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Towards Blockchain-based Multi-Agent Robotic
Systems: Analysis, Classification and Applications
Ilya Afanasyev1, Alexander Kolotov1, Ruslan Rezin1, Konstantin Danilov1, Manuel Mazzara1
Subham Chakraborty1, Alexey Kashevnik2, Andrey Chechulin2, Aleksandr Kapitonov2
Vladimir Jotsov3, Andon Topalov4, Nikola Shakev4, Sevil Ahmed4
1Innopolis University, Innopolis, Russia
2ITMO University, St.Petersburg, Russia
3University of Library Studies and Information, Sofia, Bulgaria
4Technical University of Sofia, Branch Plovdiv, Plovdiv, Bulgaria
{i.afanasyev, a.kolotov, r.rezin, k.danilov, m.mazzara, s.chakraborty}@innopolis.ru
alexey@iias.spb.su, chechulin@comsec.spb.ru, kapitonov.aleksandr@itmo.ru
v.jotsov@unibit.bg, {topalov, shakev, sevil.ahmed}@tu-plovdiv.bg
Abstract—Decentralization, immutability and transparency
make of Blockchain one of the most innovative technology of
recent years. This paper presents an overview of solutions based
on Blockchain technology for multi-agent robotic systems, and
provide an analysis and classification of this emerging field. The
reasons for implementing Blockchain in a multi-robot network
may be to increase the interaction efficiency between agents
by providing more trusted information exchange, reaching a
consensus in trustless conditions, assessing robot productivity
or detecting performance problems, identifying intruders, al-
locating plans and tasks, deploying distributed solutions and
joint missions. Blockchain-based applications are discussed to
demonstrate how distributed ledger can be used to extend the
number of research platforms and libraries for multi-agent
robotic systems.
I. INTRODUCTION
The successes demonstrated in recent years in the inte-
gration of robotic systems, wireless sensor networks (WSN),
cloud computing, distributed planning and management, and
distributed ledgers provides and optimistic outlook towards
increasingly popular technological solutions such as the Inter-
net of Robotic Things (IoRT) [1], [2], [3], [4], [5] and the
Blockchain-based Multi-Agent Robotic Systems (MARS) [6],
[7], [8], [9]. It is known that one of the important problems
in developing multi-robot systems is the design of strategies
for their coordination in such a way that the robots could
effectively perform their operations and reasonably coordinate
the task allocation among themselves [10]. Real-world sce-
narios usually require the use of heterogeneous robots and the
performance of tasks with various structures, constraints and
complexity. The task distribution for decentralized solutions is
appropriate, since the use of autonomous multi-robot systems
in complex scenarios becomes limited and inefficient, and
centralized solutions pose a danger of failure for the entire
system. Since system agents have to share information, the
requirements for the quality of communication in decentralized
systems are increasing, including such important functions as
maintaining data integrity, resiliency and security in accessing
data. Therefore, implementation of blockchain technology for
interaction and coordination of multi-agent robotic systems
become a reasonable solution for a dynamic and decentralized
task distribution.
Studies of distributed ledgers demonstrate that decentral-
ization and immutable record technologies make blockchain
one of the most powerful innovations, since in a decentralized
network, intercepting most network nodes using cyberattacks
looks economically impractical [11]. However, many popular
blockchain solutions suffer from such issues as scalability,
delay and low throughput [11]. For example, Bitcoin can
process less than 10 TPS (transactions per second), while
Ethereum can process up to 40 TPS, which is clearly not
enough compared to daily 2000 TPS VISA (which theoret-
ically can increase up to 50,000 TPS). Since the blockchain
network grows with an increase in both the number of users
and transactions, a verification of transactions slows down
the transaction process and throughput. This is known as
the classical Blockchain Trilemma - when it comes to the
choice two of the three between decentralization, scalability
and security [12]. One of the scaling methods that does not
compromise security or decentralization is called sharding,
which involves fragmentation of the available dataset into
smaller datasets called shards [11], [12]. Although multi-agent
robotic systems (MARS) are not so critical to scalability and
speed as the financial and big data-based systems, they are
nevertheless also very sensitive to delays and throughput of
the information channels at data exchange between agents.
