
extended survey, some FC simulation tools, such as iFogSim (Gupta et al., 2017;Naas et al.,
2018), Emufog (Mayer et al., 2017), Fogbed (Coutinho et al., 2018), and MyiFogSim
(Lopes et al., 2017) were discussed. As iFogSim was conceptually built using the CloudSim
communications model, it inherited some of its properties, such as the ability to co-execute
multiple tasks at the same time and the availability of plugable resource management
Generally speaking, any cloud simulation tool can be extended to be a fog-enabled
simulation tool. This is because of the fundamental property of the fog layer acting as a
bridge between end-users and the cloud. In other words, adding a fog module to a
cloud simulation tool, describing communications, roles, services, and parameters of
fog nodes, is sufcient to claim that the tool is a fog-enhanced cloud simulation tool.
Additionally, in a project that targets a Fog-BC integration applications, many researchers
used a reliable, general-purpose fog simulator and implemented the BC as if it was an
application case, such as in Kumar et al. (2020). The results of such simulation approach
can be trusted valid for limited cases, such as providing a proof of concept of the proposal.
However, critical issues, such as scalability and heterogeneity in huge networks, need
to be simulated in a more specialized simulation environments. To mention one critical
case, the BC protocols deployed in different CAs require more precise and accurate
deployment of the BC entities and inter-operation in different layers of a Fog-enhanced
IoT-Cloud paradigm. Consequently, as some simulation scenarios need an event-driven
implementation, while others need a data-driven implementation, a scenarios outputs
may differ when simulated using different simulation environments. Such possibility of
uctuated simulation outputs should normally lead to unreliable simulation results.
BC simulation tools
As we have previously investigated how a Fog-Blockchain integration is envisioned,
we started the implementation of FoBSim with a simple BC simulation tool described in
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 5/40
Baniata (2020). Consequently, we discuss the state of the art regarding BC simulation tools
available in the literature. In later sections, we describe how FoBSim serves as a reliable tool
to mimic an FC-BC integration scenario.
Anilkumar et al. (2019) have compared different available simulation platforms
specically mimicking the Ethereum BC, namely Remix Ethereum (Ethereum, 2020),
Trufe Suite (Trufe Blockchain Group, 2020), Mist (Bahga & Madisetti, 2017), and Geth
(Bruno, 2018). The comparison includes some guidelines and properties such as the
initialization and the ease of deployment. The authors concluded that Trufe Suite is ideal
for testing and development, Remix is ideal for compilation and error detection and
correction, while Mist and Geth are relatively easy to deploy. Alharby & Van Moorsel
(2019) and Faria & Correia (2019) proposed a somewhat limited simulation tool,
namely BlockSim, implemented in Python, which specically deploys the PoW algorithm
to mimic the Bitcoin and Ethereum systems. Similarly, Wang et al. (2018) proposed a
simulation model to evaluate what is named Quality of Blockchain (QoB). The proposed
model targets only the PoW-based systems aiming to evaluate the effect on changing
different parameters of the simulated scenarios on the QoB. For example, average block
size, number of TXs per block/day, the size of the memPool, etc. affecting the latency
measurements. Furthermore, the authors identied ve main characteristics that must be
available in any BC simulation tool, namely the ability to scale through time, broadcast and
multi-cast messages through the network, be Event-Driven, so that miners can act on
received messages while working on other BC-related tasks, process messages in parallel,
and handle concurrency issues.
Gervais et al. (2016) analyzed some of the probable attacks and vulnerabilities of
PoW-based BCs through emulating the conditions in such systems. Sub-consequently,
they categorized the parameters affecting the emulation into consensus-related,
such as block distribution time, mining power, and the distribution of the miners, and
network-related parameters, such as the block size distribution, the number of reachable
network nodes, and the distribution of those nodes. They basically presented a quantitative
framework to objectively compare PoW-based BCs rather than providing a general-
purpose simulation tool.
Memon et al. (2018) simulated the mining process in PoW-based BCs using the
Queuing Theory, aiming to provide statistics on those, and similar systems. Zhao, Guo &
Chan (2020) simulated a BC system for specically validating their proposed Proof-of-
Generation (PoG) algorithm. Hence, the implementation objective was comparing the
PoG with other CAs such as PoW and PoS. Another limited BC implementation was
proposed by Piriou & Dumas (2018), where only the blocks appending and broadcasting
aspects are considered. The tool was implemented using Python, and it aimed at
performing Monte Carlo simulations to obtain probabilistic results on consistency and the
ability to discard double-spending attacks of BC protocols. In Deshpande, Nasirifard &
Jacobsen (2018), the eVIBES simulation was presented, which is a congurable simulation
framework for gaining empirical insights into the dynamic properties of PoW-based
Ethereum BCs. However, the PoW computations are excluded in eVIBES, and the last
updates on the code were committed in 2018.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 6/40
To highlight the comparison between the mentioned BC simulation tools and our
proposed FoBSim tool, we gathered the differences in Table 2. PL, PoW, PoS, PoA, SC,
DM, PM, IDM, and F are abbreviations for Programming Language, Proof-of-Work,
Proof-of-Stake, Proof-of-Authority, Smart Contracts, Data Management, Payment
Management, Identity Management, and Fog-enhanced, respectively. As shown in the
table, none of the previously proposed BC simulation tools makes the PoA algorithm
available for simulation scenarios, provides a suitable simulation environment for identity
management applications, or, most importantly, facilitates the integration of FC in a BC
Many other references can be found in the literature, in which a part of a BC system, or
a specic mechanism is implemented. The simulated partis only used to analyze a
specic property in strict conditions, or to validate a proposed technique or mechanism
under named and biased circumstances, such as in Wang et al. (2020) and Raman et al.
(2019). It is also worth mentioning here that some open-source BC projects are available
and can be used to simulate BC scenarios. For example, the HyperLedger (The Linux
Foundation, 2020) projects administered by the Linux Foundation are highly sophisticated
and well-implemented BC systems. One can locally clone any project that suits the
application needs and construct a local network. However, those projects are not targeting
the simulation purposes as much as providing realized BC services for the industrial
projects. Additionally, most of these projects, such as Indy, are hard to re-congure and,
if re-congured, very sensitive to small changes in their code. Indy, for example, uses
specically a modied version of PBFT CA, namely Plenum, while Fabric uses RAFT.
The FC layer can be studied in three levels, namely the node level, the system level, and the
service level (Farhadi et al., 2020). The fog consists of several nodes connected to each
other and to the cloud. The main purpose of the fog layer is to provide cloud services, when
possible, closer to end-users. Further, the fog layer, conceptually, provides enhanced
security and latency measures. Hence, an FC system uses its components in the fog layer to
provide the services that end-users request from the cloud.
In a simple scenario, the fog receives a service request from end-users, performs the
required tasks in the most efcient method available, and sends the results back to
Table 2 Blockchain simulation tools and their properties.
Alharby & Van Moorsel (2019) and
Faria & Correia (2019)
Python χχ χχχ
Wang et al. (2018) Python χχ χχχχ
Memon et al. (2018) Java χχ χχχ χ
Zhao, Guo & Chan (2020) Python ✓✓χχχχ χ
Piriou & Dumas (2018) Python χχχχχχχ
Deshpande, Nasirifard & Jacobsen (2018) Java χχ χχχ
FoBSim Python ✓✓✓✓
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 7/40
end-users. As the clouds mainly provide Infrastructure, Software, and Platform-as-a-
Service models, those three models can be used for computational tasks, storage tasks, or
communication tasks (Maes et al., 2018).
For a fog-enhanced cloud system, a general overview of the workow is presented in
Fig. 1. As presented in the gure, the service is requested from end-users and the fog layer
provides this service if possible, otherwise, the request is forwarded to the cloud where
complex and time consuming actions are performed. However, information of the
complexity of the system, and the decision making process in the fog layer, should not be
within the concern of end-users. That is, end-users require their tasks to be performed
within a privacy-aware context and the QoS measures implications that were agreed on.
In FoBSim, the fog layer can be congured according to the scenario that needs to
be simulated. For example, the number of fog nodes, the communications within the fog
layer and with other entities of the simulated system, and the services provided by the fog,
can all be modied.
BC is a DLT that consists of several elements which need to efciently interact with
each other, in order to achieve the goal of the system. A general view of BC systems
suggests some fundamental components that need to be present in any BC system.
A BC system implies end-users who request certain types of services from a BC network.
The BC network consists of multiple nodes, who do not trust each other, that perform
the requested services in a decentralized environment. Consequently, the service provided
by a BC network can only be valid if the BC network deployed a trusted method, i.e., CAs,
to validate the services provided by its untrusted entities.
In FoBSim, the BC network can provide two models of services; namely data storage,
and computations. Meanwhile, the communications within the BC network and with the
Figure 1 Workow of an automated fog-enhanced cloud system.
DOI: 10.7717/peerj-cs.431/g-1
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 8/40
fog layer are congurable. Data storage service model implies that pieces of data are saved
on the immutable distributed ledger. Such data may be of any type including data
records, IDs, digital payment registration, or reputation measures of end-users or fog
components. It can also be noted that some applications require assets to be transferred
between clients, such as cryptocurrency transfer applications or real estate ownership
applications. Other applications do not require transferring assets rather than saving data
on the chain only, such as voting applications and eHealth applications. However, the
mentioned second type of applications may also need, on some level, a digital payment
method be embedded. In such cases, SCs on other payment platforms can be implemented
and generated, such as Bitcoin or Ethereum.
Performing computations for end-users is the second service model that the BC in
FoBSim can be congured to provide. That is, computational tasks can be sent by end-
users/fog entities to the BC in the form of SC, which are small chunks of code, run by BC
nodes upon fulllment of algorithmically veriable conditions (Coladangelo & Sattath,
2020). After running the SCs, the results can be saved in a centralized or decentralized form
according to the pre-run conguration. Figure 2 presents how the services, classically
provided by a cloud/fog system, can be interpreted into the form of services that can
be provided by a BC system. We can notice in the gure that SCs can be considered
relevant to cloud computational services, while different types of data saved on the
decentralized BC can be considered a relevant option to the centralized storage model
provided by a cloud system.
Consensus algorithms
Several approaches were proposed as a solution for the aforementioned needs, among
which are the most famous PoW CA. PoW was deployed in 2009 in the rst BC system, i.
e., Bitcoin (Nakamoto, 2019), and is currently used in other robust BC systems; such as
Ethereum (Vujičic, Jagodić& Ranđić, 2018). Although PoW methods have proven strong
security and support to BC systems, they have some drawbacks, such as high energy
consumption and high latency, that encouraged the R&D communities to search for other
trusted methods.
Figure 2 Service models provided by cloud/fog systems, and their relevant service models provided
by BC systems. Full-size
DOI: 10.7717/peerj-cs.431/g-2
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 9/40
The PoS algorithm (King & Nadal, 2012) was proposed a couple of years later in order
to solve the high energy consumption problem implied by PoW. PoS is currently being
optimized to provide similar advantages as PoW. Ethereum, for example, is planning
to substitute PoW with PoS in the very near future. However, some drawbacks of PoS
need to be solved before its ofcial deployment, such as The Monopoly Problem
(Larimer, 2013), The Bribe Attack (Bentov, Gabizon & Mizrahi, 2016;Deirmentzoglou,
Papakyriakopoulos & Patsakis, 2019), and relatively low reliability (Zhang & Chan, 2020).
In PoW-based BCs, a BC node proves the validity of its generated block of data by
coupling a puzzle solution within the block. The puzzle solution is generally characterized
by hardship to be obtained while it can easily be validated once found. Generally, the
puzzle is a mathematical problem that requires high computational power to be obtained.
In PoS-based BCs, the BC node that is allowed to generate the next block is chosen
randomly by the system. To encourage the system to pick a specic BC node, staking
more digital coins in deposit shall increase the probability of being chosen. This provides
high trust measures as faulty generated blocks are not tolerated by the system, and the
staked coins of the malicious/faulty BC node would be burned as a penalty.
