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Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

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Abstract and Figures

With an increasing penetration of ubiquitous connectivity, the amount of data describing the actions of end-users has been increasing dramatically, both within the domain of the Internet of Things (IoT) and other smart devices. This has led to more awareness of users in terms of protecting personal data. Within the IoT, there is a growing number of peer-to-peer (P2P) transactions, increasing the exposure to security vulnerabilities, and the risk of cyberattacks. Blockchain technology has been explored as middleware in P2P transactions, but existing solutions have mainly focused on providing a safe environment for data trade without considering potential changes in interaction topologies. we present EdgeBoT, a proof-of-concept smart contracts based platform for the IoT built on top of the ethereum blockchain. With the Blockchain of Things (BoT) at the edge of the network, EdgeBoT enables a wider variety of interaction topologies between nodes in the network and external services while guaranteeing ownership of data and end users’ privacy. in EdgeBoT, edge devices trade their data directly with third parties and without the need of intermediaries. This opens the door to new interaction modalities, in which data producers at the edge grant access to batches of their data to different third parties. Leveraging the immutability properties of blockchains, together with the distributed nature of smart contracts, data owners can audit and are aware of all transactions that have occurred with their data. we report initial results demonstrating the potential of EdgeBoT within the IoT. we show that integrating our solutions on top of existing IoT systems has a relatively small footprint in terms of computational resource usage, but a significant impact on the protection of data ownership and management of data trade.
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sensors
Article
Edge Computing to Secure IoT Data Ownership
and Trade with the Ethereum Blockchain
Anum Nawaz 1,2,3 , Jorge Peña Queralta 2, Jixin Guan 1, Muhammad Awais 3,
Tuan Nguyen Gia 2, Ali Kashif Bashir 4, Haibin Kan 1,5, * and Tomi Westerlund 2
1Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science,
Fudan University, Shanghai 200433, China; 18110720163@fudan.edu.cn (A.N.);
17210240095@fudan.edu.cn (J.G.)
2Turku Intelligent Embedded and Robotic Systems Group (TIERS), Faculty of Science and Engineering,
University of Turku, FI-20014 Turku, Finland; jopequ@utu.fi (J.P.Q.); tunggi@utu.fi (T.N.G.);
tovewe@utu.fi (T.W.)
3School of Information Science and Engineering, Fudan Univeristy, Shanghai 200433, China;
17110720061@fudan.edu.cn
4Department of Computing and Mathematics, Manchester Metropolitan University,
Manchester M15 6BH, UK; dr.alikashif.b@ieee.org
5Fudan-Zhongan Joint Laboratory of Blockchain and Information Security, Shanghai Engineering Research
Center of Blockchain, Shanghai 200433, China
*Correspondence: hbkan@fudan.edu.cn
Received: 30 May 2020; Accepted: 8 July 2020; Published: 16 July 2020

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Abstract:
With an increasing penetration of ubiquitous connectivity, the amount of data describing
the actions of end-users has been increasing dramatically, both within the domain of the Internet of
Things (IoT) and other smart devices. This has led to more awareness of users in terms of protecting
personal data. Within the IoT, there is a growing number of peer-to-peer (P2P) transactions, increasing
the exposure to security vulnerabilities, and the risk of cyberattacks. Blockchain technology has been
explored as middleware in P2P transactions, but existing solutions have mainly focused on providing
a safe environment for data trade without considering potential changes in interaction topologies.
we present EdgeBoT, a proof-of-concept smart contracts based platform for the IoT built on top of
the ethereum blockchain. With the Blockchain of Things (BoT) at the edge of the network, EdgeBoT
enables a wider variety of interaction topologies between nodes in the network and external services
while guaranteeing ownership of data and end users’ privacy. in EdgeBoT, edge devices trade
their data directly with third parties and without the need of intermediaries. This opens the door
to new interaction modalities, in which data producers at the edge grant access to batches of their
data to different third parties. Leveraging the immutability properties of blockchains, together with
the distributed nature of smart contracts, data owners can audit and are aware of all transactions
that have occurred with their data. we report initial results demonstrating the potential of EdgeBoT
within the IoT. we show that integrating our solutions on top of existing IoT systems has a relatively
small footprint in terms of computational resource usage, but a significant impact on the protection
of data ownership and management of data trade.
Keywords: IoT; blockchain; edge computing; data ownership; data trade; information security
1. Introduction
A wider adoption of the Internet of Things (IoT) devices and systems across different industries
and domains come together with an exponential increase in the amount of real-time data being
generated and processed) [
1
]. From the point of view of data privacy and trade between devices at
Sensors 2020,20, 3965; doi:10.3390/s20143965 www.mdpi.com/journal/sensors
Sensors 2020,20, 3965 2 of 17
the edge acquiring the data and cloud-based third-parties processing it, multiple challenges remain [
2
].
While the IoT emerged with the adoption of cloud technology, in recent years more distributed
approaches are being adopted and new paradigms are emerging [
3
]. Among these tendencies,
the edge computing paradigm has materialized as those architectures that shift most of the data
processing closer to where it is being generated, at the edge of the network. This shift and optimization
in computation also lead to better network usage and significant reductions in the network load.
However, the change in network topologies also comes with a new set of data privacy and security
implications that must be considered [
4
]. Blockchain technology provides inherent security, proof of
ownership, and identification, hence being a potential solution for many of the cybersecurity challenges
in the IoT [
5
]. By taking advantage of the built-in consensus mechanisms, IoT systems can rely on
blockchain for managing data integrity and immutability [6].
A recent trend in the IoT is to distribute the data processing and analysis through a series of
network layers, extending a traditional cloud-centric architecture [
7
]. By shifting the computation
load towards the edge of the network, applications can benefit from advantages, including lower
latency and more optimized network load [
8
]. Furthermore, in many applications, raw data do not
need to be stored and only the result of its analysis is kept [
9
]. Therefore, it is no longer the best
strategy to continuously transmit real-time data to the cloud. In turn, embracing the edge computing
paradigm reduces the possibility of data privacy violations [
10
], but it also raises additional security
considerations, including reliability of information and authenticity of the data sources. In some
scenarios, such as health-care IoT, edge computing can be leveraged for increasing the security of
personal data [
11
]. Nonetheless, the addition of intermediate layers between IoT devices, cloud
servers, and end-users applications increases the exposure to security flaws, injection of malicious
code, and cyberattacks [12].