The literature describes many types of developed algorithms
for distributed consensus, each of which has distinctive fea-
tures, advantages and disadvantages. The summary of the most
important distributed consensus algorithms, including Proof
of Work (PoW), Proof of Stake (PoS), Proof of Activity
(PoAc), Proof of Burn (PoB), Proof of Capacity (PoC), etc.
are presented in the review [13]. The methodologies used to
achieve consensus in blockchain networks largely determine
key performance characteristics, including scalability, trans-
action speed, transaction completeness, security, and resource
consumption. Each method requires a procedure to generate
arXiv:1907.07433v1 [cs.RO] 17 Jul 2019
and then adopt a block. The block can be generated or
offered by a node in the network, and it encodes a number
of transactions (for example, in cryptocurrency, it can be
monetary transactions between accounts). Further, a key step
is the adoption of the proposed block/related transactions
by network participants, a process called consensus building.
Once a block is accepted, it becomes part of the block chain,
and the newly created blocks are cryptographically linked to
it. After some time (depending on the consensus algorithm
used), the block becomes a constant part of the blockchain,
i.e. reaching a finality. However, the finality does not exclude
the existence of a small statistical probability that a block
can be changed (intentionally by design or due to an attack),
although with each new block added, and for an established
blockchain system, it becomes negligible.
Let’s summarize the key points of the blockchain technol-
ogy in relation to the work of multi-agent robotic systems [9]:
•The blockchain-based database is a database for adding
only. Once the data is included in the database, they
cannot be changed. The database forms the blockchain
state and is distributed among the nodes.
•Each node is an agent on the blockchain network and
stores a complete copy of the database. The node is
responsible for transferring all incoming data received
from another node to all its neighboring nodes and could
generate records for changing the blockchain state. All
nodes are connected through peer-to-peer communication
channels. Some nodes should play the role of a validator.
•Validators check the correctness of records for changing
the state of the blockchain and approve them (for exam-
ple, combining the records into blocks, linking the blocks
together and sending new blocks to the neighbors). Only
verified entries are applied to all nodes to build the current
state of the blockchain.
Let’s emphasize the advantages of the blockchain technol-
ogy for multi-agent systems [9]:
•Data availability is achieved through multiply duplication
of data and communication.
•Consistency of data is achieved through data validation
and strict rules of changes appliance.
•No way to remove or change the data stored in the
blockchain.
•Economic or reputational incentive forces nodes to not
violate the validation rules.
The research relevance is based on the importance in
the development of distributed multi-agent robotic systems
that could effectively perform different operations and inde-
pendently coordinate the task allocation within the system.
Information exchange during the interaction of robots has the
particular importance for reaching the goals of a multi-agent
system in conditions of uncertainty, external interference,
environmental changes or the presence of intruders when data
integrity maintenance, resiliency and security have special
value. To this end, the blockchain technology for multi-
agent robotic systems is designed to solve the problems of
information exchange for a group of robots, provide a record
of the interaction history and validate the task execution,
enhancing the efficiency of the whole system and extending
the capabilities of MARS applications.
This paper extends the study presented by the authors
in the paper [9] with new materials related to blockchain-
based multi-agent systems, including aspects of implementing
MARS via Wireless Sensor Network (WSN), ensuring the
integrity and security using distibuted ledgers, and deployment
prospects for these systems in Smart Buildings, Smart Cities
and Industry 4.0.
The continuation of this paper is structured as follows.
Section II introduces the present state of scientific and en-
gineering development in the blockchain-based multi-agent
systems. Section III describes and classifies the most typical
cases, which we identified for blockchain-based robotics ap-
plications. Section IV considers aspects of MARS realization
using Wireless Sensor Network. Section V discusses multi-
agent robotic systems related to Smart Buildings, Smart Cities
and Industry 4.0. Finally, we summarize the strong and weak
sides of the blockchain-based MARS and discuss the barriers
that technology must overcome in order to prove its viability
and become mass in the Section VI.
II. RE LATE D PAPE RS
In this section, we analyze the state-of-the-art publications
and applications where blockchain technology is used for
distributed multi-agent systems with the special focusing on
robotics. A recent research series focuses on the use of
Blockchain technology for the shared knowledge and repu-
tation management system in studying the collective behavior
of robots [6], [7], [8], [14], [15], [16], [17].