Other approaches were proposed that provide trust in BCs. Examples include the PoET
(Buntinx, 2017), and the PoA (Avasthi & Saxena, 2018). PoET-based BCs generate
randomly selected times for BC nodes. The one node whose randomly picked time elapses
rst, is the one who is granted the opportunity to generate the next block. PoA, on the
other hand, implies that only blocks signed by authorized members are validated and
conrmed by the BC network. Those authorized nodes must be known trusted participants
that can be tracked and penalized in case of faulty behavior. Both of these CAs share the
property of being suitable for private and permissioned BCs, while PoW and PoS are
known for being suitable for public and permissionless BCs.
FoBSim allows to choose the suitable CA according to the simulated scenario. While
there are many versions of each CA mentioned, we currently provide the simplest version
of each so that modications can be performed with no complexities. To obtain more
information about them, more details can be found at Sheikh (2018),Singh et al. (2019),
and Chen et al. (2017).
In a very simple scenario, an end-user sends a request to the BC network, which consists of
BC nodes, to perform a dened TX. As stated at the beginning of this section, TXs may be
data to be stored (i.e., payment data, reputation data, identity data, etc.), or can be SCs
whose results can be either saved in a centralized (in the case of Cloud) or distributed
manner (in the cases of fog or BC). Once the TX is performed, it should be agreed on by the
majority of BC nodes if to be saved on the distributed ledger and, sub-consequently, be
added to the chain saved in all BC nodes.
On the other hand, if the fog layer is controlling and automating the communications
between the end-user layer and the BC network, as in Baniata & Kertész (2020), the
TXs are sent from end-users to the fog. After that, some communication takes place
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 10/40
between the fog layer and the BC network in order to successfully perform the tasks
requested by end-users. In such system model, we assume that the BC network lays in a
different layer than the fog layer. The case where the BC network is placed in the fog layer
is covered in Functionality of the BC Deployment. Nevertheless, a feedback with the
appropriate result of each TX should be easily achievable by end-users.
Distributed ledger
In the case were data needs to be stored in a decentralized manner, no Trusted Third Party
(TTP) needs to be included in the storing process. The entity considered as a TTP in
regular fog-enhanced cloud systems is the cloud, where data is stored. However,
computations can take place in the fog layer to enhance the QoS.
Within DLT-enabled systems, such as BC, groups of data are accumulated in blocks,
and coupled with a proof of validity, as explained in Consensus Algorithms. Once a
new block of TXs is generated, and the proof is coupled with them, the new block is
broadcast among all BC nodes. Nodes who receive the new blocks verify the proof and
the data within each TX, and if everything is conrmed valid, the new block is added to
the local chain. With each BC node behaving this way, the new block is added to the
chain in a distributed manner. That is, a copy of the same chain, with the same exact
order of blocks, exists in each BC node. Further, a hash of the previous block is added to
the new block, so that any alteration attack of this block in the future will be impractical,
and hence almost impossible.
Functionality of the BC deployment
As a BC-assisted FC system can provide computational and storage services, the BC
placement within the FC architecture may differ. That is, BC can be placed in the fog layer,
the end-user layer, or the cloud layer. In FoBSim, however, we consider only the rst two
mentioned placement cases.
When the BC is deployed in the fog layer, storage and computational services are
performed by the fog nodes themselves. In other words, fog nodes wear a second hat,
which is a BC network hat. Thus, when storage to be provided by the fog while fog nodes
are also BC nodes, data is stored in all fog nodes in the fog layer. A simple system
model is demonstrated in Fig. 3A, where only one chain is constructed in the lower
fog layer and one fog control point in the upper layer monitors the BC functionality.
However, such a model is not practical and more complexities appear in a real-life
scenario, including heterogeneous fog nodes, multiple BC deployments, different CAs, and
different service models. In such complex systems, FoBSim can be easily extended by
adding the needed classes and modules and, hence, cover necessary proposed scenario
entities. A note is worth underlining here is the importance of differentiating between the
services provided by fog nodes which are BC nodes, and the services provided by fog nodes
which are not BC nodes. The rst type gets incentivized by end-users for providing both
fog services and BC services, while the second type gets incentivized by end-users for
providing only fog services. Such critical issues need to be taken care of, when simulating
Fog-BC scenarios, to maximize the reliability of the obtained results.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 11/40
In a system model where the BC is deployed in the end-user layer, we can distinguish
two types of end-users; namely task requester and BC node. In a fog-enhanced BC system,
the fog controls the communication between the two types of end-users. Specically,
BC nodes perform the tasks that were sent to the BC network by the fog, which originally
were requested by task requester end-users. Further, the fog can control the privacy
preservation of data and incentivize BC nodes in the form of digital currency, as in
Baniata, Anaqreh & Kertesz (2021). To be specic, BC nodes can be further sub-
categorized according to the scenario to be simulated. Adding other types of BC nodes is
up to the developers and the system model. For example, the Bitcoin system is modeled in
a simpler way, where BC is directly connected to task requester end-users, and it only
provides a payment ledger service. Ethereum, on the other hand, provides computational
and data management services. This makes Ethereum surpass Bitcoin because it can
provide more services to end-users. FoBSim improves both system models by optionally
adding the fog layer. The system model provided by FoBSim when the BC is deployed in
the end-user layer is demonstrated in Fig. 3B.
To cover all architectural elements described in FC Architectural Elementsand BC
Architectural Elements, we implemented FoBSim according to the conceptual workow
demonstrated in Fig. 4. The current version of FoBSim covers all the architectural elements
of a BC system and an FC system. This means that FoBSim successfully inlines with
the general architecture of a reliable BC simulation presented in Liaskos, Anand &
Alimohammadi (2020). In fact, many more services and scenarios can be simulated using
FoBSim, covering the fog layer inclusion besides the BC. As presented in Fig. 4, different
CAs can be used, different services of the BC network can be declared, and different
placement scenarios of the BC network can be chosen. When the BC network is located in
the fog layer, the number of BC nodes does not need to be input because, as described
earlier, each fog node is also a BC node. Nevertheless, the number of task requester end-
Figure 3 FC-BC integration system model, where (A) the BC is deployed in the fog layer, and (B) the BC is deployed in the end-user layer.
DOI: 10.7717/peerj-cs.431/g-3
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 12/40
users connected to each fog node needs to be input, while some fog nodes in a PoA-based
scenario might not be authorized to mint new blocks. Once the network is built, running
and testing the system model can take place.
The FoBSim environment is implemented using Python v3.8, with the inclusion of some
common packages such as: random,randrange,multiprocessing,time, and hashlib.
The current version of FoBSim can be cloned and directly run as all the variables, lists,
dictionaries, and sets have been given initial values. However, these parameters can be
modied before running the code in the Sim_parameters.json le. FoBSim tool is open-
source and freely available at Baniata & Kertesz (2020).
FoBSim modules
To facilitate the understanding of FoBSim, we demonstrate the methods within each
FoBSim module in Fig. 5. Further, we show the classes and methods of FoBSim modules
Figure 4 Workow of a simulation run using the FoBSim environment.
DOI: 10.7717/peerj-cs.431/g-4
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 13/40
in tables of the Supplemental Material for this paper. Some notes to be taken care of need
to be underlined as well:
1. There is a big opportunity for developers to implement new methods in the fog
layer. For example, the fog nodes can be extensible to provide privacy-preserving
mechanisms (such as described in Baniata, Almobaideen & Kertesz (2020)),
computational services (such as described in Fröhlich, Gelenbe & Nowak (2020)), or
reputation and trust management services (such as described in Debe et al. (2019)).
Figure 5 The interaction among modules and methods of the FoBSim environment.
DOI: 10.7717/peerj-cs.431/g-5
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 14/40
2. In this module, the mempool, where TXs are accumulated, is a Python
multiprocessing queue that allows different processes to synchronously add() and get()
3. There are other minor methods from other modules also called by FoBSim entities that
mint a new Block, or receive a new TX/Block, in order to synchronously and smoothly
apply each different CAs policies, as declared in its simple version.
4. After each simulation run, some temporary les can be found in the temporary folder of
FoBSim. These les are originally initiated by the main module, the BC module, or
the miner module. The temporary les are used synchronously by different FoBSim
entities, mimicking the real-world interaction between BC entities. The current version
of FoBSim generates some or all of the following les depending on the simulated
Minerslocal chains.
Minerslocal records of userswallets.
Log of blocks conrmed by the majority of miners.
Log of nal amounts in minerswallets (initial values staked values + awards).
Log of coin amounts which were staked by miners.
The longest conrmed chain.
Forking log
Genesis block generation
The rst block added to the chain in each simulation run is the most important block
of the chain. Different scenarios imply different formats of this block, and different
methods to broadcast it among, and be accepted by, miner nodes. In the current version of
FoBSim, however, a genesis block is initiated with a list of TXs containing only the
string genesis_blockand the labels of the miners available when this block was generated.
The block number is 0, the nonce is 0, the generator_id is The Network, the previous
hash is 0, and the hash is generated using the hashing_function in the
module. The timestamp of the genesis block indicates when the chain was launched, hence
all blocks shall have bigger timestamp values than the genesis timestamp. Figure 1 of the
Supplemental Material of this paper shows a standard FoBSim genesis block, generated in
a BC network that consists of two miner nodes.
FoBSim consensus algorithms
Currently, there are three available CAs ready to be used in different simulation scenarios.
Next, we describe each one individually as to facilitate any modications by developers.
However, we need to indicate that the three included CAs are in their simplest versions
and may require some individual modication in case of the need of more sophisticated
ones. Before delving into the CAs, however, we need to discuss the Gossip protocol in
FoBSim, as it is deployed regardless of what CA is chosen.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 15/40
Gossip protocol
A Gossip Protocol (Blywis et al., 2011) is usually deployed in P2P systems for maintaining
the consistency of distributed data saved in decentralized networks. Specically in BC
systems, miner nodes regularly, yet randomly, gossip to their neighbors about their current
version of the chain, aiming to reach consensus nality as soon as possible. According to
specic characteristics of the BC, the locally saved chains are updated so that all conrmed
chains are equivalent at any given moment (He, Cui & Jiang, 2019). The equivalency that
any BC system is seeking is dened by the contents similarity of the chains (i.e., TXs,
hashes, etc.), and the order similarity of the conrmed blocks. That is, a chain [b1,b2,b3]is
not equivalent to [b1,b3,b2] despite the fact that both have similar contents.
Gossiping protocols are usually fault tolerant as many failing nodes do not affect
the protocol. Furthermore, they can adapt to the dynamics of the network, so some
solutions have been proposed in the literature for nodes joining and leaving the network.
However, gossiping is an iterative method that never quits as long as the network is up,
and it may take time to converge. Additionally, a high level of communication costs is
expected for gossiping, while randomly chosen neighbors are informed about updates.
Thus, one cannot provide precise analysis about the time needed for the network
agreement on a piece of data.
Although the implementation of such protocol is relatively simple, it is differently
implemented in different systems. Some famous examples of efcient gossiping protocols
include the Push-Sum protocol (Kempe, Dobra & Gehrke, 2003), the Push-Flow algorithm
(Gansterer et al., 2013), and different versions of the Push-Pull averaging protocol
(Gabor & Jelasity, 2018). Furthermore, we found that its application in FoBSim is useful,
when the PoW CA is used in a multiprocessing scenario, with a relatively low puzzle
difculty. Additionally, it can be easily noted that the number of simulated TXs/blocks and
the initial TX per block conguration affect the speed of the system to reach consensus
nality. That is, for low numbers of TXs, blocks, and low ratios of TXs per block, miners
might not have the required time to converge locally saved chains. Accordingly, nal
versions of local chains in some FoBSim simulations, under such circumstances, may
not coincide, which is normal and expected as described in Fan et al. (2020). Nevertheless,
we deployed a simple Push-Pull Gossip version in FoBSim that works perfectly ne,
so that modications can be easily conducted if needed. In the current version of FoBSim,
a Time To Live (TTL) parameter was not added to the Pull requests when gossiping.