Over the past decade, an amalgamate of IoT products, solutions, and systems have been
penetrating both industrial and domestic domains, which has led to a growing number and variety
of security vulnerabilities and cyberattacks [
13
]. Proper security methods gain special significance
in environments where personal data is being gathered, such as smart homes [
14
]. In particular, voice
assistants are becoming increasingly accessible in the consumer electronics market, creating a direct
risk to end-users privacy [15]. The utilization of current encryption methods is not enough to protect
users’ data and privacy [
16
]. Therefore, the need for a more robust solution for sharing and trading
personal data securely and safeguarding privacy is evident.
Since the introduction of Bitcoin in 2008, blockchain technology has become increasingly utilized
in various domains, mostly for finance-related applications, or as an immutable distributed data storage
solution [
17
]. it was with the introduction of ethereum and the ability of running short programs
within the blockchain, when a vast number of applications emerged, in particular in the IoT field [
18
].
With proof of ownership and distributed data transactions, blockchain technology provides a natural
channel for trade between data producers or sellers (edge devices) and data consumers or buyers
(i.e., third-party applications) [
19
]. Current works integrating blockchain for the IoT focuses on securing
individual transactions between applications and edge devices [
20
], or among devices [
21
]. We believe
that its potential at a system level for integrating edge devices as data producers and applications or
third-party services as data consumers is yet to be explored in more detail. Therefore, we put a focus
on the problem of data ownership, enabling the integration, validation, control, and audit of third
party access to IoT data.
This paper introduces EdgeBoT, an IoT system architecture exploiting edge computing for data
processing and the ethereum blockchain as a distributed middleware. EdgeBoT enables direct peer-to-peer
data transactions in the Blockchain of Things (BoT). Our proposed model transfer the data trading
rights directly to their producers (edge computing devices), which rely on smart gateways to run
the blockchain. In particular, we are interested in the ethereum blockchain because of the possibility
to run smart contracts, scripts that are validated as part of a transaction in the blockchain.
Smart contracts are written in a scripting language designed as part of ethereum and executed
Sensors 2020,20, 3965 3 of 17
and validated in a decentralized manner validation of [
22
]. We leverage the inherent secure nature of
these contracts to secure and control third-party access to IoT data. Furthermore, the immutability of
a blockchain transactions’ records permits the audit of past transactions by any node in the network.
The rest of this paper is organized, as follows. Section 2explores related works in the use of
blockchain technology in IoT platforms. Section 3introduces the EdgeBoT architecture and presents its
advantages for secure integration of third-party services with IoT devices. In Section 4, Experimental
data, results, and validation of EdgeBoT are presented in Section. Finally, Section 5concludes the work
and describes future research directions.
2. Related Work
Although considerable research has been conducted on security and privacy solutions for IoT
devices [
14
,
23
25
], edge-based solutions for data security were also presented frequently in recent
years [
26
28
], and specifically distributed blockchain-based solutions for data privacy. Since its early
development, blockchain technology has been seen as a candidate to solve many of the security
challenges that centralized data exchange models inherently have, due to its decentralized nature
and full end-to-end encryption scheme. Table 1lists out major challenges and problems in the existing
centralized infrastructure and their proposed solutions based on blockchain [
18
,
29
,
30
]. In this section,
we review and summarize previous works on exploiting blockchain-based solutions to increment data
privacy, data ownership, and secure P2P transfer data within the IoT.
Table 1.
Major challenges and problems in existing centralized infrastructure and description of how
blockchain aids in overcoming these challenges. The challenges are summarized from the related
works listed in this paper, mainly from [18,29,30].
EXISTING CHALLENGES PROPOSED APPROACH
CHALLENGE: Data ownership
Existing client-server models might
use personal information without
owner’s consent and knowledge
in the blockchain model, every node can work as a server and does
not need to rely on service providers to store personal information.
Besides, data cannot be extracted without the network’s knowledge.
CHALLENGE: Third party access
Vulnerabilities in client server models
makes it easy for the attacker to get
access of sensitive information
in distributed structure of blockchain, data is distributed over
a number of nodes, which removes the threat of data access
via single entry point. Adversaries can get a portion of data
that are cryptographically hashed and meaningless. Validating
all transactions with the consensus of network nodes eliminates
the need of third parties and records of transactions processed
by the network be tampered with.
CHALLENGE: Unauthorized access
a vulnerability in a third-party service
can grant access to all personal data
beyond what the third party is storing.
Blockchains’ distributed structure and their strong cryptographic
algorithms reduces the risk of unauthorized access to private data.
An attacker with access to a validated third party can only access
data where access was previously granted.
CHALLENGE: Cost
A drastic increase in the amount of IoT
devices connected to cloud increased
the need of computational capabilities
the distributed structure of the blockchain model and edge based
computing architectures reduces the computational burden of
individual nodes, including external cloud services.
CHALLENGE: Data Manipulation
Data manipulation risk is high for IoT
external storages.
the blockchain model provides resilience to data compromise, as
it cannot be forged because the information in the mined blocks
is not allowed to alter.
CHALLENGE: Server unavailability
Centralized architectures that are
based on cloud servers can fail
if the connection to the server
is broken.
the peer to peer nature of the blockchain-based transactions ensures
that, if a connection between two nodes exists, then data can
be transmitted independently of the availability of other nodes.
Sensors 2020,20, 3965 4 of 17
In [
31
], Li et al. propose an efficient and secure mobile healthcare system, Edgecare,
which provides a hierarchical distributed architecture to manage and guarantee healthcare data
privacy by leveraging edge computing with stackelberg game-based optimization algorithm to achieve
fair data trading. In another study [
32
], Lin et al. present Edge-AI enabled architecture to make
sensory data trade-able as a knowledge. Authors work on a proof-of-trade consensus mechanism
and non-cooperative game based optimum knowledge approach to build a knowledge market.
In a recent review by Krittanawong et al. [
33
] summarizes the potential opportunities and challenges
by integrating blockchain with AI to develop personalized medicine for cardiovascular, according
to their review this combination can work as a booster for reliable data availability needed
for personalized medicines, but still, it is needed to consider the privacy of patients and data producers.
Debe et al. [
34
] proposed an IoT based decentralized trust model that consists of fog nodes to get
the reputation of publicly available fog nodes. Trust level is retained by using the feedback from existing
users and interactions, by using this approach reputation management system will become more
reliable and transparent as compared to existing third party based systems. In another recent research
work [
35
], Mazzei et al. proposed and implemented a trustless industrial solution that acts as a bridge
between IoT solutions and the virtual world of digital twins. This proposed blockchain-based solution
provides a standalone interoperable tracking system for industrial applications. When compared
to this and other solutions available, our proposed architecture is so far generic, and we have focused
towards discussing a system-level view rather than on specific integrations, which will be the objective
of our future works.