The study [14] presented a trust management model for
decentralized robotic networks that focuses on access con-
trol and reputation management for each node. This model
provides group access based on a robot-oriented trust that is
selected and dynamically updated over time. The analysis of
the system was carried out by compromising a robot using
attacks. The idea of using blockchain technology to solve
security problems in multi-robot systems were discussed in
[7], [15]. The author [7] states that combining peer-to-peer
networks with cryptographic algorithms allows reaching an
agreement by a group of agents (with the following recording
this agreement in a verifiable manner) without the need for
a controlling authority. He describes some blockchain-based
innovations that could provide a breakthrough in MARS
applications:
•New security models and methods to preserve data con-
fidentiality and robot’s entity validation;
•Design of distributed decision making and collaborative
missions using special transactions in the ledger that
allow robotic agents to vote and reach agreements;
•Development of blockchain ledgers for using different
robot’s parameters, corresponding to changing environ-
ments without any changes in their control algorithm,
allowing to increase the flexibility of robots without
increasing the complexity of MARS design;
•Creation of infrastructure for MARS to follow certain
legal norms and safety rules adopted for human society
that could even result in building new business models
for MARS operation.
In the paper [8], the theoretical concept of managing security
problems in multi-robot systems using blockchain technology
was reinforced by the implementation and proof-of-concept
for controlling Byzantine robots. The authors developed an
approach to using decentralized programs based on smart
contracts to create secure swarm coordination mechanisms,
as well as for identifying and eliminating Byzantine swarm
members through collective decision making.
The study [6] is based on the organization of the
blockchain protocol for multi-agent coordination and control
of unmanned aerial vehicles (UAVs). The paper [15] concerns
the consensus protocol of the blockchain, which uses an addi-
tional procedure for verifying the liability execution to prevent
payment transactions to questionable service providers. For
this purpose, the liability execution for agent-based service
providers in the decentralized trading market is verified by a
formal model checker. As the proof-of-concept, an application
was implemented, where a taxi was modeled with the subse-
quent delivery check at the end of the completed mission.
The article [18] proposes a modular architecture, combining
the RobotChain [19] framework as a decentralized ledger for
registering events with robots, smart contract technology for
managing robots and Oracle for processing any data types.
The modular architecture can be used in various contexts
(manufacturing, network or robot management, etc.) since it
is easy to integrate, adapt, maintain, and expand for new
domains. What is more, this architecture allows to refuse
from tokens to accelerate the validation process or replace
them by a reputation system for managing tasks and reaching
consensus, since the monetary value may not make sense for
private blockchain-based networks. The examples of robotic
applications can be:
•Task allocation between robot network;
•Information support of robots in operations (for example,
a robot cannot recognize objects, while others can);
•Assessing the robot productivity or detecting performance
problems;
•Voting consensus for swarm robotics.
To ensure the interaction of heterogeneous robots in the
cyber-physical space, an ontology can be used that describes
the knowledge and competencies of the robots in the system,
provides a quick exchange of information between coalition
members and smart contracts for the allocation of sensory,
computational, control and service tasks between intelligent
robots, embedded devices and information resources [16]. To
do this, the study [16] presents a methodology for creating
cyber-physical smart space with instructions how to create and
manage coalitions of intelligent robots using knowledge pro-
cessors and information stored in the blockchain. The similar
way, the paper [17] discusses the cyber-physical-social system,
which combines smart space technology and blockchain. The
interaction between mobile robots and humans is related on
ontology-based publication/subscription mechanism, where all
data exchange is controlled and key information is stored in
the blockchain network.
Intelligent cyber-physical systems can be implemented as
multi-agent systems with the ability to schedule tasks by
agents [20]. In such multi-agent systems, the protocol of plan
execution should lead to proper completion and optimization
of actions, inspite of their distributed execution. However, in
unreliable scenarios there is a probability that agents will not
follow the protocol due to failures or malicious reasons that
result in the plan failure. To prevent such situations, the plan
can be executed by agents through smart contracts, ensuring
that the task is performed even in an untrusted environment
[20]. Moreover, smart contracts can be automatically generated
from manufacturing plans, resulting in automation of the
entire system with seamless integration of agents into one
cyber-physical system [20]. In the similar way, self-sustaining
cyber-physical environments are formed in which all critical
aspects at both the cyber and physical levels are effectively
stimulated, coordinated and supported using blockchain-based
mechanisms and protocols for data storage, communication
and coordination [21]. Thus, the advantages of a properly
developed blockchain framework for organizing multi-agent
systems (which can be extended to both robotic and cyber-
physical systems) should meet the following requirements
[21]:
•Decentralization of data storage, which increases the
rigidness of the system;
•Scalability and ease of joining new agents;
•Participants reach a consensus in conditions without trust;
•Ability to maintain trust among initially unknown agents;
•Transparency and immutability;
•Agents remain fully autonomous, they fully control their
identity and private keys;
•Blockchain data is complete, consistent and accessible.