This, as expected, oods the network with Pull and Push requests each time a node wants
to gossip. Nevertheless, we faced no problem whatsoever when the network consisted of up
to 1,500 miners. If more miners need to be deployed in the simulation scenario, where
gossiping is activated, we recommend either conguring the gossiping requests to have a
TTL (i.e., a number of hops the requests perform before they are terminated), and/or
decreasing the number of neighbors the gossiping node is sending the gossip request to.
That is, instead of gossiping with all neighbors, a miner can randomly choose a neighbor to
gossip with. Consequently, each neighbor will gossip with a randomly chosen neighbor
of his, etc. More details on such implementation approach can be found in Lan et al.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 16/40
(2003), while detailed analysis regarding the success rate of gossiping, with a given TTL in a
given P2P network, can be found in Bisnik & Abouzeid (2007).
Algorithm 1 describes how the Pull-request in the default Gossip protocol of the current
version of FoBSim works. If the gossiping property was set to true, Each miner runs
this algorithm each time the Gossip() function is called for that miner (as a default, the
Gossip function is called each time a miner is triggered to build a new block and when
a new block is received). As demonstrated in the algorithm, a default FoBSim miner
requests information about the longest chain, and adopts it if its contents were agreed
on by the majority of the network, which is a condition tested using Algorithm 2.
Additionally, if a miner receives a new valid block, and the resulting local chain was longer
than the global chain, the miner updates the global chain instantly, which represent the
Push request of the Gossip protocol in FoBSim.
In big BC networks, the mentioned issues need to be carefully designed, so that the
consistency of the distributed ledger by the end of the simulation run is guaranteed, while
the efciency of the algorithm is optimized.
The proof of work
In a simplied scenario of a PoW-based BC, miners collect TXs from the mempool
(which is a shared queue in FoBSim) and accumulate them in blocks that they mint.
Specically, all available miners compete to produce the next block that will be added to the
chain. The fastest miner producing the next block is the miner whose block is accepted by
all other miners of the BC. Synchronously, all blocks that are being minted by other
miners are withdrawn, and all TXs within are sent back to the mempool. To mimic this
Algorithm 1 The default Gossip protocol in FoBSim.
Result: Conrmed Local_chain in μ
initialization: Self(miner μ
conrmed_chain = self.local_chain;
temporary_global_chain = longest_chain;
Condition_1 = len(temporary_global_chain) > len(conrmed_chain);
Condition_2 =blocks in temporary_global_chain are conrmed by network majority;
if Condition_1 AND Condition_2 then
conrmed_chain = temporary_global_chain;
self.local_chain = conrmed_chain;
self.top_block = conrmed_chain[str(len(conrmed_chain)-1)];
if BC_function is Payment then
self.log_users_wallets = conrmed_chain_from.log_users_wallets
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 17/40
scenario in FoBSim, we needed to deploy the multiprocessing package of Python and
trigger all miners to work together on the next block.
Each miner then works within an isolated core of the device on which the simulation is
conducted. Using this approach is doable and explainable in simple scenarios, where each
process needs to access one or few shared objects. However, we found it challenging to
mimic complex scenarios, where huge number of processes require accessing the same
shared lists. For example, when the BC functionality is payment, the BC is deployed in the
fog layer, and the CA is PoS, the wallets of end-users, fog nodes, and mining nodes need to
be all global for read and update by all processes. We also experimented the Python
package: multiprocessing.shared_memory, which partially solved the problem as multi
processes can read and update values in a Shareable List object. However, as declared in the
ofcial Python documentation (The Python Software Foundation, 2020), the Shareable List
object lacks the dynamicity required in terms of length and slicing. According to the
mentioned insights, we implemented two approaches for PoW mining in FoBSim, the rst
starts all miners in parallel (using the multiprocessing package), while the second
consequentially calls for miners to mint new blocks (using a FOR loop). Both approaches
are available in the miners_trigger() function in the module, and developers
are free to use either. We do encourage developers, however, to be cautious and
carefully test their results when using the parallel processing approach, as each different
scenario may require different access management scheme to different FoBSim entities.
Hence, a complex scenario simulation may require some modications to some variables
and lists so that they become shareable by all processes in different modules. Detailed
instructions for implementing different memory-sharing scenarios can be found in the
Python ofcial documentation (The Python Software Foundation, 2020).
When a miner receives a new block, it checks whether the hash of the block (in which
the nonce or the puzzle solution is included) is in line with the acceptance condition
Algorithm 2 The default chain conrmation function in FoBSim.
Result: bool chain_is_conrmed
Passed parameters: Chain C, network size;
initialization: chain_is_conrmed = True;
block_conrmation_log = blockchain.conrmation_log;
Condition_1 = not (C[block][hash] in block_conrmation_log);
Condition_2 = block_conrmation_log[chain[block][hash]][votes] <= (network size / 2);
for block in Cdo
if Condition_1 OR Condition_2 then
chain_is_conrmed = False;
return chain_is_conrmed
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 18/40
enforced by the module. Further, the receiver miner checks whether
sender end-users have sufcient amounts of digital coins to perform the TXs (in the case
of payment functionality). Unlike PoS and PoA, all miners work at the same time for
achieving the next block. Hence, any miner is authorized to produce a block and there is no
miner verication required. Algorithm 3 presents how PoW is implemented in FoBSim.
The proof of stake
In a simplied version of PoS, miners stake different amounts of digital coins (which they
temporarily are not allowed to claim) in the BC network. The network then randomly
chooses a miner to mint the next block, with higher probability to be chosen for miners
who stake more coins. Once a miner is chosen, it is the only one authorized to mint
Algorithm 3 The default PoW mining algorithm in FoBSim miner.
Result: New block βconrmation
initialization Self(miner μg);
Collect TXs from memPool;
if BC_function is Payment then
validate collected TXs
if BC_function is Computational Services then
add the evaluation results to TXs
Accumulate TXs in a new BC block β;
Find the puzzle solution of β(nonce);
Broadcast βto neighbors;
if New block βis received then
if βnonce is correct then
if BC_function is Payment then
validate and conrm TXs in β
add block βto the local chain;
Broadcast βto neighbors;
report a successful block addition [β,μ
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 19/40
and broadcast the next block. In case of faulty TXs/blocks, the minter loses its staked coins
as a penalty, while in case of correct blocks, the minter is awarded some digital coins.
To mimic this in FoBSim, each miner is initiated with a specic amount of coins in its
wallet. After that, a randomly generated number of coins (up to the amount of coins in its
wallet) is staked by each miner. This way, every miner has a different probability to be
chosen by the network. Next, the network randomly chooses, say 10% of the available,
miners and picks the one with the highest stake. This chosen miners address is
immediately broadcast to all miners so that any block received from any other miner is
rejected. Once the new block is received, it is validated and added to the local chain.
Algorithm 4 presents how PoS is implemented in FoBSim.
Here, a very wide space is available for implementing reputation management schemes
in FoBSim. Different scenarios and different applications require different parameters
affecting entitiesreputation. Further, adding other types of miners, end-users, or even fogs
implies that different DBs can be suggested.
It is also worth mentioning here that we found it unnecessary to use the multiprocessing
package because only one miner is working on the next block. Hence, no competition is
implied in the PoS scenario.
The proof of authority
In a simplied version of the PoA algorithm. only authorized network entities (by the
network administrators) are illegible to mint new blocks. Regardless of the BC
functionality, there is also no need to deploy the multiprocessing package for PoA-based
scenarios as there is no competition as in PoS-based scenarios.
To mimic the PoA in FoBSim, we allow the user to declare which entities are authorized
to mint new blocks. The declaration requested from the user appears in the case of BC
deployment in the fog or end-user layer. That is, each fog node is administering a group
of end-users, and providing communications (and probably computations) services to
them. However, it is not necessary for each fog node in the fog layer to be a BC node
as well, but it should be there as only a fog node. Authorized fog nodes then are wearing
both hats, fog nodes and BC miners. When the BC is deployed in the end-user layer,
authorized miners are responsible for minting new blocks and maintaining the distributed
ledger. Meanwhile, unauthorized miners are only responsible for validating new blocks,
received from their neighbors, and for maintaining the distributed ledger.
This approach allows for comfortably emulating a scenario where the BC in the fog
layer and part of the fogs are included in the BC functionality. Notice that a fog node that is
also a BC node performs all the required tasks in logical isolation. This means that a
fog node that is administering a group of end-users has a buffer to save the end-users TXs,
but it does not use these TXs to mint a new block. Rather, it sends these TXs to the
mempool as required, and then, only if it was authorized, it collects TXs from the
mempool. Notice also, that the mempool is a simple queue in FoBSim, yet it can be
implemented for some scenarios to be a priority queue. Our implementation of isolating
the services provided by a fog node that is also a BC miner facilitates the simulation of
scenarios where TXs need to be processed according to their priority. For example, miner
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 20/40
Algorithm 4 The default PoS mining algorithm in FoBSim.
Result: Conrmed new block β
initialization miners μ
, miners.wallets, stake random no. of coins from each miner.;
The Network:;
while mempool.qsize() > 0 do
Randomly choose a predened no. of miners;
Choose the miner with the highest Stake value;
Inform all miners of the ID of the next block generator μ
The Miner:;
if a new ID μg is received from the Network then
if MyAddress == μ
Collect TXs from memPool;
if BC_function is Payment then
validate collected TXs
if BC_function is Computational Services then
add the evaluation results to TXs
Accumulate TXs in a new BC block β;
Broadcast β;
wait for a new block from μ
if b is received then
if μ
== β.generator then
if BC function is Payment then
validate and conrm TXs in β
add block βto the local chain;
broadcast βto neighbors;
report a successful block addition [β,μ
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 21/40
nodes in Ethereum usually choose the SCs with the highest Gas/award provided by
end-users. This is a type of prioritizing that can be simulated in FoBSim. Similarly, in
Bitcoin, a priority value is computed for each TX according to Eq. (1), and TXs with higher
fees and higher priority values are processed faster (Narayanan et al., 2016). The default
PoA algorithm implemented in FoBSim is claried in Algorithm 5.
Priority ¼PinputAge inputValue
TXsize (1)
Transaction/block validation in FoBSim
Here, we need to underline some differences between the terms verication, validation,
and conrmation, and we need to see how FoBSim differentiates between those terms
in different scenarios. As we have touched on these differences in Baniata & Kertész
(2020), we need to accurately dene each of these terms in order to correctly describe how
FoBSim works.
Validation is the process when a miner (either a minter or receiver) checks the
correctness of a claim. That is, in the case of a minter miner, the puzzle solution (or nonce)
provided with the minted block needs to be correct before the block is broadcast. If the
nonce was valid, the block is broadcast, otherwise, a new solution is searched for.
While in the case of a receiver miner, the nonce is checked once. If in this later case the
solution was valid, the block is accepted, otherwise, the block is rejected.
In the case of payment functionality, the validity of TXs fetched from the mempool is
tested. This means that the amount of coins in the wallet of the sender of each TX, in
the payment functionality, is compared to the amount to be transferred. If the wallet
contains less than the transferred amount, the TX is withdrawn from the block. Later when
the new block is received by a miner, the same hash validation and TXs validation take
place, except if one of the TXs were invalid, the whole block is rejected. In the case of a
block rejection, the minter miner is usually reported in a reputation-aware context.