Yuan et al. [
36
] designed and implemented an emission trading system that is based on
hyperledger, another open-source permissioned blockchain-based distributed system. This system
can be leveraged for increasing credibility in trading services for polluters by sharing immutable
transactions in a chain. In another study by Rehman et al. [
37
], a quite similar approach is used
for sharing economy services. Cognitive edge framework that is based on blockchain technology
is proposed to secure smart city services by using smart contracts and off-chain nodes to store
immutable records. AI is used to extract informative information from existing records for training
datasets need to use for smart contract logic. In addition to academia, Popov et al. [
38
] proposed
iota, a distributed ledger for commercial IoT products and services. When compared to a traditional
blockchain architecture, iota replaces the chain for the tangle, a data structure based on a directed
acyclic graph that enables better scalability and lower latency for transaction confirmation [
39
]. Iota
solves the main challenge preventing micro-payments being more widely adopted with blockchain
in the IoT: the fact that the cost of processing a transaction is higher than the amount of currency
involved in the transaction itself. In iota, there are no transaction fees. This is possible, because, in order
to make a transaction, nodes need to validate two other transactions. This scenario is quite relevant
to the approach we implement in our model. In EdgeBoT, a node does not need to compute existing
transactions to save new transactions in the chain while in iota, the node first needs to participate
in the validation of two other transactions.
In a comprehensive survey, Yang et al. [
40
] addressed significant challenges, including security
and privacy, self organization, functions integration, scalability, and resource management of integrated
blockchain based edge systems. The authors also proposed a comprehensive classification of state
of the art solutions by providing use-case based scenarios and attempts to explore the technologies
working near the blockchain-based edge computing domain. Cha et al. [
41
] proposed a robust digital
signature scheme for blockchain connected gateway to maintain user privacy preferences for IoT
devices securely. Bergquist et al. [
42
] proposed and implemented blockchain technology and smart
contracts as means of building privacy-sensitive applications. The authors used medication plans as
a primary use-case but they proposed that results can be shifted to other applications where sensitive
data are being shared and a proof of legitimacy or authentication is required.
Another significant challenge of edge computing based on blockchain is scalability. Instead of
chain models, researchers come up with different solutions. Poon et al. [
43
] come with a scalable
Sensors 2020,20, 3965 5 of 17
decentralized autonomous blockchain solution Plasma based on edge computing, which can update
billion state updates within a second. They propose blockchain consortium in a MapReduce
format, which allows for child chains (sidechains) over a hierarchical tree to come up with
the scalability problem. It allows efficient matching of hierarchical ledger topology and provides fixed
withdrawal delays to increase its scalability by minimizing the damage. It works in asynchronous mode
during transaction handling. A similar approach was proposed by the Zilliqa team [
44
], where they use
the concept of sharding to cope with scalability issues. In this work, they divided the load into smaller
shards that can process parallel transactions. Along with sharding, a new scripting language for special
propose smart contracts and execution environment as an under-laying architecture was proposed.
Which makes a considerable difference in a computation platform for high scale parallel transactions.
Another similar work is done by Sompolinsky et al. [
45
], who proposed a tree-based distributed ledger
instead of chain framework. They focused on network delay effects, which lead to double-spend
attacks. The GHOST protocol is introduced by modifying the bitcoin protocol, which increases security
by reducing the confirmation time of transaction.
Most of the existing applications were focused on securing connectivity between end-users
and specific IoT devices, routing connections through a blockchain-based network. However, to the
extent of the authors’ knowledge, little to no attention has been put in exploiting blockchain and smart
contracts to provide P2P data trade and data ownership rights for data-sensitive environments
to its’ producers. Therefore, we propose an EdgeBoT platform that adapts to a wide variety of
M2M interaction topologies. At the same time, it is scalable and takes into account the integration
of both smart gateways and end-devices at the edge of the network, with a wide range of
computing capabilities.
3. EdgeBoT: P2P Data Trade Architecture
Transactions that involve data exchanges strongly rely on third-parties that act as intermediaries.
Traditionally, data acquired by end-devices has been analyzed, aggregated and stored by cloud
services. Third-party cloud services, in turn, act as a bridge between data producers and consumers.
The strong dependency of the digital world on the third-party cloud services opens the door to a wide
variety of vulnerabilities in terms of security and data privacy protection. More recently, the rising
paradigm of fog/edge computing has broadened the network stack by adding extra layers between
end-devices and cloud services or other third parties. These layers, which are closer to both data
consumers and producers, are often less secure than cloud services and increase the number of possible
vulnerabilities in the overall system [
46
]. While blockchain was originally designed for data storage
security, immutability, and auditability, it also has the advantage of allowing end-users or devices
to exchange digital assets directly without any intermediate third parties involved in the process [
29
,
47
].
EdgeBoT is a network architecture that consists of manager nodes (arbitrator, regulatory
authorities, handlers), edge gateways, sensors, and actuators, as well as end-user applications (data
buyers). The overall system divides into the following layers:
1.
Sensor layer includes individual sensors, actuators or other light nodes which do not possess any
computational or storage capabilities to participate in a network for data processing and trade.
2.
Edge layer consists of a local network (BLE, radio access points) and single-board
computers (SBC).
3.
Fog layer consists of manager nodes which work as an arbitrator, regulatory authorities,
transaction and data handlers for the Ethereum blockchain.
4. Cloud layer provides a place for applications and storage.
Figure 1illustrates the architecture of the deployed EdgeBoT network, with P2P distributed
communication and data exchange. Apart from cloud services and applications running on other
powerful computing platforms, manager nodes at the Fog layer represent the bulk of the network
nodes that work independently in a fully autonomous way (pre-defined smart contracts or scripts).
Sensors 2020,20, 3965 6 of 17
These require higher computational capabilities to handle a large number of parallel operations.
The Fog layer is directly connected to the Cloud layer providing the final step in forming the final link
between the Sensor and Cloud layers. The Fog layer is responsible to initialize a network, the System
initialization process in the sequence diagram that is shown in Figure 2. The Fog layer is also fully
responsible for running the Ethereum network and creating smart contracts. Therefore, edge gateways
at the Edge layer first request to join the private Ethereum network (the New device registration block
in Figure 2) to obtain the cryptographic keys generated at the Fog layer. By private we do not mean
a consortium blockchain where certain nodes have higher authority, but only that new nodes will
need to request access to a single manager for joining the blockchain (after which the manager is just
a normal node). Edge gateways can connect and handle several sensors and actuators located at
the Sensor layer. Sensors are responsible to gather data and send them to the upper layer for processing
and storage, the Data processing, encryption and storage block Figure 2. Whenever a new node
wants to buy or sell data in the EdgeBoT network, it first needs to register in the network, the New
buyer registration block in Figure 2. During this operation, a child key derivation (CKD) function
generates both private-public keys for the node. At that point, the buyer node becomes a full member
of the network by creating its public key derived from network key and has access to the encrypted
history of transactions and all previous data batches that have been generated since the establishment
of the platform.