III. THE CL AS SI FIC ATIO N OF T HE BLOCKCHAIN-BA SE D
ROBOTICS APPLICATIONS
In this section, we classify blockchain-based robotic multi-
agent systems, which we revealed during the literature review.
Let’s consider and discuss our vision of possible robotic multi-
agent applications based on blockchain technology shown in
the Figure 1 (that is the extended version of the classification
presented in [9]).
A. Agent Tasks Assigned in Executable Code from Blockchain
Let’s consider a scenario in which there are multiple agents
(robots), without taking into account the hardware platform.
Any agents have a pre-installed control program, but they
are configured to receive external executable code (bytecode),
which is a set of commands in the package, to implement
operations, achieving the multi-agent system’s goal. The word
”bytecode” means here the platform-independent executable
Fig. 1: The classification of typical cases for using blockchain
technology in multi-agent systems in robotics applications
code that results in the same sequence of command execution
for any agents. Thus, at the moment of sending the bytecode,
there is no need to know the exact hardware platform of the
agent (robot), which must execute the commands specified in
the bytecode.
The blockchain could be used here to distribute such byte-
codes to the agents [9]:
- The system which generates the tasks should not be
connected to the agent directly. The peer-to-peer network is
used to deliver the message.
- A message will be delivered even if an agent is turned off.
- The agent is able to inform constantly about its state
changes (e.g. ”moved forward for 10 meters”, ”picked an
object”, etc.). The state is stored in the blockchain therefore
it could be recovered quickly in case of the agent was turned
off.
- The delayed bytecode execution could be scheduled.
- Two or more command sequences could be automatically
queued for execution by the agent.
B. Distributed Decision Making by a Time-Limited Voting
Distributed decision making (DDM) is the challenging task
in multi-agent robotic systems. There was proposed a solution
to use the blockchain in SWARM systems [7], in which it
was used the idea of sending cryptocoins as the prizes to some
addresses that must be achieved. However, the development of
blockchain technology provides new opportunities that are also
effective for solving this problem, for example, using smart
contracts in Ethereum. For this purpose, smart contract(s)
should be developed to create an infrastructure by conducting
polls with complex behaviors, such as time-limited voting or
vote delegation.
C. Distributed Decision Making for Tasks Assigned in Byte-
code
By combining the approaches (B) and (A), it is possible
to obtain another interesting solution, where the use of smart
contracts for agents may contain some tasks formulated in
bytecode. Other agents may vote for actions, resulting in
defining co-generated script that can be obtained from a smart
contract.
D. Action Validation to Exclude Intruders or Faulty Agents
Agents can be used to check each others actions, locations
or states. Let’s look at the swarm where agents execute
some actions to achieve the common goal. Periodically, agents
send telemetry information of sensor measurements and their
location based on odometry. Sometimes an agent may start
working incorrectly and send wrong data. Information ob-
tained from other agents can be used to reach a consensus
that the agent working wrong and the recovery procedure
can begin. In this case, co-evolution scenarios can be applied
further. A consensus based on information obtained from other
agents can also be used to identify a robot that is behaving
incorrectly, for example, it had been hacked or infected by
an attacker. To solve the performance problem of validators,
the Sharding approach can also be used here [11], [12]. Data
from agents are combined depending on their location: thus,
the separate shard is formed. Validators are coordinated for
the shard, therefore the information volume for processing is
significantly reduced.
E. Economic Incentive to Optimize Task Performance
The financial side of the blockchain can be used as a
basis for stimulating a multi-robot system. Thus, researchers
from Carnegie Mellon University performed multi-robot map-
ping with a market approach, coordinating a robot team
and maximizing the information acquisition at minimal cost
[22]. This approach demonstrated reliability and adaptability
to a dynamic environment, even with the loss of colony
members, in addition to its ability to withstand communication
losses and disruptions. The researchers found that, the market
architecture in the negotiations of robots, improved the robot
team efficiency for environmental study in many times [22].
Although the algorithm was designed to minimize the distance
traveled during the mission, minimizing the exploration time
also showed encouraging results for quick survey of the ter-
rain. This approach also allows dynamical changing priorities
to minimize the resource consumption for the robot team,
fulfilling the multiple mission objectives for the robot group
[22].