If all the contents of a newly received block are valid (i.e., the hash, the TXs, the wallets, the
block number, and the nonce) the block is added to the locally saved chain. Here, we can
say that TXs are conrmed, because the block is added to the chain (i.e. the block is
The verication, on the other hand, is the process of verifying the identity of an
entity. For example, in the case of PoA, only authorized miners are allowed to mint new
blocks. Similarly, in the case of PoS, a received block should be generated by a miner that
all other miners expect to receive the new block from. Additionally, public information
about end-userswallets need to be accessible by miners to validate their TXs. Thus, a
received block, with some TXs generated by end-users who do not have wallets, or
whose wallets contents are not readable by miners, can not be validated and conrmed.
Failing to conrm a TX is not necessarily caused by end-users not having sufcient coins to
transfer, but may also happen for end-users who can not be veried.
All of these critical principles are, by default, taken care of in FoBSim. All miners
are informed about the end-users public identities and walletscontents. After that,
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 22/40
transferred coins are updated locally in each miner. Consequently, a new TX from the
same end-user will be compared to the updated amount of coins in its wallet. Invalid TXs
are not included in the block being minted, while invalid TXs cause the rejection of the
Algorithm 5 The default PoA mining algorithm in FoBSim.
Result: Conrmed new block β
initialization Fog nodes Ψ
if BC placement is Fog Layer then
User Input(address of authorized fog nodes)
Input(address of authorized miners)
save authorized miners μ
in Miners_List;
The Miner:;
while mempool.qsize() >0do
if self.address μ
collect TXs from memPool;
if BC function is Payment then
validate collected TXs
if BC function is Computational Services then
add the evaluation results to TXs
accumulate TXs in a new BC block β;
broadcast βto neighbors;
if βis received then
if μ
if BC function is Payment then
validate and conrm TXs in β
add block βto the local chain;
broadcast βto neighbors;
report a successful block addition [β,μ
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 23/40
whole received block. Once a blocks contents are validated, and the TXs/block generators
are veried, the TXs are conrmed, the locally saved wallets amounts are updated, and
the block is locally conrmed and added to the chain. The most interesting thing is that the
very small probability of a double spend attack (Karame, Androulaki & Capkun, 2012)
which can appear in PoW-based scenarios, can be easily simulated in FoBSim. All
processes are actually happening during each simulation run, rather than substituting
them with a small delay as in most BC simulation tools we checked. Hence, validation,
verication, and conrmation processes can be modied according to the scenario to
be simulated. Nevertheless, Bitcoin decreases the double spend attack probability by
regularly raising the difculty of the puzzle, which is a property that can be modied in
FoBSim as well. To facilitate the simulation of such critical scenarios, we deployed two
broadcasting approaches for newly minted blocks. The rst allows the broadcast process
using a simple FOR loop, where miners sequentially validate and conrm new blocks.
The second allows the broadcast process using the multiprocessing package, which
allows all miners to receive and process new blocks at the same time. Relatively, developers
need to be cautious when using the second approach, because of some critical challenges
similar to those mentioned in The Proof of Work.
Awarding winning miners
Generally speaking, BC miners get rewarded by two system entities for providing the BC
service (i.e., BC functionality). The rst is the end-user who generated the TX, who pays a
small fee once the TX is conrmed (e.g., Gas in Ethereum). The second is the BC network
itself (i.e., all miner nodes), which updates the winning miners wallet once a new block
(minted by the winning miner) is conrmed. We can notice here how important it is to
clarify the difference between validation, verication, and conrmation. That is, a miner is
veried by its public label and public wallet key/address (ID). Then, a miner being
authorized to mint a new block is validated (claim). Finally, a miner is awarded for minting
a conformable block (miners wallet is updated).
In FoBSim, we implemented the second mechanism, where miners get rewarded for
their services by the network. We assume this part is hard because it, also, needs to be
agreed on by the majority of BC miners (i.e., at least 51%), and it requires the condition
that they conrm the block. The default implementation of FoBSim does that. For the rst
incentivization mechanism, we thought that it is not applicable in many different
scenarios, hence we left it for the developers to add it if needed. For example, to allow
end-users to provide fees for getting tasks in the BC, one eld can be added to generated
TXs, containing the amount of fees the end-user is willing to pay for the service.
Once a miner picks a TX (mostly, TXs with higher fees are faster to be picked and
processed by miners) and the block containing the TX is conrmed, all miners add the
TX fees to the winning miners wallet. Figure 2A of the Supplemental Material presents
a screenshot of FoBSim output, concluding that a new block was received from Miner_2 by
Miner_3, and that the BC module just obtained the needed conrmations to consider
the new block conrmed by the whole BC network. Thus, the minter is awarded. Later, the
receiver miner presents its updated local chain according to the successful network
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 24/40
conrmation. On the other hand, Figure 2B of the Supplemental Material presents a
screenshot of the miner_wallets_log after a simulation run, where the PoA CA was used
and all miners, except for Miner_5, were authorized to mint new blocks (initial wallet value
was 1,000).
Strategies in FoBSim
As had been discussed so far, there are some default strategies used by FoBSim entities
throughout each simulation run. To mention some, TXs are picked by miners with no
preference, e.g., the highest Gas or priority. Also, a default chain is a single linear chain and
new blocks are added to the top of this chain. Some applications, however, have multiple
chains or multi-dimentional chains, e.g., Directed Acyclic Graph (DAG) based chains.
Additionally, if two blocks appear in the network, the block that was accepted by the
majority of miners is conrmed rather than, in some BC systems, the older one is
conrmed even if it was conrmed by the minority. Further, a valid block is immediately
announced, once found, into the FoBSim network, while in some applications, there might
be a conditional delay. For instance, if a selsh mining attack scenario is to be simulated,
miners would prefer to keep their newly found blocks secret, hoping they will nd the next
block as well (Negy, Rizun & Sirer, 2020).
The current version of FoBSim supposes that the data ows from end-users to the
fog, and from the fog to the BC network. However, there are other possible data ow
schemes that can be simulated, as depicted in Fig. 6. For example, the BC in the current
version provides DLT services to end-users, which are communicating with the BC
through the fog layer, while services might be provided by the fog layer to the BC network
or from the BC network to the fogs in some applications. Further, an application
where end-users may need to request data directly from the BC might be possible,
which implies different data ow scheme as well. FoBSim facilitates the modication of the
data ow in the simulated application, and presents an extra Cloud module that can add
more possibilities to the application.
Network connectivity characteristics are a major and critical concern in any BC system.
To facilitate network architects job, FoBSim allows to dene the number of nodes in
each layer, the number of neighbors of each BC node, and the general topology of the
network. Additionally, all BC nodes are connected into one giant component by default,
whether they were deployed in the fog layer or end-user layer. Accordingly, the effect of
manipulating the topology of simulated networks can be easily captured.
FoBSim constraints
Some properties have not been implemented in the current version of FoBSim, such as
MT, Digital Signatures and Mining Pools. Additionally, FoBSim source code can be run on
a PC with Microsoft Windows or Linux OS, but it may need some modications if to be
run on a PC with a MAC OS (some functions require access to OS operations such as
deleting or modifying les located at the secondary memory). Finally, the default limit of
recursion in Python may restrict the number of miners to 1,500, which may raise some
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 25/40
error regarding the maximum allowed memory use by the interpreter. To solve this, one
can modify the maximum limit using the sys.setrecursionlimit in the main function.
Merkle trees
An MT, or a Hash Tree, is a data structure which is mostly a binary tree, whose leaves are
chunks of data. Sub-consequently, each leaf is double hashed with its siblings to produce
their new parent, which represents its two children. Hashes are recursively hashed
together, in a binary manner, until obtaining one root that represents the whole tree. MTs
are used in BCs such as Bitcoin to decrease the probability of security attacks, along with
other security measures, to reach a level where it is (a) easy for light-weight nodes to
validate new TXs and (b) computationally impractical to attack/alter a BC. That is, each
TX in any given block is hashed with the next, and so on, so that one root hash of all
TXs is saved in the block header. Using this root hash, and other components of the block,
the hash of the block is generated. This means that not only a conrmed block is
impossible to alter, but also a conrmed TX within a conrmed block.
However, not all BC systems deploy an MT approach due to some probable
conicts with system requirements or objectives. Thus, we decided to leave this to be
implemented by developers according to the systems that need to be simulated, and we
decided that the default conguration of BC nodes in the current version of FoBSim is to
make all miners full node miners. That is, every miner locally stores a complete copy of the
chain so that any TX can be validated according to TXs recorded locally. Additionally,
there are different deployment models of MT approaches in different BC systems. That is,
some BCs may deploy MTs for hashing other chunks of data/tokens instead of TXs.
To implement an MT approach in FoBSim, one can add a function that performs a
loop through all TXs in a newly minted block, up to the last TX. After that, the root of the
MT is added to the block before it is broadcast to the BC and the hash of the block is
computed accordingly. Miners who receive a new block shall, accordingly, validate the
added root. Hence, a validation step, to test the correctness of the MT root compared with
TXs within the new block, needs to be added to the validation function in the miner
Figure 6 Possible data ow schemes in an integrated Fog-BC system.
DOI: 10.7717/peerj-cs.431/g-6
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 26/40
module of FoBSim. To make use of such added property, one can dene a light-weight
miner type which saves only the header of a newly conrmed block instead of the whole
block. Accordingly, such type of miners validate new TXs according to this light chain of
headers, hence consume less time, energy, and storage to maintain the work of the BC
Digital signatures
As our main aim is to generally simulate TX generation, validation, and conrmation, in
different BC-based, inter-operation, and consensus scenarios, we did not target security
issues. This is because such issues are determined individually for each case to be
simulated, leading to different mining economics. The security techniques and approaches
in BC-based fog and IoT systems had been discussed in many previous works, such as
Sodhro et al. (2020). Specically, digitally signed coins/tokens are primarily used in real-
world applications of cryptocurrencies in order to prevent security attacks, such as the
double spend attack. Different BC-based cryptocurrency systems use different mechanisms
and protocols regarding signing and minting new coins, hence, different scenarios require
the implementation of the reference coins and digital signing techniques to be simulated.
Examples might include a research work that aims at comparing different signing
protocols in different CAs. This being said, FoBSim does not target a specic
cryptocurrency system, such as Bitcoin, yet it provides the generalized environment used
in such systems, where problems and solutions can be implemented and emulated by
What the default version of FoBSim provides, however, is a simplied protocol of coin
transfer between users. That is, each miner holds a locally saved record of user wallets,
which is used in TX validation in case of payment BC functionality. We found that this
approach can output similar results to those output by systems with signed coins,
except that this approach allows a double spend attack in case of malicious end-users. If a
scenario to be simulated, where there are some faulty/malicious entities among system
users (which is not implemented in the default version of FoBSim), then digitally
signed coins need to be implemented as well. Additionally, miner nodes in FoBSim are
assumed to be trusted to send reports of conrmed blocks. Thus, reports sent by miner
nodes to the network aiming to participate in voting regarding winning miners are
assumed always legitimate. To sum up, FoBSim miners can track who, paid whom,
how much, and they are trusted to participate in voting without a cryptographic proof.
While, in other implementation approaches, FoBSim miners may track who has
transferred, what units, of which stocks (i.e. digitally signed coins/tokens), to whom, and
their votes regarding winning miners must be veried by network entities (i.e., by also
adding the new block to their local chains, and following this addition with other new
blocks, each newly added block can be considered, in a sense, a conrmation). Similarly,
end-users who generate new TXs do not need to sign their generated TXs as they are
assumed trusted (i.e. the default implementation of FoBSim does not include malicious
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 27/40
Mining pools
Pool mining is the collaboration between miners to form mining pools and to distribute
the earned rewards in accordance with pool policies to earn a steady income per miner
(Majeed, Kim & Hong, 2020). Examples of such mining pools include, F2Pool,
and Slush Pool. Mining pools provide the advantages of making mining prots more
predictable to miners and allowing small miners to participate. However, the existence
of pool mining increases the probability of system centralization and discourages full
nodes. The necessity of adding a mining pool extension to FoBSim is dependant on the
scenario to be simulated. As the general idea of mining pools is to allow miners to perform
mining under the umbrella of a named group, if one of the group miners nds a block,
the award is divided among all group members according to the computational power each
member provides. A mining pool is managed by a pool manager, whose protocol is dened
according to the business model of the pool.