3.1. Data Processing and Aggregation at the Edge
Edge gateways are smart gateways at the edge of every sidechain and work with local networks only.
Edge gateways consist of SBC’s, such as Raspberry Pi boards or Intel UP boards, which have enough
computational capabilities to run AI algorithms that are designed for resource-constrained devices.
The sensor layer consists of several actuators or sensors which do not possess any computational
capabilities to participate directly in the blockchain network. Therefore, sensor nodes rely on the edge
gateway that they are connected to, which acts as an intermediary for them. These edge gateways
run sidechains to store data hashes of their previous data batches and transaction validation records.
Complete flow of generation of data batch from the data segment, its processing at edge gateways,
and storage on cloud layer are described in the data processing, encryption, and storage block of
sequence diagram shown in Figure 2.
Manager Node
(Handlers)
Manager Node
(Arbitrator)
Manager Node
(Regulatory
Authorities)
Sensor Layer
Data creation
Edge Layer
AI implementation
Information creation
Discard raw data
Cloud Layer
Data storage
Knowledge base systems
Application: Buyer
Third party services
Fog Layer
Blockchain as a service
Validator Nodes
Data Trade
Figure 1.
Architecture of EdgeBoT, a distributed P2P data trade and fair access network model based
on the ethereum platform.
Sensors 2020,20, 3965 7 of 17
SensorLayer
After time T
save new Block
EdgeLayer CloudLayer:
Storage
CloudLayer:
Application
Search encrypted
Data
RequestBlockData( BlockID,PublicKey
Ack.byassigningpk
SendBlockHeader
4.3:Sentpaymentrequirements
2:Generationof
encryptionkeys After time T
save new Block
3.2:Dataprocessing
(e.g.withEdge-AI)
1.2:Requestvalidation
3.3:Broadcastmetadata
andbatchheader
FogLayer
1.3:Requestvalidation
3:Datageneration
3.1:Senddatasegment
Accessgranted
4.1:Buyerrequestdatabatch/es
4.2:Lookup
batch
After time T
save new Block
4.5:Validatetransaction
SendBlockHeader
4.4:decision+transactionfeereceived
5.Encryptedsk+addresssent
1.2:Requesttoaccessprivateethereum
1.1:Implement
smartcontracts
1:System
initialization
Ack.byassigningpk
Transaction
initialization
SendBlockHeader
5.1Downloaddatabatch
Data
processing,
encryptionand
storage
Newdevice
registration
Newbuyer
registration
3.3:Sendencrypteddatabatchforstorage
Transaction
Confirmtion
Figure 2. System sequence diagram depicting sequential picture of complete network.
Sensor nodes are connected to one edge node only, which minimizes the vulnerable channels
in which data could be compromised. If one sensor node is compromised, an adversary can only affect that
particular edge node. Therefore, single encrypted connection significantly increases the levels of data
security, ensures fair access to data and proper ownership, adding the sensor nodes to the backbone
chain of the EdgeBoT network. While the manager nodes at the Fog layer can communicate with any
other manager node in the network in a completely autonomous way, without any external supervision
to its actions or reactions. These nodes can also be used as a custom access control model.
3.2. Data Trade through Ethereum
The backbone of EdgeBoT is the Ethereum blockchain, which records all transactions that occur
within the network by using a time-stamp system and cryptographic hashes to prevent alteration
retroactively [
48
]. By using smart contracts, all of the specifications of any particular user, such as
limited access time to a network, can be defined, ensured, and recorded.
The complete sequence of data transaction in EdgeBoT is illustrated in the transaction initialization
block in Figure 2. Whenever an external buyer (a cloud layer application) wants to acquire data, it joins
the network and requests a given data batch while using batch headers that are available publicly on
the network. A manager node at the Fog layer replies with the transaction price and conditions.
If the buyer accepts the conditions, the manager node validates the transaction and sends the unique
secret encryption key (derived from CKD function) of the requested data batch by encrypting it with
Sensors 2020,20, 3965 8 of 17
ECDSA, using the buyer’s public key along with the address of the data batch. The buyer downloads
the encrypted data batch from cloud layer (storage) and decrypts it using its public key. After getting
the required data, possible scenarios occur which are shown in Figure 3. In a normal case, a buyer
is satisfied with the received data. In a dispute case, a buyer is dissatisfied with the received data,
or even unable to download or decrypt the data. In this case, the buyer can request for a refund.
After getting the refund application, a manager node verifies the request by downloading the required
data batch. If the buyer ’s request is legitimate, the refund request will be processed. If the buyer was
wrong, the manager node sends the encrypted sk again, which finalize the transaction request.
Figure 3. Sequential diagram to elaborate possible scenarios to settle down transaction request.
3.3. Security Measures
The distributed structure of edge/fog computing brings security and privacy challenges
for the system involve heterogeneous edge nodes. Several vital security challenges have been identified
in [
49
51
]. Distributed Denial of service (DDoS) and Man in the Middle (MITM) different security
measure have been taken into account to overcome these reported attack vulnerabilities like Sybil
attacks, which is explained in Table 2. DDoS and Time-delay attacks are handled in our proposed
system by limiting the number of requests by the edge node to the sensor node and vice-versa. By using
different encryption schemes and system elements, EdgeBoT is resilient enough for network and data
security attacks. Elliptic curve integrated encryption scheme (ECIES) [
52
] has been used to save data
securely by using the child key derivation function (CKD) [
53
] for every data batch, the elliptic curve
digital signature algorithm (ECDSA) [
54
] is used to share the unique key of data and communication.
To increase security, double authentication of nodes is required after three frequent requests per
minute by the same buyer. When a buyer requests data from a manager node that is stored encrypted
in a third-party service, i.e., cloud storage. The Manager node sends the encryption key of the requested
data batch with its storage location on the cloud is to the buyer instead of sending the requested data
batch itself. This scheme is chosen to save bandwidth and computational resources in edge nodes,
as well as avoiding storage limitations at the edge. To transfer child secret key csk, in a secure way
Sensors 2020,20, 3965 9 of 17
to the buyer, asymmetric encryption is used. Suppose that
cskN
is the key to the requested data batch
N
of data. Subsequently, the node encrypts
cskN
with the buyer’s public key, which will be, in turn,
decrypted at the receiving end with the buyer’s secret key. Information regarding the location of
the stored data is also included in the encrypted payload, together with
cskN
, if that information is not
known by the buyer beforehand.
Table 2. Security Measures.