F. Automated Task Dispatching via Blockchain
The blockchain-based distributed consensus can be used to
dispatch, assign and execute tasks between competing agents.
In this case, the dispatching code can be written in the
form of a smart contract stored in the blockchain for the
implementation of the following possible scenario:
1) The client sends a request for the task execution to the
smart contract dispatcher.
2) The dispatcher notifies agents about the new request.
3) The agents agree the task performance in the blockchain
via a peer-to-peer network.
4) Blockchain validators determine the order of agreements
in accordance with the commission that a particular
agent pays for processing the agreement.
5) The first agreement received by the dispatcher is con-
firmed by the smart contract code, and the order details
are provided to the appropriate agent.
Thus, the market will regulate the choice of agents that will
be sent to complete the task. However, certain agents can use
strategies that allow them to pay more to be selected validators
that may lead to appearing the most efficient and stable service
providers. The case study on the creation of such a multi-agent
system is presented in the paper [15], which implements an
automated dispatching taxi script with validation of obligation
fulfillment by comparing the route traveled with a map by a
validator.
G. Authentication / Suitability Check
There are critical situations associated with the occurrence
of risks / threats that require authentication:
•When agents do not trust each other, but use a common
physical resource;
•When agents may be attacked by third parties. Thus,
hacker attacks can reveal confidential information and/or
location, change the task and influence on the agent’s
activity outcome.
In the paper [9], the example with a service station for elec-
tric vehicles (EV) battery replacement was considered, where
a blockchain-based solution was proposed to provide battery
authentication services. Since the smart contract code in the
blockchain is not changeable, therefore battery amortization
state is available to all participants in the deal. Therefore, the
service station and EV can connect to any blockchain nodes
to check the battery, excluding data replacement at the man-
in-the-middle attack.
The similar system was described in article [23], where
blockchain was used to store information about the battery life
cycle and solve the problem of its replacement for a transaction
without trust between the parties. Thus, the calculation of the
battery price and the exchange of digital currency between an
EV owner and the station, as well as the key logic, were im-
plemented using smart contracts to solve the problem of lack
of trust. Similarly, the study [24] introduced an autonomous
charging architecture and a billing framework for EV charging
based on IOTA technology that provides resistance to hacker
attacks and preserving confidential user information.
H. Ensuring the Integrity and Security based on Blockchain
The one of the key features of the multi-agent robotic
systems is the increased requirements to the security in
conditions of limited energy consumption of the devices,
high requirements to productivity, physical characteristics and
mobility of devices, their compatibility with each other. The
presence of both software and hardware/software components
interacting with each other in multi-agent robotic systems and
their environment as well as possible variability of cyber-
physical environment determine the susceptibility of such
systems to specific sets of attacks.
To meet today’s challenges, it is necessary to develop an
integrated approach for security [25] of robotic systems. The
comprehensiveness of the approach here means not only the
union of various security systems and it is also very important
to take into account the protection of the security system in
itself against the attacks. And here the blockchain can be very
effective solution because of its reliability.
There are three basic security concepts - confidentiality,
integrity, and availability. Cryptographic protocols, encrypted
storage, etc. are usually used to ensure the confidentiality. For
the availability support the intrusion detection and prevention
systems, backup communication links, etc. are usually used.
To ensure the integrity, checksum or digital signature are
widely used. However, the use of blockchain technology can
significantly improve the efficiency of ensuring the integrity
of stored and transmitted data.
Let’s consider the main areas of the distributed multi-agent
robotic systems where the blockchain can be used [7].
•Robot’s sensors [26];
•Robot’s storage [27];
•Robot’s architecture [28];
•Interaction of agent with neighboring agents by IoT
protocols [29];
•Interaction of agent with distant hosts or cloud through
Internet [30];
•Data aggregation, analysis and storage in the cloud [31].
As an example, to enhance the security of the multi-agent
robotic systems interaction, the new possible architecture of
internet - Named Data Networking (NDN) can be used as a
part of the system. The NDN is formed with two basic things,
i.e. Sending Request and Receiving the data packet. Regarding
the aspect of securities issues NDN can use approaches along
with the blockchain to protect itself against the various threats.