In the current version of FoBSim, all miners are full node miners. That is, each miner
attempts to solve the puzzle using its own resources, to validate newly generated TXs
and to accumulate them into new blocks. When a block is received by a full node, it is
validated and conrmed locally (all miners save the whole BC for validation, verication,
and conrmation). Consequently, any prots and awards, obtained because of the full
miner work, are directly added to the miners wallet. On the contrary, a miner receives an
award that is proportional to the computational power it provides, even if it was the one
who found the next block.
Following the validation and verication methods of simulation models presented in
Sargent (2013), we have so far discussed the technologies and the paradigms lying within
our proposed FoBSim environment. Further, we highlighted our proposal novelty
compared to other related works, discussed the event validity in FoBSim, and presented the
algorithms and modules lying within to facilitate a structured walk-through validation.
Next, we follow an operational validity approach by presenting case studies that we
simulated using FoBSim. The setup and behavior of FoBSim is discussed, and the results of
the simulation runs are presented afterwards.
Case 1: Comparing time consumption of PoW, PoS, and PoA
When we compare PoW, PoS and PoA in terms of average time consumed for block
conrmation, PoW is expected to present the highest time consumption. This is because
of the mathematical puzzle that each minter needs to solve in order to prove its illegibility
to mint the next block. In PoS, on the other hand, the network algorithm randomly
chooses the next minter, while it (slightly) prefers a miner with a higher amount of staked
coins. Once a minter is chosen, all miners are informed about the generator of the next
block and, thus, the minter needs to perform no tasks other than accumulating TXs in a
new standard block. Other miners then accept the new block if it was generated by the
minter they were informed about, hence the verication process takes nearly no time
(assuming that the transmission delay between miners is set to 0). In simple versions of
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 28/40
those two algorithms, all miners have the same source code, thus all miners may be
minters, veriers, and chain maintainers.
The PoA algorithm is the tricky one though. This is because all authorized miners mint
new blocks, verify newly minted blocks, and maintain the chain locally. Meanwhile, other
BC nodes verify new blocks and maintain the chain, but do not mint new blocks
(De Angelis et al., 2018). Consequently, every BC node has a list of authorized entities,
including the methods to verify their newly minted blocks. This implies that the more
authorized entities, the more complex the verication can be on the receiver side.
Accordingly, it is advised that a small number of entities be given authorization for
decreasing the complexity of verication (Binance Academy, 2020). Meanwhile, the more
maintainers in a PoA-based BC, the higher the overall security level of the system.
In this case study, we run FoBSim several times, with which we deploy different CAs
under similar conditions. The simulation runs targeted specically the measurement of
the average time consumed by each CA, from the moment where a miner is triggered
to mint a new block, until the minted block by this miner is conrmed by, at least, 51% of
other BC miners. To accurately measure this average, we added some variables holding the
starting time and the elapsed time, exactly before calling the build_block() function and
right after a block is conrmed by reaching the required number of conrmations.
As described in Table 3, we changed the difculty of the puzzle during the PoW-based
BC simulation runs from an easy level (5), to a harder level (10), and nally to very hard
levels (15) and (20). During the runs where PoA was used, we changed the number of
authorized miners from 2/5 (2 authorized out of a total of 5 miners), 5/10, 10/20, and 25
authorized miners for the rest of runs.
As we wanted to abstractly measure the average conrmation time, we avoided the
computational services and payment functionality, because both imply extra time
consumption for performing the computational tasks, and validating the payments,
respectively. We also avoided the identity management functionality because the number
of TXs per end-user is limited by the number of ID attributes required to be saved on
the chain. Hence, our best choice was the data management functionality. We kept the
total number of TXs delivered to the mempool unchanged, which gives equivalent
input for all simulation runs. However, we changed the number of TXs generated by each
user as to be equal to the number of miners in each run. More precisely, as the total
Table 3 Simulation parameters conguration for Case 1.
Simulation parameter\ Consensus PoW PoS PoA
No. of miners 5500 5500 5500
No. of neighbors per miner 4 4 4
Puzzle difculty 520 ––
Authorized miners All Random choice 225
Initial wallet 1,000
BC functionality Data Management Data Management Data Management
BC deployment end-user layer end-user layer end-user layer
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 29/40
number of TXs is determined using Eq. (2), where a,band care the number of fog nodes,
the number of end-users, and the number of TXs per end-user, respectively, the values of
those variables uctuated in each run. Concerning the runs where a PoS is deployed,
miner nodes were initiated with a wallet that has 1,000 coins, allowing miners to stake
random amounts of coins. Additionally, winning miners were awarded 5 coins for each
conrmed block they had minted.
We deployed the FoBSim environment on Google Cloud Platform, using a C2-
standard-16 (up to 3.8 GHz, 16 vCPUs, 64 GB memory), with Debian OS. We have chosen
to place the BC in the end-user layer for all runs, not for any reason other than testing
the reliability and stability, of FoBSim components and results, in such complex inter-
operable (Belchior et al., 2020) Edge-Fog-BC scenarios. Table 4 presents the exact results
we obtained, which are depicted in Figs. 7A and 7B.
According to the results obtained from the simulation runs, one can notice that
PoW-based BCs consume much more time to conrm a block, than PoA- and PoS-based
BCs, which is inline with the theoretical and experimental results of most previous
research. Additionally, the average block conrmation time, in PoW-based and PoA-based
BCs, seems to be directly proportional to the BC network size, which complies with the
Table 4 Results of Case-1, where the PoW puzzle difculty ranges from 5 to 20, and the number of
Miners (M) ranges from 5 to 500.
M=5 M=10 M=20 M=50 M= 100 M= 500
PoS algorithm 0.018 0.06 0.18 0.046 0.09 0.19
PoA algorithm 0.002 0.008 0.03 0.2 0.41 2.94
PoW-5 algorithm 0.08 0.36 2.1 1.31 6.15 60.6
PoW-10 algorithm 0.07 0.44 2.1 2.03 5.21 58.9
PoW-15 algorithm 0.25 0.42 2.23 2.26 6.18 74.76
PoW-20 algorithm 6.02 9.5 24.2 59.62 ––
Figure 7 Average block conrmation time (A) consumed by PoS-based BC vs. PoA-based BC,
relatively to the number of miner nodes (B) consumed by PoW-based BC (the cases of difculty =
5, 10, 15, and 20), relatively to the number of miner nodes.
DOI: 10.7717/peerj-cs.431/g-7
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 30/40
results recently presented in Misic, Misic & Chang (2020). Comparatively, an average block
conrmation time in a PoS-based BC seems unaffected by the network size, which
complies with the expectations recently presented in Cao et al. (2020).
Case 2: Capturing the effect of using the gossip protocol
In this case, we compare the number of chain forks at the end of several simulation
runs, where we interchangeably activate and deactivate the gossiping property in a
PoW-based BC. Accordingly, one can notice the effect of gossiping on ledger nality under
different conditions, namely the puzzle difculty and the transmission delay between
miners. As it was mentioned in Gossip Protocol, gossiping is a continuous process
during the life time of the network, which implies that miners would mostly have
different chain versions at any given moment. In this case, we detect the number of
chain versions at the end of simulation runs, which can be decreased to one version under
strictly designed parameters, such as medium network size, high puzzle difculty, low
transmission delay, low number of neighbors per miner, etc. Nevertheless, our goal in
this case is to demonstrate how the activation of the gossiping property during a
simulation run on FoBSim can decrease the number of chain versions and, thus, it can
positively contribute to the consistency of the distributed ledger. For this case, we also
deployed the FoBSim environment on the Google Cloud Platform, using a C2-standard-16
VM (up to 3.8 GHz, 16 vCPUs, 64 GB memory), with Ubuntu OS.
Table 5 presents the initial conguration in each simulation scenario, while Tables 6
and 7present the results we obtained by running the described scenarios, which are
depicted in Figs. 8A and 8B. As can be noted from the results, the default gossip protocol in
FoBSim could decrease the number of chain versions at the end of each simulation run.
Although the number of chain versions did not reach the optimum value (i.e., one
chain version), it is obvious that activating the gossiping property decreases the number of
Table 5 Simulation parameters conguration for Case-2, where the Gossiping property is
interchangeably activated and deactivated.
Simulation parameter Puzzle difculty effect Transmission delay effect
No. of fog nodes 5 5
No. of users per fog node 5 5
No. of TX per user 5 5
No. of miners 100 100
No. of neighbors per miner 2 2
No. of TX per block 5 5
Puzzle difculty 5, 10, 15, 20 20
Max end-user payment 100 100
Minersinitial wallet value 100 100
Mining award 5 5
Delay between neighbors 0 0, 5, 10, 15, 20
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 31/40
chain versions at each simulation run and, thus, enhances the distributed ledger
Case 3: Comparing deployment efficiency of BC in the fog layer vs.
end-user layer
In this case, we compare BC deployment efciency in the fog layer and end-user layer.
The efciency we are seeking is determined by both the total time needed to perform all
requested BC services and total storage cost. That is, less time and storage needed to
perform all tasks (e.g., conrm all newly minted blocks or run the generated SCs) indicates
higher efciency of the BC system. To fairly compare the BC efciency when deployed in
those two layers, we stabilize all BC parameters that are congurable in FoBSim, except for
the number of miner nodes to deduce the trend in total time consumption when the
network dynamically allows for new nodes to join the network. We deployed the
FoBSim tool on the Google Cloud Platform, using a C2-standard-16 VM (up to 3.8 GHz,
Figure 8 The effect of activating the gossiping protocol in FoBSim, on the number of chain versions
at the end of PoW-based BC simulation runs, where (A) the puzzle difculty uctuates from 5 to 20
and (B) the transmission delay between neighboring miners uctuates from 0 to 25 ms.
DOI: 10.7717/peerj-cs.431/g-8
Table 6 Results of Case-2, where the puzzle difculty ranges from 520, and the Gossiping in
FoBSim was interchangeably activated and deactivated.
Conguration diff. = 5 diff. = 10 diff. = 15 diff. = 20
Gossip activated 81 70 57 16
Gossip deactivated 92 98 100 67
Table 7 Results of Case-2, where the transmission delay between neighbors ranged from 025 ms.,
and the Gossiping in FoBSim was interchangeably activated and deactivated.
Conguration T.D. = 0 T.D. = 5 T.D. = 10 T.D. = 15 T.D. = 25
Gossip activated 12 18 14 26 33
Gossip deactivated 15 39 59 68 76
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 32/40
16 vCPUs, 64 GB memory), with Ubuntu OS. The detailed parameter conguration while
running the described scenarios is presented in Table 8.
Recalling the results presented in Bi, Yang & Zheng (2018) and Li et al. (2017),
average transmission delay between miners in the fog layer can be estimated by 12 ms.,
while it can be estimated between miners in the end-user layer to 1,000 ms. (higher
transmission delays were reported in well known BC networks, such as Bitcoin, in
Sallal (2018)). We simulated the data management BC service and PoW consensus with
gossiping activated. According to Eq. (2), the number of requested tasks was automatically
modied due to the continuous change in the number of fog nodes (since we oscillated
the number of fog nodes to deduce the trend of total time consumption). The total average
time for performing requested BC services, in similar simulation sittings, while the BC is
deployed in end-user and fog layers, is compared in Fig. 9A.
To accurately measure the storage cost during the simulation run, we implemented an
independent Python code, available in the FoBSim repository, namely storage_cost_analysis.
Table 8 Simulation parameters conguration for Case-3, where the efciency of BC is assessed in the
fog layer and end-user layer, in terms of total run time and total storage cost.