Parameter Implementation
Authorization Public key cryptography to encrypt CSK
Confidentiality
Public key cryptography and proof of authority
Integrity Broadcast hash of each data_batch
Availability Achieved by limiting number of requests
Anonymity Discard raw data and store only processed information
ECIES is used in key encapsulation mechanism which combined with a data encapsulation
mechanism, to encrypt data batch by using the public key of edge node and send encrypted
data to managerial nodes. As compared to the more widely used Rivest–Shamir–Adleman (RSA)
cryptosystem, elliptic curve cryptography (ECC) requires shorter keys to provide the same levels
of security. At the same time, ECC usually requires lower computational and memory resources,
which makes it stand strong for scarce computing devices. It is used in practice to establish a key
in situations where data transfer is unidirectional or data is stored encrypted under the public key.
CKD functions are used by hierarchical deterministic wallets to derive children keys from
parent keys. This methodology is employed to generate a unique secret key for each data batch
in the device to be encrypted. Based on the parent’s public key (private and public keys are both
256 bits), its chain code and the desired child index, and 512-bit hash is generated. The one-way
hash function used in the process makes it impossible to obtain the original parent key from
the nth-child key. The additions modulo n in the process generate seemingly random numbers.
ECDSA is the first successful standard algorithm based on elliptic curve cryptography,
which gained the attention of security researchers due to its robust mathematical structure
and smaller key sizes for constrained resource devices. it is used in the communication of buyers
and sellers as well as to send unique child secret of required data batch.
Advanced encryption scheme (AES) is used in EdgeBoT to securely store private data, cloud
services or other third-party storage solutions. To store the data in a secure, encrypted way is of
particular importance to edge nodes or those nodes in the network that do not have enough resources
for local storage. AES encryption is chosen due to its high speed and less number of bits required
for the encrypted payload as compared to other data encryption standards, like DES. Besides,
most newly developed micro-controllers provide hardware modules for AES encryption.
4. Implementation and Results
To test the feasibility and reliability of the EdgeBoT architecture in real-life scenarios, the proposed
architecture is implemented using the Raspberry Pi 3 Model B+ minicomputer as an edge gateways.
For the implementation of the different parts of the system, the Go programming language (golang),
Solidity for the smart contracts, and a suite of web technologies (Node.js
R
, HTML5, CSS3, jQuery)
for the front-end application are used. We run Raspbian based on Linux kernel version 4.14.52-v7+
in the Raspberry Pi, which has 1GB of RAM and 4-core ARM processor (BCM2837 @ 1.4GHz). A local
Wi-Fi network has been used to enable communication between different edge gateways and its sensor
nodes. The cloud servers are simulated with a desktop computer, equipped with an Intel Core i5
processor and 8GB of RAM memory. Remix IDE is used to deploy smart contracts. Data requests
and transactions to and from third-party cloud services are generated with Metamask, a browser
extension enabling us to initiate parallel transactions during the experiments.
Sensors 2020,20, 3965 10 of 17
4.1. System Implementation
This solution is divided into six processes, as described below. Each process is accompanied
by a short pseudo-code algorithm, which describes the different steps. SC is used to refer
to smart contracts.
1.
System initialization: the generation of encryption parameters, creation of genesis file, and the
initialization of the Ethereum blockchain. Define terms of usage, certificates, and policies through
smart contracts. A new device/buyer registration process is done through the implementation of
smart contracts.
2.
Generation of encryption keys: Each connected device generates its key pair of secret and public
keys
(sk
,
pk)
. The secret key is randomly generated, and then the private key and child secret
keys are derived from it. Each child secret key is used to encrypt one data batch.
Process 1:
Result: System initialization
Define:
ECDSA parameters:
T= (P,a,b,G,n,H)
Encryption keys length: κ;
Generate:
Blockchain genesis block, Hash function Fh;
Implement:
Smart contract codes SC;
Process 2:
Result: Generation of encryption keys
Define:
Unique secret/public key pair (sk,pk)Secret key:
sk =random(κ);
Calculate:
pk = [sk]G;
Generate:
Child secret keys csk0, . . . , cskn;
3.
Data processing, encryption and storage: Edge gateways divide the acquired data segments into
data batches after every time
T
and implement embedded edge AI algorithms. If edge devices do
not have enough resources to process data, data is saved as raw data. After encryption, devices call
the smart contract function. NewDataBatch to add a transaction to the blockchain. The transaction
includes the data hash, the encrypted AES encryption key, the time-stamp of the storage
operation, type, and the size of stored data and price. After encryption and broadcasting the hash
and metadata, each batch is sent to a storage.
4.
Transaction Initialization: When the Application layer (buyer) requests a data batch from
manager nodes (seller), it first looks into the available records calling the smart contract function
LookUpBatch. If it finds suitable data and its price is within predefined limits, then it initializes
a data trade by depositing the amount. This is done calling the smart contract function Deposit,
to which a buyer adds a price, its public encryption key, its address or identifier, and the ID of
the requested batch.
Sensors 2020,20, 3965 11 of 17
Process 3:
Result: Data processing, encryption, and storage
while True do
Generate:
Data batch db;
data_fe atures =pro cessEd geA I(db);
hash =Fh(d ata_f eat ures);
dbE=AES_encrypt(d f cs k);
dbjso n =json(dbE);
uid =cloud_store(dbjson);
db_in f o ={
uid,
hash,
timestamp,
data_type,
data_size,
price,
};
Invoke:
SC.NewDataBatch(db_i n f o);
delay T1;
Process 4:
Result: Transaction Initialization
Define:
Data type: data_type;
Maximum buying price: max_price;
Data batches for sale: dbs = [];
Time window lower limit: star t_time;
Time window upper limit: end_time;
Buyer’s public encryption key: b pk;
Invoke:
resul t =SC.LookUpBatch(dat a_type,
start_time,end_time );
foreach (addr,d b_in f o)in result do
if db_in f o.s elling_price <max_ price then
dbs.append({dev_addr,db_in f o});
foreach (addr,d b_in f o)in dbs if meetsBuyingConditions(db_info) do
Define:
Buying price: price <max_price;
Timestamp: ts;
Calculate:
sign =p rice db_in f o.ui d addr ts;
Invoke:
SC.Deposit(pri ce,uid,addr,bpk,sign);
5.
Transaction Confirmation: Manager nodes run this process periodically, querying available
DataBatch requests with a deposit. If any deposit is found meeting the selling conditions
in the requested batch, it validates the transaction, and then it encrypts the csk of the requested
batch with the buyer’s public key and confirms the Deal.
6.