There are many types of attacks to be noted [32], e.g. Inter-
est Flooding, Cache Misappropriation, Data Fishing, Selfish
Attack etc. It is hard to avoid these security attacks by exist-
ing security solutions due to the decentralized and dynamic
characteristics of NDN. But the decentralized blockchain [33]
approach can be applied to meet the security requirement of
NDN. It can use the hash of Interest or Data by smart contract
and it will decrease the chance of user privacy leaking because
both data identifier and user identifier will be replaced with
temporary names. In addition, nobody will be able to change
or delete the fields due to the blockchain structure of hashed
chain blockchain [34].
It is already proven [35] that malicious actions can be
detected by NDN blockchain model and blockchain with smart
contract plays an efficient role in this scenario. The blockchain
maintains the trajectory of Interest and Data in a hashed
manner. The distributed blockchain for NDN contain series
of blocks, and each block contains a hashed transition set.
Each block has a head pointer (except the initiated block) that
linked to a previous block. It consists of a timestamp record the
time when the block is written, a bit that linked to a successor
block. In each block, it keeps a hash value that composed by
several hashed Interest or Data transition records.
IV. REA LI ZATION OF MULTI-AGE NT ROBOTIC SYSTEM
VIA WIRELESS SEN SO R NET WORK
Wireless sensor networks (WSN) are widely used for the
practical implementation of multi-agent systems, and the ad-
dition of mobile robots to the WSN structure is a well-
observed trend [36]. Robots that are active doers in a multi-
agent robotic system (MARS) can provide flexibility when
installing network sensors and realizing active data acquisition,
since they can perform various operations and interact with
the environment [36], [5]. Although these interactions can be
predetermined or based on real-time observations, however,
the choice of a suitable communication protocol for robo-
tized WSN can be a challenge, considering the complexity
and multi-components of robots, as well as the type of
communication implemented in MARS: one-to-many. One
possible solution is to use the HTTP protocol over a WiFi
connection, although it is not very suitable for bidirectional
communication due to such difficulties as specifying ports
and sometimes IP addresses for each network component,
large packet size, high power consumption, and transmission
problems for control commands via the Internet connection
[36]. The alternative solution is to use Cloud Computing
and the Internet of Things (IoT) technologies for organizing
communication between nodes and controlling the WSN robo-
tized components, especially when using the Message Queue
Telemetry Transport (MQTT) protocol. Due to small packets’
size and ”publish/subscribe” concept, managing the connection
between network devices can be simpler and more feasible.
Thus, at present 6LoWPAN networks with MQTT protocol
are becoming a good solution for MARS applications due
to low power consumption, IP-driven nodes and support for
large mesh networks. As an example, the studies [36],
[37] proposed a robotized WSN suitable for solving various
problems of environmental monitoring. The robotized WSN
is an adaptive system in which intelligent agents are moving
sensors for detecting and tracking areas, where the monitored
environment parameters are different from certain threshold
values. For the experiment, iRobot Create and KUKA youBot
mobile platforms were used with additionally installed single-
chip Gumstix Verdex pro TM XL6P computers and various
expansion modules as robotic agents (mobile network nodes).
The functionality of the proposed WSN robotic with data
exchange using 6LoWPAN is verified using the MQTTBox
platform. It enables building MQTT clients for publishing or
subscribing topics, configuring MQTT virtual device networks,
testing MQTT devices etc.
However, security becomes a critical issue when applying
IoT concept to organize communication between the robotized
WSN nodes. A successful solution of the problem of interac-
tion between the nodes, together with recording the interaction
history and performing the verification task can be provided
by the blockchain technology (see, the Section III H). This
can increase the efficiency of the robotized WSN and expand
the possibilities of their applications.
The notion of a blockchain is associated with a publicly
available chronological database of transactions recorded by a
network of agents (e.g. swarm of agents). It is obvious that
software robots (holons) are suitable for this purpose and such
a research with robotic swarms had been executed [7]. Each
agent possesses private and public keys used to prove the
origin and/or encrypt messages. As stated in [7], the proof
may delay the task execution up to 10 minutes. In order to
improve this situation, it is reasonable to use other cryptocur-
rencies instead of Bitcoin, for example, based on IOTA or
Ethereum. On the other hand, not all transactions should be
included in blocks. In many cases, different preprocessing
schemes should be applied to deeply model the environment
and classify the situation [38]. Such an example is shown
in Figure 2, where six security-based ontologies have been
depicted and their combination defines dangerous processing
nodes (in red), warning zones (in yellow), and safe (uncolored)
zones. Blockchain-based solutions are required only in red and
sometimes in yellow zones. The information preprocessing
allows not only increasing the efficiency of the system, but
also reducing its vulnerability. Other applications for modeling
and data processing, also suitable for blockchain agents, are
discussed in [38].