Simulation parameter For total time efciency For total storage efciency
No. of fog nodes 10100 100
No. of users per fog node 2 5
No. of TX per user 2 5
No. of miners 10100 100
No. of neighbors per miner 3 5
No. of TX per block 5 5
Puzzle difculty 20 15
Max end-user payment 100 100
Minersinitial wallet value 1,000 1,000
Mining award 5 5
Delay between neighbors fog layer: 12 ms., fog layer: 12 ms.,
end-user layer: 1,000 ms end-user layer: 1,000 ms
Figure 9 BC efciency comparison while deployed in end-user layer vs. fog layer, in terms of
(A) total elapsed time for the BC network to perform requested services, and (B) total storage
used by the BC network to perform requested services. Full-size
DOI: 10.7717/peerj-cs.431/g-9
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 33/40
py. As described in FoBSim Modules, the output analysis les, ledgers, wallets, etc. of
running a given simulation scenario using FoBSim, are automatically saved in a folder
titled temporarywithin the same repository. Thus, our implemented storage analyzer
aims at regularly (i.e., every one second as a default sitting) measuring the size of this
temporary folder while the simulation is running. The measured sizes are then saved into
an Excel sheet to facilitate performing the analysis we are seeking. To exemplify this, the
total storage used by the BC network is compared in Fig. 9B, where similar simulation
sittings were congured (detailed in Table 8), except for the layer where the BC is
It can be noted from the results presented in the third case that deploying the BC
network in the fog layer may enhance its efciency in terms of total time consumed to
perform similar tasks in similar conguration, and in terms of total storage cost by the BC
network to maintain the same distributed ledger (same number of conrmed blocks by the
end of the simulation run).
In this paper, we proposed a novel simulation tool called FobSim that mimics the
interaction between the entities of an integrated Fog-Blockchain system. We briey
described the architectural elements of Fog Computing (FC) and Blockchain (BC)
technologies, and designed FoBSim in order to cover all the elements we described.
We deployed three different consensus algorithms, namely PoW, PoS and PoA, and
different deployment options of the BC in an FC architecture, namely the end-user layer
and the fog layer. Additionally, we ne tuned the FoBSim modules so that various services,
provided by FC and BC, can be adopted for any proposed integration scenario.
The services that can be simulated are distributed payment services, distributed identity
services, distributed data storage and distributed computational services (through Smart
Contracts). In our paper, we described the modules of FoBSim, the TX modeling, the
Genesis block generation, the gossiping in FoBSim, the Consensus Algorithms, TX and
block validation, incentive mechanisms, and other FoBSim strategies. We validated
FoBSim with case studies: the rst compares the average time consumption for block
conrmation in different consensus algorithms, while the second analyzes the effect of
gossiping on the consistency of the distributed ledger, in uctuated puzzle difculty and
transmission delay congurations. The last case compared the efciency of the BC
network, in terms of total time consumption and total storage required to perform similar
tasks, when deployed in the fog layer against the end-user layer. The results of the rst case
showed that the PoS algorithm provides the least average block conrmation time,
followed by PoA and PoW, respectively. The results of the second case showed how the
gossiping protocol, implemented within FoBSim, effectively contributes to enhance the
consistency of the distributed ledger. The last case showed that deploying the BC network
in the fog layer may drastically enhance the BC performance, in terms of total execution
time and total storage cost, due to low transmission delay between miners.
In the future releases of FoBSim, we are willing to make more CAs available, as well
as enhancing the identity management scheme in FoBSim. We will further investigate
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 34/40
adding the reputation management service in a generalized and simple manner so that
analysis can be provided, while proposed reputation management ideas, conditions, or
properties can be easily implemented/modied.
This research was supported by the Hungarian Scientic Research Fund under the grant
number OTKA FK 131793, by the grant NKFIH-1279-2/2020 of the Ministry for
Innovation and Technology, Hungary, and by the National Research, Development and
Innovation Ofce within the framework of the Articial Intelligence National Laboratory
Programme. The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Hungarian Scientic Research Fund: OTKA FK 131793.
Ministry for Innovation and Technology, Hungary: NKFIH-1279-2/2020.
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Hamza Baniata conceived and designed the experiments, performed the experiments,
analyzed the data, performed the computation work, prepared gures and/or tables,
authored or reviewed drafts of the paper, and approved the nal draft.
Attila Kertesz conceived and designed the approach and the experiments, analyzed the
data, authored and reviewed drafts of the paper, and approved the nal submission.
Data Availability
The following information was supplied regarding data availability:
The source code of the simulator is available at GitHub:
Supplemental Information
Supplemental material for this article can be found online at
Alharby M, Van Moorsel A. 2019. Blocksim: a simulation framework for blockchain systems.
ACM SIGMETRICS Performance Evaluation Review 46(3):135138.
Anilkumar V, Joji JA, Afzal A, Sheik R. 2019. Blockchain simulation and development platforms:
survey, issues and challenges. In: International Conference on Intelligent Computing and Control
Systems (ICCS). IEEE, 935939.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 35/40
Avasthi AA, Saxena A. 2018. Two hop blockchain model: resonating between proof of work
(PoW) and proof of authority (PoA). International Journal of Information Systems &
Management Science 1(1):1 DOI 10.1504/IJISAM.2018.10014439.
Bahga A, Madisetti V. 2017. Blockchain applications: a hands-on approach. Blacksburg: VPT.
Baniata H, Kertész A. 2020. PF-BVM: a privacy-aware fog-enhanced blockchain validation
mechanism. In: CLOSER. 430439.
Baniata H. 2020. Fog-enhanced blockchain simulation. In: The 12th Conference of PhD Students in
Computer Science (CS2). University of Szeged. 8387.
Baniata H, Almobaideen W, Kertesz A. 2020. A privacy preserving model for Fog1013 enabled
MCC systems using 5G connection. In: Fifth International Conference on Fog and Mobile Edge
Computing (FMEC). Piscataway: IEEE, 223230.
Baniata H, Anaqreh A, Kertesz A. 2021. PF-BTS: a privacy-aware fog-enhanced blockchain-
assisted task scheduling. In: Information Processing and Management. Vol. 58.
Baniata H, Kertesz A. 2020. FoBSim. GitHub. Available at
(accessed 27 October 2020).
Baniata H, Kertesz A. 2020. A survey on blockchain-fog integration approaches. IEEE Access
8:102657102668 DOI 10.1109/ACCESS.2020.2999213.
Beck R, Avital M, Rossi M, Thatcher JB. 2017. Blockchain technology in business and information
systems research. Business & Information Systems Engineering 59:381384.
Belchior R, Vasconcelos A, Guerreiro S, Correia M. 2020. A survey on blockchain
interoperability: past, present, and future trends. Available at
Bentov I, Gabizon A, Mizrahi A. 2016. Cryptocurrencies without proof of work. In: International
Conference on Financial Cryptography and Data Securitys. Springer, 142157.
Bi W, Yang H, Zheng M. 2018. An accelerated method for message propagation in blockchain
networks. arXiv. Available at
Binance Academy. 2020. Proof of authority explained. Available at
en/articles/proof-of-authority-explained (accessed 27 October 2020).
Bisnik N, Abouzeid AA. 2007. Optimizing random walk search algorithms in P2P networks.
Computer Networks 51(6):14991514. 2009. Bitcoin is an innovative payment network and a new kind of money.
Available at (accessed 27 October 2020).
Blywis B, Günes M, Juraschek F, Hahm O, Schmittberger N. 2011. A survey of ooding, gossip
routing, and related schemes for wireless multi-hop networks. Tech. rep. Berlin, Germany: Freie
Universitat. Available at bitstream/handle/fub188/18401/2010-
Bonomi F, Milito R, Natarajan P, Zhu J. 2014. Fog computing: a platform for internet of things
and analytics. In: Big Data and Internet of Things: A Roadmap for Smart Environments. Springer,
Bruno. 2018. Explaining ethereum tools: what are geth and mist? Available at
%20it,fromWei(eth. (accessed 27 October 2020).
Buntinx JP. 2017. What is proof of elapsed time. Available at https://themerkle. com/what-is-proof-
of-elapsed-time/ (accessed 8 August 2020).
Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R. 2011. CloudSim: a toolkit for
modeling and simulation of cloud computing environments and evaluation of resource
provisioning algorithms. Software: Practice and Experience 41(1):2350.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 36/40
Cao B, Zhang Z, Feng D, Zhang S, Zhang L, Peng M, Li Y. 2020. Performance analysis and
comparison of PoW, PoS and DAG based blockchains. Digital Communications and Networks
6(4):480485 DOI 10.1016/j.dcan.2019.12.001.
Chen L, Xu L, Shah N, Gao Z, Lu Y, Shi W. 2017. On security analysis of proof-of-elapsed-time
(poet). In: International Symposium on Stabilization, Safety, and Security of Distributed Systems.
Springer, 282297.
Coladangelo A, Sattath O. 2020. A quantum money solution to the blockchain scalability problem.
Available at
Coutinho A, Greve F, Prazeres C, Cardoso J. 2018. Fogbed: a rapid-prototyping emulation
environment for fog computing. In: IEEE International Conference on Communications (ICC).
IEEE, 17.
Dastjerdi AV, Gupta H, Calheiros RN, Ghosh SK, Buyya R. 2016. Fog computing: principles,
architectures, and applications. In: Internet of Things. Amsterdam: Elsevier, 6175.
De Angelis S, Aniello L, Baldoni R, Lombardi F, Margheri M, Sassone V. 2018. PBFT vs proof-
of-authority: applying the CAP theorem to permissioned blockchain. Available at eprints.soton.
Debe M, Salah K, Rehman MHU, Svetinovic D. 2019. IoT public fog nodes reputation system: a
decentralized solution using Ethereum blockchain. IEEE Access 7:178082178093
DOI 10.1109/ACCESS.2019.2958355.
Deirmentzoglou E, Papakyriakopoulos G, Patsakis C. 2019. A survey on long-range attacks for
proof of stake protocols. IEEE Access 7:2871228725 DOI 10.1109/ACCESS.2019.2901858.
Deshpande A, Nasirifard P, Jacobsen H-A. eVIBES: congurable and interactive ethereum
blockchain simulation framework. In: Proceedings of the 19th International Middleware
Conference (Posters). 1112.
Ethereum. 2020. Remix platform. Available at (accessed 27 October
Fan C, Ghaemi S, Khazaei H, Musilek P. 2020. Performance evaluation of blockchain systems: a
systematic survey. IEEE Access 8:126927126950.
Farhadi M, Lanet J-L, Pierre G, Miorandi D. 2020. A systematic approach toward security in fog
computing: assets, vulnerabilities, possible countermeasures. Software: Practice and Experience
Faria C, Correia M. 2019. BlockSim: blockchain simulator. In: IEEE International Conference on
Blockchain (Blockchain). Piscataway: IEEE, 439446.
Fröhlich P, Gelenbe E, Nowak MP. 2020. Smart SDN management of fog services. In: 2020 Global
Internet of Things Summit (GIoTS). Piscataway: IEEE, 16.
Gabor D, Jelasity M. 2018. Robust decentralized mean estimation with limited communication. In:
European Conference on Parallel Processing. Springer, 447461.
Gansterer WN, Niederbrucker G, Straková H, Grotthoff SS. 2013. Scalable and fault tolerant
orthogonalization based on randomized distributed data aggregation. Journal of Computational
Science 4(6):480488.
Gervais A, Karame GO, Wüst K, Glykantzis V, Ritzdorf H, Capkun S. 2016. On the security and
performance of proof of work blockchains. In: Proceedings of the 2016 ACM SIGSAC Conference
on Computer and Communications Security. 316.
Global Times. 2019. China launches blockchain-based smart city identication system.
Available at (accessed 27 October 2020).