Finalization of data transfer: When a buyer receives an encrypted data batch AES key, it uses
the ECIES decryption algorithm to obtain the AES batch key. Subsequently, it queries the storage
provider with the batch address and decrypts it to obtain the information. The data transfer,
or purchase, is done if a buyer is satisfied. If the buyer is not satisfied with the received data
batch, it will ask for a refund. The manager node will respond to a dispute case by cross-checking
the batch details. After obtaining the results, a manager node will respond according to the results.
Sensors 2020,20, 3965 12 of 17
Process 5:
Result: Transaction Confirmation
while True do
Invoke:
results =SC.LookUpDeal(de vice_address);
foreach resul t in results if meetsSellingConditions(result)
do
cskE=ECIES_encrypt(csk,result.bsk);
addr =cloud_address(result.uid);Invoke:
SC.Deal(result.bsk,cskE,uid,addr);
delay T2;
Process 6:
Result: Finalization of Data Transfer
Invoke:
resul t =SC.LookUpDeposit(bsk,uid);
if result.done is True then
csk =ECIES_encrypt(resul t.cskE);
data E=cloud_request(result.addr);
data_batch =AES_decrypt(dataE,csk);
4.2. Performance Analyses
Resource consumption has been analyzed at edge gateways to check the required resources
to handle the transaction validation. Figure 4shows the percentage usage of RAM and CPU along
Y-axis. These results are calculated during transaction validation as well as in an idle condition. In the
idle condition, the percentage usage of RAM is 17% as an average, and during transaction, an increase
of 10% to 15% is shown. Overall, on average, it used about 26% of RAM resources whose impact
on overall system memory usage is not significant during a transaction. However, sharp peaks can
be seen in the CPU utilization; in the Idle condition, only 8% CPU resources were used as average,
while during a transaction it used up-to 45% of the total. The CPU variance at idle time was of about
10% and about 5% at peak usage.
0 5 10 15 20 25 30 35 40 45 50
0
10
20
30
40
50
Time (s)
Resource Utilization (%)
CPU
RAM
Figure 4.
Consumption of CPU and RAM during randomly selected transactions and idle time
in between. During transaction handling, CPU requires more resources while in the idle condition it’s
quite low. For the RAM, it is not a huge difference during the transaction handling.
The time that is required to complete a transaction by the edge gateway is measured
and subdivided into sections: time required to retrieve metadata (TRD), transaction validation
Sensors 2020,20, 3965 13 of 17
time (VTR), and transaction confirmation time (TCT). The measurements are shown in Figure 5.
When considering the available limited resources, it is promising that TRD needs only 34.6 ms on
average, VTR 36 ms and TCT 73.6 ms on average. it is worthwhile to note that TCT also relies on
the network, which, in this experiment, was affected by our shared Wi-FI network’s slow network
response time resulting in increased overall time.
Tr. #1 Tr. #2 Tr. #3 Tr. #4 Tr. #5
0
20
40
60
80
Time (ms)
TCT
TRD
VTR
Figure 5.
Latencies to retrieve mata data (TRD), transaction validation time (VTR) needed for single
transaction request, and time required to confirm one transaction (TCT).
Our second group of experiments focused on the end-to-end delay of concurrent requests:
end-to-end delay = request initialization by interested buyer + time to retrieve metadata + response
time by manager nodes + time to confirm one transaction. The results in Figure 6show an increase
in the end-to-end delay by the increasing number of concurrent transaction requests. These results
from a group of experiments show the efficiency of the proposed model to implement in information
critical systems data trade autonomously. This proposed trust-less structure increases reliability
and transparency in data trade. it led us to consider that single board computers can work as manager
nodes to handle their associated data and transactions without the need for third party cloud services
in the future, since the computational resources required leaving room for other edge services and data
processing processes to run at the same time. However, increased delay with a parallel number of
transactions shows its limitations to be used in mission critical systems where time span comes at
first priority.
4.3. Scalability Analysis
Edge/fog computing comes with significant benefits; however, it has an inherent scalability barrier
that limits its ability to support a wider range of applications. Specifically, system models require robust
block creations, involve millions of edge nodes, and a large number of transactions per second. Several
researchers have directed their efforts towards mitigating this issue, with different models to replace
chain models. Some of the existing proposals that address this challenge are pegged sidechains [
55
],
hierarchical trees [
43
], cross-chains [
56
], and DAG [
57
]. However, in EdgeBoT, scalability enhancement
is not a concerning issue, as it is working in a private P2P network that can subdivide this network as
sidechains. Edge gateways are not required to process several requests per minute. After every time T,
manager nodes need to check whether there are new requests for data. If it has a request in a queue,
it will respond to every request one by one.
Sensors 2020,20, 3965 14 of 17
012345678910 11 12 13
100
200
300
400
500
600
700
Transaction Request ID
End-to-End Latency (ms)
Min
Max
Avg
Figure 6. Impact of concurrent transactions on end-to-end transaction latency.
5. Conclusions and Future Work
With the increasing usage of connected devices, the Internet of Things (IoT) is generating a vast
amount of data, but the protection of personal data, the online privacy of users and organizations
is becoming an increasingly challenging task. Intermediaries and dependence on third-parties act as
adversaries and opens the door to a wide variety of vulnerabilities.
In recent years, the edge-computing paradigm, together with ethereum, a distributed ledger,
has shown great potential to be widely leveraged and adopted for securing IoT applications. We have
presented EdgeBoT, a topology for edge computing (actuators, sensors, and end-user applications)
that provides P2P data trade without the need for a third party, making clear the vision of data
ownership model. It enables the extension of a private ethereum blockchain to resource-constrained
edge devices through a hybrid edge-cloud computing architecture that relies on smart gateways
to run the blockchain. EdgeBoT is easily adaptable, scalable, and able to accommodate a wide range
of applications.
In EdgeBoT, devices are fully autonomous in terms of their P2P interaction topologies, being able
to directly trade their data with third parties. All policies of data trade and ownership rights
are implemented through smart contracts. Sidechains are used for parallel transactions and they
make a system scalable. They also limit the use of bandwidth and energy needed by edge gateways
to update blockchain. To securely store data, devices use elliptic curve integrated encryption scheme
(ECIES), generating a unique key for every saved data batch by using child key derivation function
(CKD), and double authentication for devices sending requests frequently, which makes our solution
resilient enough to vulnerable attacks. To share these unique keys, we have implemented an elliptic
curve digital signature algorithm (ECDSA) for communication. The proposed EdgeBot architecture
serves as a generic model to secure IoT data ownership rights while preserving the privacy of users.
It can be implemented in smart home devices, the industrial internet of things (IIoT), smart health
applications (including precision medicine, clinical trials, or accuracy diagnosis), knowledge based
systems, or in the research and development of different IoT products.