Fig. 2: An ontology-related preprocessing scheme with six
security-based ontologies and their combinations that identify
dangerous processing nodes (red), warning zones (yellow), and
safe (uncolored) zones
V. SM ART BUILDINGS, SMA RT CIT IE S AN D IND US TRY 4.0
The increasing integration of devices and robots with the
Internet infrastructure and everyday activities is leading to a
vision of the future where the Internet itself is progressively
disappearing from view, i.e. the conscious actions necessary
to connect to the network and transfer packets of data on it
are becoming more and more transparent. The future citizen
will not refer to the Internet as often as we do in the same
way we now simply connect devices in a stable manner very
differently from what we used to do two decades ago, when
more conscious effort was necessary and the connection could
be lost several times over a work session. The more the Internet
will enter our life, the less we will notice, the more pervasive it
will become in every aspect of personal and professional life.
For example, the use of a multi-agent system with auditable
blockchain voting helps to make the voting records transparent
and unchangeable, allowing to overcome the main e-voting
problem how to increase the level of respondents’ trust in the
electronic voting system [39].
This progressive integration is what today we call the Inter-
net of Things (IoT) [40], where every object is transparently
connected to the network and can communicate with other
objects, systems or individuals. In this developing scenario
robots also play a role, and robotics as a discipline cannot be
considered as a completely separate domain and independently
developing. The Internet of Robotics Things (IoRT) [1], [2],
[3], [4], [5] is a recently defined concept, which aims at
describing the integration of robotics technologies in IoT
scenarios. Multi-Agent Robotic Systems, as described in this
paper, is a notable application scenario for which IoRT can
constitute the working infrastructure [41], both for domestic
(domotics/Smart Buildings) and industrial (Industry 4.0) use.
The papers [42], [43] examine the integration of the
blockchain technology and the Internet of Things, which is
expected to transform human life and provide great economic
benefits. However, the main restrictions for such integration
are insufficient data security and a level of trust.
Regarding Smart Buildings and Software-defined Buildings
(SDB), blockchain is destined to represent a persistence in-
frastructure of pervasive application in everything concerning
home resource, environment and processes [44], from energy
management to billing, from environmental comfort to safety,
surveillance and further. Developing the idea of Smart Build-
ing system, researchers integrate subsystems, such as intelli-
gent networks, services, buildings and household appliances,
into models of Smart Spaces and even Smart Cities, using
blockchains to effectively exchange data when interacting
subsystems, connecting and remote control for reaching a
better life quality, sustainability, energy conservation and the
development of socio-economic systems [45], [44]. The IoRT
in the domotics context will be the enabling infrastructure
for blockchain-based Multi-Agent Robotic Systems. In this
context, and it also applies to Smart Cities as aggregation
of Smart Buildings, a large number of sensors collect data
with high variability of accuracy, reliability and frequency.
Therefore, the Blockchain technology could permit the man-
agement of public immutable ledgers tracking all the activities
and determining those that are more trustful and those that
are less, and act accordingly. This hardware and software
infrastructure will simplify the interaction of the different
agents in the different buildings allowing traceability of data
collection and enhancing trust, security and accuracy of the
cooperation between multiple agents.
Ecological and environmental monitoring is a sphere where
all advantages of blockchain are needed, and usage of IoT and
IoRT is a good way to create a big independent sensor network.
Transparency, immutability and security are highly demanded
for environmental monitoring. Peer-to-peer approach gives a
way of a cheap connection of the new sensor to the global
network and start provide a data about environment publicly.
Right now the concept of citizens’ observatories one of the
most suitable for blockchain technology. Such projects as
WeObserve [46] and WeSenseIt [47] demonstrate how citizens
sensor networks helping to improve fullness of the ecological
information, and with peering technologies it can be a really
scalable solution.
In the context of Industry 4.0 (Smart Factories) [48], and
with the increasing trend of automation, similar considerations
on the efficacy of IoRT and Blockchain hold. Blockchain
technology may eventually represent the pillar of a business or
organization thanks to better contract management, effective
quality control, better accountability, recognition and authen-
tication of IoRT devices. In general, blockchain technology
in Industry 4.0 gives us the chance to innovate and refresh
the concept of cybersecurity, offering a mechanism by which
activities can be immutably tracked and pseudonymized [49].