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 37/40
Gupta H, Dastjerdi AV, Ghosh SK, Buyya R. 2017. iFogSim: a toolkit for modeling and
simulation of resource management techniques in the Internet of Things, Edge and Fog
computing environments. Software: Practice and Experience 47(9):12751296.
Habibi P, Farhoudi M, Kazemian S, Khorsandi S, Leon-Garcia A. 2020. Fog computing: a
comprehensive architectural survey. IEEE Access 8:6910569133
DOI 10.1109/ACCESS.2020.2983253.
He X, Cui Y, Jiang Y. 2019. An improved gossip algorithm based on semi- distributed blockchain
network. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge
Discovery (CyberC). Piscataway: IEEE, 2427.
Karame GO, Androulaki E, Capkun S. Double-spending fast payments in bitcoin. In: Proceedings
of the 2012 ACM Conference on Computer and Communications Security. 906917.
Kempe D, Dobra A, Gehrke J. Gossip-based computation of aggregate informa1028 tion. In: 44th
Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings. Piscataway:
IEEE, 482491.
King S, Nadal S. 2012. Ppcoin: Peer-to-peer crypto-currency with proof-of-stake. In: Self-published
Paper. 1.
Kreku J, Vallivaara VA, Halunen K, Suomalainen J, Ramachandran M, Muñoz VM, Kantere V,
Wills G, Walters RJ. 2017. Evaluating the efciency of blockchains in IoT with simulations. In:
IoTBDS. 216223.
Kumar T, Harjula E, Ejaz M, Manzoor A, Porambage P, Ahmad I, Liyanage M, Braeken A,
Ylianttila M. 2020. BlockEdge: blockchain-edge framework for industrial IoT networks. In:
IEEE Access. Piscataway: IEEE.
Lan J, Liu X, Shenoy P, Ramamritham K. 2003. Consistency maintenance in peer-to-peer le
sharing networks. In: Proceedings the Third IEEE Workshop on Internet Applications. WIAPP
2003. Piscataway: IEEE, 9094.
Larimer D. 2013. Transactions as proof-of-stake. Available at
Li J, Zhang T, Jin J, Yang Y, Yuan D, Gao L. 2017. Latency estimation for fog-based internet of
things. In: 27th International Telecommunication Networks and Applications Conference
(ITNAC). Piscataway: IEEE, 16.
Liaskos S, Anand T, Alimohammadi N. 2020. Architecting blockchain network simulators: a
model-driven perspective. In: IEEE International Conference on Blockchain and Cryptocurrency
(ICBC). Piscataway: IEEE, 13.
Lopes MM, Higashino WA, Capretz MAM, Bittencourt LF. 2017. Myifogsim: a simulator for
virtual machine migration in fog computing. In: Companion Proceedings of the10th
International Conference on Utility and Cloud Computing. 4752.
Maes SH, Perreira M, Murray BP, Bharadhwaj R. 2018. US Patent 9,882,829.
Majeed U, Kim K, Hong CS. 2020. Mining pool selection strategy in blockchain networks: a
probabilistic approach. KIISE Transactions on Computing Practices 26(6):280285.
Markakis EK, Karras K, Zotos N, Sideris A, Moysiadis T, Corsaro A, Alexiou G, Skianis C,
Mastorakis G, Mavromoustakis CX, Pallis E. 2017. EXEGESIS: extreme edge resource
harvesting for a virtualized fog environment. IEEE Communications Magazine 55(7):173179.
Markus A, Kertesz A. 2020. A survey and taxonomy of simulation environments modelling fog
computing. Simulation Modelling Practice and Theory 101:102042
DOI 10.1016/j.simpat.2019.102042.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 38/40
Mayer R, Graser L, Gupta H, Saurez E, Ramachandran U. 2017. Emufog: extensible and scalable
emulation of large-scale fog computing infrastructures. In: 2017 IEEE Fog World Congress
(FWC). Piscataway: IEEE, 16.
Memon RA, Li J, Ahmed J, Khan A, Mangrio MI. 2018. Modeling of blockchain based systems
using queuing theory simulation. In: 15th International Computer Conference on Wavelet Active
Media Technology and Information Processing (ICCWAMTIP). IEEE, 107111.
Misic J, Misic VB, Chang X. 2020. Performance of Bitcoin network with syn1070 chronizing nodes
and a mix of regular and compact blocks. In: IEEE Transactions on Network Science and
Engineering. Piscataway: IEEE.
Montresor A, Jelasity M. 2009. PeerSim: a scalable P2P simulator. In: IEEE Ninth International
Conference on Peer-to-Peer Computing. Piscataway: IEEE, 99100.
Naas MI, Boukhobza J, Parvedy PR, Lemarchand L. 2018. An extension to ifogsim to enable the
design of data placement strategies. In: IEEE 2nd International Conference on Fog and Edge
Computing (ICFEC). Piscataway: IEEE, 18.
Nakamoto S. 2019. Bitcoin: a peer-to-peer electronic cash system. Available at
Narayanan A, Bonneau J, Felten E, Miller A, Goldfeder S. 2016. Bitcoin and cryptocurrency
technologies: a comprehensive introduction. Princeton: Princeton University Press.
Negy KA, Rizun PR, Sirer EG. 2020. Selsh mining re-examined. In: International Conference on
Financial Cryptography and Data Security. Springer, 6178.
Nikdel Z, Gao B, Neville SW. 2017. DockerSim: full-stack simulation of container based Software-
as-a-Service (SaaS) cloud deployments and environments. In: IEEE Pacic Rim Conference on
Communications, Computers and Signal Processing (PACRIM). Piscataway: IEEE, 16.
OpenFog Consortium. 2017. OpenFog reference architecture for fog computing. In: Architecture
Working Group. 1162.
Petri I, Barati M, Rezgui Y, Rana OF. 2020. Blockchain for energy sharing and trading in
distributed prosumer communities. Computers in Industry 123(8):103282
DOI 10.1016/j.compind.2020.103282.
Piriou P-Y, Dumas J-F. 2018. Simulation of stochastic blockchain models. In: 14th European
Dependable Computing Conference (EDCC). Piscataway: IEEE, 150157.
Qayyum T, Malik AW, Khan Khattak MA, Khalid O, Khan SU. 2018. FogNetSim++: a toolkit for
modeling and simulation of distributed fog environment. IEEE Access 6:6357063583
DOI 10.1109/ACCESS.2018.2877696.
Rahman UU, Bilal K, Erbad A, Khalid O, Khan SU. 2019. Nutshell: simulation toolkit for
modeling data center networks and cloud computing. IEEE Access 7:1992219942.
Raman RK, Vaculin R, Hind M, Remy SL, Pissadaki EK, Bore NK, Daneshvar R, Srivastava B,
Varshney KR. 2019. A scalable blockchain approach for trusted computation and veriable
simulation in multi-party collaborations. In: IEEE International Conference on Blockchain and
Cryptocurrency (ICBC). Piscataway: IEEE, 277284.
Sallal MF. 2018. Evaluation of security and performance of clustering in the bitcoin network, with
the aim of improving the consistency of the blockchain. PhD thesis, University of Portsmouth.
Sargent RG. 2013. Verication and validation of simulation models. Journal of Simulation 1:1224.
Sheikh S. 2018. Proof-of-work vs proof-of-stake: a comparative analysis and an approach to
blockchain consensus mechanism. . International Journal for Research in Applied Science &
Engineering Technology 6(12):786791.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 39/40
Singh PK, Singh R, Nandi SK, Nandi S. 2019. Managing smart home appliances with proof of
authority and blockchain. In: International Conference on Innova- tions for Community Services.
Springer, 221232.
Smart Dubai Department. 2020. Blockchain. Available at
blockchain (accessed 27 October 2020).
Smartcity Press. 2019. China taking a big leap with blockchain. Available at https://www.smartcity.
press/blockchain-technology-china/ (accessed 27 October 2020).
Sodhro AH, Pirbhulal S, Muzammal M, Zongwei L. 2020. Towards blockchain-enabled security
technique for industrial internet of things based decentralized applications. Journal of Grid
Computing 18:114.
Sonmez C, Ozgovde A, Ersoy C. 2018. Edgecloudsim: an environment for performance
evaluation of edge computing systems. Transactions on Emerging Telecommunications
Technologies 29(11):e3493 DOI 10.1002/ett.3493.
The Linux Foundation. 2020. What is hyperledger? Available at
(accessed 27 October 2020).
The Python Software Foundation. 2020. The Python standard library. Available at https://docs. (accessed 14 September 2020).
The Python Software Foundation. 2020. The Python standard library. Available at https://docs. (accessed 14 September 2020).
Trufe Blockchain Group. 2020. Trufe overview. Available at
trufe/overview (accessed 27 October 2020).
Vujičic D, JagodićD, RanđićS. 2018. Blockchain technology, bitcoin, and Ethereum: a brief
overview. In: 17th International Symposium Infoteh-Jahorina (infoteh). Piscataway: IEEE, 16.
Wang B, Chen S, Yao L, Liu B, Xu X, Zhu L. 2018. A simulation approach for studying behavior
and quality of blockchain networks. In: International Conference on Blockchain. Cham: Springer,
Wang B, Wang Q, Chen S, Xiang Y. 2020. Security analysis on tangle-based blockchain through
simulation. In: Australasian Conference on Information Security and Privacy. Cham: Springer,
Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F, Niakanlahiji A, Kong J, Jue JP. 2019. All
one needs to know about fog computing and related edge computing paradigms: a complete
survey. Journal of Systems Architecture 98(2011):289330 DOI 10.1016/j.sysarc.2019.02.009.
Zhang R, Chan WKV. 2020. Evaluation of energy consumption in block-chains with proof of work
and proof of stake. Journal of Physics: Conference Series 1584:012023.
Zhao F, Guo X, Chan WK. 2020. Individual green certicates on blockchain: a simulation
approach. Sustainability 12(9):3942 DOI 10.3390/su12093942.
Baniata and Kertesz (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.431 40/40
... In this approach we merge Fog Computing and Blockchain technologies to provide a privacyaware, scalable and interoperable solution for effective vaccination information management. We derive three scenarios from real world vaccination reports, and use their requirements to evaluate the scalability of various Blockchain systems with the FoBSim tool [15]. The results can serve as recommendations for possible implementations of our proposal, which could contribute to a better and more efficient fight against COVID-19. ...
... Therefore we define three different scenarios based on real world data with different scalability needs. In an earlier work we developed a BC simulation tool called FoBSim [15]. It can be used to investigate the behavior of a BC system by using different parameters and consensus algorithms, hence we will use this tool for the evaluation. ...
... FoBSim allows for investigating BC systems with three different consensus algorithms by default: Proof-of-Authority (PoA), Proof-of-Stake (PoS) and Proof-of-Work (PoW) (see [15] for definitions and implementation details). ...
Full-text available
Following the outbreak of the COVID-19 virus in the beginning of 2020, a new pandemic has spread over the world, changing our lives. Emerging technologies have played an important role in providing various solutions for preventing virus spreading. Different contact tracing, social distancing and vaccination passport applications have been developed to reduce the number of infections, and to overcome certain restrictions. Nevertheless, their widespread use is hindered by the general lack of trust associated with them, due to their centralized nature and lack of privacy-awareness, explainability and interoperability. In this article we propose VACFOB, a general architecture for VACcination information validation and tracking with a FOg and cloud-based Blockchain system. In this vision, we merge Fog Computing and Blockchain technologies to provide a privacy-aware and scalable approach for interoperable and effective vaccination information management. We also evaluate the scalability of the underlying Blockchain system by means of simulation.
... We have previously shown in [4] how a BC deployed in the fog layer outperforms a BC deployed in the end-user layer, in terms of block finality and storage. This paper aims at analyzing the forking phenomenon in integrated FC-BC systems that utilizes a probabilistic finality mechanism. ...