We executed extensive proof-of-concept experiments to evaluate the performance and reliability
of the proposed model. The experiments showed that less than 40 % of computing resources were
used on average. This indicated that our model can be implemented on various low power single
board minicomputers available as off-the-shelf products. Promising results of performance analysis
lead us to consider the EdgeBoT as a feasible model for edge computing to secure IoT data ownership
and trade.
Sensors 2020,20, 3965 15 of 17
At this stage, our system shows some limitations of response time, which makes it limited to static
environments only. Scenarios where a user needs a rapid response in dynamic environments need
further study and will be one of the objectives of our next works. Furthermore, we are exploring
a tighter integration of AI algorithms towards embedded and secure edge intelligence and extending
our other works in this area [
10
,
58
]. we aim at defining new frameworks able to deal with
heterogeneous data sources and a wide array of application scenarios.
Author Contributions:
Conceptualization, A.N.; Data curation, A.N. and J.G.; Methodology, A.N. and J.P.Q.;
Supervision, H.K. and T.W.; Writing—original draft, A.N. and J.P.Q.; Writing—review and editing, J.P.Q., M.A.,
T.N.G., A.K.B., H.K. and T.W. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by National Natural Science Foundation of China (Grant No. 61672166 and
U19A2066) and National Key R & D Program of China (No. 2019YFB2101703).
Conflicts of Interest: The authors declare no conflict of interest.
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... o Data Exchange Criteria Matching -smart contracts are used to match the data provider and requester privacy policy requirements (Dib et al., 2020;Lopez and Farooq, 2019;Loukil et al., 2018;Nawaz et al., 2020;Rantos et al., 2019). ...
... Pricing (Liu et al., 2019) Contact Tracing (Lv et al., 2020) Data Exchange Criteria Matching (Dib et al., 2020;Lopez and Farooq, 2019;Loukil et al., 2018;Nawaz et al., 2020;Rantos et al., 2019) ...
... Traditional (Chuang et al., 2020(Chuang et al., , 2018Dimitriou and Mohammed, 2020;Fan et al., 2020;Nawaz et al., 2020;Zhaofeng et al., 2020;Zichichi et al., 2020) Anonymous (Ou et al., 2019) Fairtrade (Li et al., 2021;Zhao et al., 2019) Micro-payments (Radhakrishnan et al., 2019;Robert et al., 2020) Reputation - (Chuang et al., 2020(Chuang et al., , 2018Pu et al., 2020;Mohamed Abdur Rahman et al., 2020;Saldamli et al., 2020) As shown on Figure 6, 19 implementations (49%) use ledger technology as a storing mechanism. Of those, 8 store the full data set, 9 store the hash of the data set, and 2 use a policy to store the complete data set on or off-ledger. ...
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To understand how distributed ledger technology (DLT) enables people-centered IoT solutions we conducted a systematic literature review of tested implementations since 2017. We created a people-centered classification to analyze 39 implementations. We found that people-centered DLT-IoT architectures are in their infancy and detected no evidence of emerging patterns. We observed that Ethereum is the most used DLT. Fit-for-purpose technologies like IOTA and concepts like Self-Sovereign Identity (SSI) were underrepresented. We noted an increased interest in privacy-preserving and edge-computing mechanisms, and identified three areas for future research. We hope this survey will assist others learning more about people-centered IoT solutions.
... In the IBEC, blockchain can be used for data trading or sharing in a decentralized manner. This is mainly because the incentive mechanism in blockchain can motivate data producers to share high-quality data [158]- [160]. For instance, a four-layer data trading architecture, named EdgeBoT, is introduced in [158] for the IoT system by combining edge computing and Ethereum blockchain. ...
... No [158] Data Management Secure and efficient data trading environment in IoT. ...
Preprint
As an important technology to ensure data security, consistency, traceability, etc., blockchain has been increasingly used in Internet of Things (IoT) applications. The integration of blockchain and edge computing can further improve the resource utilization in terms of network, computing, storage, and security. This paper aims to present a survey on the integration of blockchain and edge computing. In particular, we first give an overview of blockchain and edge computing. We then present a general architecture of an integration of blockchain and edge computing system. We next study how to utilize blockchain to benefit edge computing, as well as how to use edge computing to benefit blockchain. We also discuss the issues brought by the integration of blockchain and edge computing system and solutions from perspectives of resource management, joint optimization, data management, computation offloading and security mechanism. Finally, we analyze and summarize the existing challenges posed by the integration of blockchain and edge computing system and the potential solutions in the future.
... The main element involved in the blockchain technology is of the trust and maintain the security of the information provided by the users (Liang et al., 2017). Meanwhile, the use of technology in the financial and banking industry inculcates that the customers with their digital identities need to protect their identities through their data ownership mechanism and they can control their data by themselves (Karafiloski & Mishev, 2017;Liang et al., 2017;Nawaz et al., 2020). ...
... Blockchain technology has the significant feature of user oriented controlling mechanism of data through which they are authorized to allow and provide permission to which their information is accessible Rabbani, Khan, & Atif, 2021;Mustafa Raza Rabbani et al., 2021d). The online applications of Facebook, Google, and Amazon have centralized systems and they mainly control the information of users raising the transparency and authenticity concerns (Liang et al., 2017;Nawaz et al., 2020). However, blockchain technology successfully caters such problems of privacy and information security where the user enjoys the rights of ownership on his/her own data. ...
Chapter
Blockchain and cryptocurrency have almost become synonymous. Cryptocurrency is arguably one of the most sensational financial innovations of the 21st century. The current study claims that blockchain technology is not limited to the application of digital currencies in finance and banking; there are wide applications of blockchain technology in the given field. Blockchain uses the unique properties enabling decentralized, secured, transparent, and temper-proof financial transactions that have the potential to revolutionize the financial services industry. Given such a stance, the chapter outlines the application of blockchain technology in the finance arena beyond the digital currency. In this chapter, the authors provide the 10 applications of blockchain technology in the financial services industry implementing the blockchain technology and revolutionizing the finance and banking industry. The chapter also highlights the hurdles to application of blockchain technology in the finance and banking industry.
... Arneil investigated how English settlers used Locke's theory of property in the Two Treatises of Government as an ethical justification for its colonial actions, including appropriating Indian-occupied land and tilting the balance of power in favor of property owners [9]. Nawaz et al. proposed a blockchain-based data security IoT platform, enabling devices to trade data directly with third parties and without intermediaries, while protecting data ownership and end-users' privacy [24]. Sun et al. proposed an extension to an existing data management framework, enabling access to public and encrypted data sources to enhance information transparency, enable semantic data treatment, and provide an appropriate data fusion context [25]. ...