The progress in various technologies and their cooperation
for robotics, automation, IoT, big data processing, cloud com-
puting and blockchain lead to the fourth industrial revolution,
when the interaction of the Smart Factory components within
the company and external industrial IoT systems provide trust
and reliable control over the resource distribution and products
[50], [51]. For example, in logistics, where the supply chain is
a multi-agent system in which each supplier has own behavior
model and purposes, the blockchain can bring the necessary
transparency and trust, speeding up the supply processes and
eliminating many shortcomings of current supply chains [42],
[52].
VI. CONCLUSIONS AND DISCUSSION
Currently, the approach consisting in the organization of an
immutable distributed database storing all relevant informa-
tion and providing access to agents of a multi-agent robotic
system (therefore expanding the capabilities of the system as
a whole) is of great relevance in the context of the Fourth
Industrial Revolution. The key component of the approach
is a distributed ledger technology (blockchain), which allows
agents to interact or allocate the tasks through responsible
smart contracts. On the one hand, the reliability of the agent
is mainly determined by the reputational model, allowing to
determine the trust level to the agent only after the fulfillment
of the agreed obligations. On the other hand, the automation
of obligation fulfillment by an agent can provide a verification
procedure that will allow to verify the liability execution even
among initially unknown agents and reach a consensus in
conditions without trust ([7], [15], [21]). In addition, the goals
for the blockchain implementation in a multi-agent robotic sys-
tem may be the increase of the interaction efficiency between
agents by organizing more trusted information support, assess-
ing the robot productivity or detecting performance problems,
voting consensus for swarm robotics, plan scheduling and
task allocation, deploying distributed decision making and
collaborative missions.
The basic message of this paper is to bring together and
provide, as a guide, ideas on how frameworks, architectures
and structures supported by blockchain solutions can be used
to solve practical problems that face by multi-agent robotic
systems and cyber-physical systems. Relevant studies show
that the blockchain begins to play a large role in the develop-
ment of systems and applications with many agents (robots),
in which the development of strategies for their coordination
is conducted in such a way that agents can effectively perform
their operations and intelligently coordinate task allocation
[10]. The analysis of the recent publications allowed to identify
and classify groups of tasks for multi-agent robotic systems
based on blockchain technology. This classification is our
main contribution by this paper. Blockchain technologies can
be used to expand the existing number of platforms and
libraries used by researchers, or to motivate them to use
a common solution that is widely distributed and tested,
rather than trying to develop their own software solutions to
cover similar scenarios. Real-world scenarios may require the
use of disparate agents and the performance of tasks with
different structures, constraints and complexity. Therefore, the
requirements for the quality of communication in decentralized
systems are increasing, including such important functions as
maintaining resiliency, data integrity and security in accessing
data. Therefore, the introduction of blockchain technology for
the interaction and coordination of multi-agent robotic systems
becomes a reasonable solution for many modern research and
industrial tasks.
Based on modern investigations, the authors conclude that
at present one of the most promising tasks in the field
of developing multi-agent systems is the development of
methodologies, models, structures, architectures and meth-
ods, aiming to integrate blockchain technology with high
complexity systems, such as cyber-physical systems, robot
swarm, the Internet of things, the Internet of Robotic Things,
Smart Buildings, Smart Factories and Smart Cities. To fully
implement such integration, it is necessary to automate and
solve subtasks of different difficulty levels, such as intellectual
support for agent interaction, task and plan allocation, analysis
of task performance and liability execution, evaluation of agent
performance, identification of improperly functioning agents
and intruders, security-based issues and many others.
Many tasks remain to be done at the moment and require
solutions, but some of them, according to the authors, are the
most important and urgent [9]:
•Development of a conceptual model of information sup-
port for robot network during the task performance;
•Design of a typical ontological model of a multi-robot
network;
•Development of requirements and formal process model-
ing for liability execution [53], [15];
•Design of a consensus protocol for a group interaction
verification before launching a task based on the infor-
mation from a distributed ledger;
•Development of a validation methodology for task per-
formance by the robotic system;
•Design of multi-agent robotic system architecture;
•Automatic process reconfiguration for multi-robot sys-
tems based on real scenarios;
•Analysis of cybersecurity [54];
•Improvement of existing frameworks allowing for multi-
agent robotic networks to perform collaborative tasks
with regard to scalability, decentralization and security
requirements.
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