... To obtain accurate measures for our analysis, we used the FoBSim tool [4] for mimicking BC operations both in the end-user and fog layers. The main advantage of FoBSim is that it provides means for investigating integrated BC-FC systems using different deployment options and different consensus mechanisms. ...
... Note that, even if there was only one chain version, one cannot obtain an inconsistency level of 0% using Equation 3. To solve this, we can further develop Equation 3 into Equation 4. ...
Conference Paper
Full-text available
Both revolutionary technologies of Fog Computing (FC) and Blockchain (BC) serve as enablers for enhanced, people-centric trusted applications, and they do meet in the provision of higher standards and expectations. In this paper, we address the reliability of fog-enhanced BC systems by analyzing the forking phenomenon under different conditions, and provide a reliable Distributed Ledger (DL) consistency assessment. We use the FoBSim tool that is specifically designed to mimic and emulate realistic FC-BC integration, in which we deploy the Proof-of-Work (PoW) consensus algorithm and analyze the forking probability under fluctuating conditions. Based on our results, we propose an inconsistency formula, which can quantitatively describe how consistent the DL in a BC system can be. Finally, we show how to deploy this formula in a decision making model for indicating optimal deployment features of a BC network in a Fog-enhanced system.
... We have previously proposed an extensible tool for simulating integrated Fog-BC applications, called FoBSim [26]. FoBSim provides easy configuration through its Command Line Interface (CLI) for selecting BC deployment model, data model, Consensus algorithm and application model suitable for scenarios to be simulated. ...
Conference Paper
Full-text available
The unprecedented pace of technological development in smart systems, incorporating sensing, actuation, and control functions, have the following properties and needs: (𝑖) they are interconnected and need scalable, virtualized resources to run, store and process data, (𝑖𝑖) they are mobile and can potentially access and build on user data made available by smartphones and tablets, and (𝑖𝑖𝑖) they are getting smarter, so they may get access to user data provided by connected smart devices. As the number of smart devices in smart systems grows, the vast amount of data they produce requires high-performance computational and storage services for processing and analysis and other novel techniques and methods that enhance these services and their management. Blockchain applications have been proposed in a wide variety of environments such as distributed voting, eHealth, Mobile Computing, Internet of Vehicles, etc. We believe that integrating Blockchain technology with smart applications for managing data of mobile devices can further enhance the privacy and security requirements of current complex systems. In this paper, we discuss Blockchain-integration possibilities for smart systems to support the efficient, secure, and privacy-aware execution of smart applications. We propose a design space where issues need to be solved in different layers of such integrated systems. Accordingly, we envision a Blockchain-enabled simulation framework capable of analyzing the integration possibilities with fog/edge and cloud infrastructures at different layers of smart systems. The framework will be able to model and analyze the behavior of Blockchain networks in large-scale fog-enhanced smart systems while using different AI methods.
Full-text available
The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.
Full-text available
In recent years, the deployment of Cloud Computing (CC) has become more popular both in research and industry applications, arising form various fields including e-health, manufacturing, logistics and social networking. This is due to the easiness of service deployment and data management, and the unlimited provision of virtual resources (VR). In simple scenarios, users/applications send computational or storage tasks to be executed in the cloud, by manually assigning those tasks to the available computational resources. In complex scenarios, such as a smart city applications, where there is a large number of tasks, VRs, or both, task scheduling is exposed as an NP-Hard problem. Consequently, it is preferred and more efficient in terms of time and effort, to use a task scheduling automation technique. As there are many automated scheduling solutions proposed, new possibilities arise with the advent of Fog Computing (FC) and Blockchain (BC) technologies. Accordingly, such automation techniques may help the quick, secure and efficient assignment of tasks to the available VRs. In this paper, we propose an Ant Colony Optimization (ACO) algorithm in a fog-enabled Blockchain-assisted scheduling model, namely PF-BTS. The protocol and algorithms of PF-BTS exploit BC miners for generating efficient assignment of tasks to be performed in the cloud's VRs using ACO, and award miner nodes for their contribution in generating the best schedule. In our proposal, PF-BTS further allows the fog to process, manage, and perform the tasks to enhance latency measures. While this processing and managing is taking place, the fog is enforced to respect the privacy of system components, and assure that data, location, identity, and usage information are not exposed. We evaluate and compare PF-BTS performance, with a recently proposed Blockchain-based task scheduling protocol, in a simulated environment. Our evaluation and experiments show high privacy awareness of PF-BTS, along with noticeable enhancement in execution time and network load.
Full-text available
The decentralisation of energy supply and demand can contribute decisively to protecting the environment and climate of the planet by consuming electricity in the proximity of the generation source and avoiding losses in transmission and distribution. Supporting energy transactions with emerging intelligent technologies can advance the development of energy communities and accelerate the integration of renewable sources. Distributed energy solutions play an essential role as they are explicitly designed to produce, store and deliver green energy. Profiting with these benefits is essential, especially in the context of the current debate on stopping climate change. Several technologies such as waste heat recovery with intelligent algorithms can improve the energy distribution and provide significant resource savings. On the other hand, the usage of Blockchain technology in energy markets promises to incentivise the use of renewables and provide a reliable framework to monitor real-time information of energy production and consumption. Blockchain can also enable trading between independent agents and lead to the formation of more secured energy communities. In this paper, we demonstrate how Blockchain can be utilised to support the formation and use of energy communities. We propose a Blockchain-based energy framework as a mean to support energy exchanges in a community of prosumers. We demonstrate how smart contracts can manage energy transactions and enable a more secured trading environment between consumers and producers. We utilise data and models from a real fish processing industrial site in Milford Haven Port, South Wales, based on which we validate our research hypothesis.
Full-text available
Industry 4.0 has promised to bring new digital ecosystem for various key industrial applications in terms of providing secure, intelligent, autonomous and self-adaptive industrial IoT (IIoT) networks. Such industrial systems will be relatively more complex because of the involvement of the vast number of the heterogeneous sensors/devices and diverse nature of various stakeholders and service providers. These complex industrial processes also have strict requirements in terms of low-latency services/resources together with security, trust and process mon-itoring/tracking among others. In this context, Blockchain and Edge Computing emerge as prominent technologies to strengthen the vision of the digitization for the various industries by fulfilling/addressing the essential requirements for processes. For example, edge paradigm ensures low latency services for IIoT applications and on the other hand, blockchain provides key features such as security, trust and decentralization among others. In this paper, we have proposed a 'BlockEdge' framework that primarily combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. We have also provided performance evaluation of the proposed framework using well-known simulation environment for edge/fog computing, i.e.'iFogSim' and compared with the existing IIoT model which does not include blockchain.
Full-text available
As the Industrial Internet of Things (IIoT) is one of the emerging trends and paradigm shifts to revolutionize the traditional industries with the fourth wave of evolution or transform it into Industry 4.0. This all is merely possible with the sensor-enabled technologies, e.g., wireless sensor networks (WSNs) in various landscapes, where security provisioning is one of the significant challenges for miniaturized power hungry networks. Due to the increasing demand for the commercial Internet of things (IoT) devices, smart devices are also extensively adopted in industrial applications. If these devices are compromising the date/information, then there will be a considerable loss and critical issues, unlike information compromising level by the commercial IoT devices. So emerging industrial processes and smart IoT based methods in medical industries with state-of-the-art blockchain security techniques have motivated the role of secure industrial IoT. Also, frequent changes in android technology have increased the security of the blockchain-based IIoT system management. It is very vital to develop a novel blockchain-enabled cyber-security framework and algorithm for industrial IoT by adopting random initial and master key generation mechanisms over long-range low-power wireless networks for fast encrypted data processing and transmission. So, this paper has three remarkable contributions. First, a blockchain-driven secure, efficient, reliable, and sustainable algorithm is proposed. It can be said that the proposed solution manages keys randomly by introducing the chain of blocks with less power drain, a small number of cores, will slightly more communication and computation bits. Second, an analytic hierarchy process (AHP) based intelligent decision-making approach for the secure, concurrent, interoperable, sustainable, and reliable blockchain-driven IIoT system. AHP based solution helps the industry experts to select the more relevant and critical parameters such as (reliability in-line with a packet loss ratio), (convergence in mapping with delay), and (interoperability in association with throughput) for improving the yield of the product in the industry. Third, sustainable technology-oriented services are supporting to propose the novel cloud-enabled framework for the IIoT platform for regular monitoring of the products in the industry. Moreover, experimental results reveal that proposed approach is a potential candidate for the blockchain-driven IIoT system in terms of reliability, convergence, and interoperability with a strong foundation to predict the techniques and tools for the regulation of the adaptive system from Industry 4.0 aspect.
Full-text available
Although there are several special features in block-chain technology such as machine trust, traceability, and security, high energy consumption remains an issue in broadening the applications of block-chain technology. Some researchers proposed the use of proof of stake (PoS) mechanism rather than proof of work (PoW) mechanism to reduce energy consumption of block-chain. However, because PoS cannot guarantee fairness, mixed consensus mechanisms could be a solution and has been adopted in many studies. This paper aims to evaluate the performances of PoW, PoS and mixed consensus mechanisms from three aspects: energy consumption, fairness, and reliability. An agent-based model of a typical block-chain system equipped with different consensus mechanisms is created in NetLogo. This model simulates and evaluates the performances of different consensus mechanisms in the block-chain system.
Full-text available
We put forward the idea that classical blockchains and smart contracts are potentially useful primitives not only for classical cryptography, but for quantum cryptography as well. Abstractly, a smart contract is a functionality that allows parties to deposit funds, and release them upon fulfillment of algorithmically checkable conditions, and can thus be employed as a formal tool to enforce monetary incentives. In this work, we give the first example of the use of smart contracts in a quantum setting. We describe a simple hybrid classical-quantum payment system whose main ingredients are a classical blockchain capable of handling stateful smart contracts, and quantum lightning, a strengthening of public-key quantum money introduced by Zhandry [55]. Our hybrid payment system employs quantum states as banknotes and a classical blockchain to settle disputes and to keep track of the valid serial numbers. It has several desirable properties: it is decentralized, requiring no trust in any single entity; payments are as quick as quantum communication, regardless of the total number of users; when a quantum banknote is damaged or lost, the rightful owner can recover the lost value.
Compact blocks and compact block protocol are a recent addition to the Bitcoin (BTC) data propagation protocol that aims to reduce bandwidth requirements and, possibly, reduce latency as well. In this work we evaluate operation of BTC network under a mix of regular and compact block traffic, assuming that nodes randomly leave and re-join the network, and perform block and, optionally, transaction pool (mempool) synchronization upon returning. Our analysis begins by evaluating block and transaction deficits accumulated during the node absence and block synchronization. Then, we analyze mempool behavior and show that mempool synchronization is necessary since it decreases probability of transaction deficit and the need for transaction retrieval actions. Finally, we analyze the impact of synchronization activities and transaction deficit on data distribution times in the BTC network with high and low bandwidth distribution modes for a mix of compact and regular blocks. Results demonstrate resilience to node absence and subsequent synchronization, as well as substantial performance improvements for small protocol changes. Furthermore we show that the low bandwidth mode is more resilient to potential security attacks.
Six years after the introduction of selfish mining, its counterintuitive findings continue to create confusion. In this paper, we comprehensively address one particular source of misunderstandings, related to difficulty adjustments. We first present a novel, modified selfish mining strategy, called intermittent selfish mining, that, perplexingly, is more profitable than honest mining even when the attacker performs no selfish mining after a difficulty adjustment. Simulations show that even in the most conservative scenario (\(\gamma =0\)), an intermittent selfish miner above 37% hash power earns more coins per time unit than their fair share. We then broadly examine the profitability of selfish mining under several difficulty adjustment algorithms (DAAs) used in popular cryptocurrencies. We present a taxonomy of popular difficulty adjustment algorithms, quantify the effects of algorithmic choices on hash fluctuations, and show how resistant different DAA families are to selfish mining.