... Thus, this study presupposes that the concept of "raw materials → information → knowledge → convergence content on heterogeneous information" introduced above applies to the maritime industry within the modern trading system. The primary-level raw materials that make up the comprehensive conceptual data include the raw facts and evidence needed to apply feasible theories, empirical evidence verified by proven methods, or experiments derived from scientific simulations, and facts, letters, voices, and pictures acquired through a phenomenological survey of literature [24]. ...
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Stakeholders in the maritime industry have agreed to expand commercial operations by developing autonomous vessels, the epitome of innovative technologies. However, they are still concerned about the risks of infringing on the rights and interests of individuals or organizations linked to data transactions. Based on Locke’s theory of property ownership, and the assurance of absolute ownership as specified in the Constitution of the Republic of Korea, the business model is still in its early stages. This study examined the concept, transaction structure, and application scope of data activating digital platforms to support data transactions, and aid in advancing the new business sector for commercial operations of maritime autonomous surface ships (MASSs). Consequently, this study contributes to and recognizes the future value of knowledge and intellectual property related to maritime data. This is achieved by defining the intellectual property emerging from newer avenues, such as MASSs, blockchain technology, and smart ports, that are consequential to the rapid development of the maritime industry.
... Many research has recently proposed attention to creating a good perspective for IoT and regulating IoT use. However, such particular answers offer no general solution for many daily challenges in IoT networks, such as problem-identification, fixation, mobility, compatibility, and sustainability (Nawaz et al., 2020). A permanent middleware tier for supporting digital environments such as FI-WARE (Future Internet-WARE) and European Telecommunications Standards Institute (ETSI) is another way to enhance IoT devices. ...
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The emergence of the Internet of Things (IoT) has given digital communications a range of interconnected functionalities. The IoT is an intelligent technology between the real and the digital world. An IoT end-to-end solution comprises components such as Component for User Access: Mobile Application or Web Application. IoT Gateway. Cloud Infrastructure. The upcoming technology of contacts and service delivery in IoT is comprehensive and open. The broad IoT platform is provided by a network of sensors and separate nodes. IoT framework is adopted for implementation and assistance by the intelligent computing, storing information, and characteristics of the devices. Safety is a big concern in such an open atmosphere and needs to be dealt with the decentralization. A security framework for future IoT end-to-end connectivity (SF-FIoTC) is proposed. Decentralized and scalable safety architecture for future IoT is developed. This system uses the benefits of intelligent computation and tree-based hash systems to ensure device-to-device privacy and to need authorization. The tree-based hashing can be used for the authentication of demands. The system's decentralized safety feature offers safety at the machine level and ensures greater confidentiality and communication systems reliability. In terms of classification accuracy, transmission loss, delivery ratio, and latency, the operational study of the suggested security architecture can be analyzed, and the performance is excellent in all cases.
... Distributed ledger technologies have multiple applications in multi-robot systems and distributed autonomous systems. Blockchain technology, in particular, has been applied to robot swarms able to deal with byzantine agents [57], for sharing computational and communication resources [58], but also for privacy-critical applications [59,60]. The distributed consensus algorithms in DLTs, the auditability of operations, and the built-in encryption, among others, aid in designing more secure and privacy-preserving systems at the edge [13]. ...
Article
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Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.
... This has been exploited to integrate blockchain with supply chain management, which, in the case of the IoT ecosystem, allows guaranteeing the origin and trustworthiness of data without the need for intermediaries [6]. The IoT devices can be fully autonomous to directly trade their data with third parties through smart contracts that govern all policies of data trade and ownership rights [7]. ...
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Non-fungible tokens (NFTs) are widely used in blockchain to represent unique and non-interchangeable assets. Current NFTs allow representing assets by a unique identifier, as a possession of an owner. The novelty introduced in this paper is the proposal of smart NFTs to represent IoT devices, which are physical smart assets. Hence, they are also identified as the utility of a user, they have a blockchain account (BCA) address to participate actively in the blockchain transactions, they can establish secure communication channels with owners and users, and they operate dynamically with several modes associated with their token states. A smart NFT is physically bound to its IoT device thanks to the use of a physical unclonable function (PUF) that allows recovering its private key and, then, its BCA address. The link between tokens and devices is difficult to break and can be traced during their lifetime, because devices execute a secure boot and carry out mutual authentication processes with new owners and users that could add new software. Hence, devices prove their trusted hardware and software. A whole demonstration of the proposal developed with ESP32-based IoT devices and Ethereum blockchain is presented, using the SRAM of the ESP32 microcontroller as the PUF.
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Background: Social media platforms have numerous potential benefits and drawbacks on public health that have been described in the literature. The coronavirus disease 2019 (COVID-19) pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions. Objective: This review aims to highlight a brief history of social media in healthcare, as well as report its potential negative and positive public health impact that have been characterised in the literature. Methods: We searched electronic bibliographic databases in Pubmed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from 10 Dec 2015 to 10 Dec 2020. We screened title and abstracts, and selected relevant reports for review of full-text and reference lists. These were analysed thematically and consolidated into applications of social media platforms for public health. Results: The positive and negative impact of social media platforms on public health are catalogued based on recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence (AI). However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy. Conclusions: Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health.
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The IoT devices deployed in various application scenarios will generate massive data with immeasurable value every day. These data often contain the user’s personal privacy information, so there is an imperative need to guarantee the reliability and security of IoT data sharing. We proposed a new encrypted data storing and sharing architecture by combining proxy re-encryption with blockchain technology. The consensus mechanism based on threshold proxy re-encryption eliminates dependence on the third-party central service providers. Multiple consensus nodes in the blockchain network act as proxy service nodes to re-encrypt data and combine converted ciphertext, and personal information will not be disclosed in the whole procedure. That eliminates the restrictions of using decentralized network to store and distribute private encrypted data safely. We implemented a lot of simulated experiments to evaluate the performance of the proposed framework. The results show that the proposed architecture can meet the extensive data access demands and increase a tolerable time latency. Our scheme is one of the essays to utilize the threshold proxy re-encryption and blockchain consensus algorithm to support IoT data sharing.
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With the development in information and communications technology (ICT) and drones such as Internet-of-Things (IoT), edge computing, image processing, and autonomous drones, solutions supporting search and rescue (SAR) missions can be developed with more intelligent capabilities. In most of the drone and unmanned aerial vehicle (UAV) based systems supporting SAR missions, several drones deployed in different areas acquire images and videos that are sent to a ground control station (GCS) for processing and detecting a missing person. Although this offers many advantages, such as easy management and deployment, the approach still has many limitations. For example, when a connection between a drone and a GCS has some problems, the quality of service cannot be maintained. Many drone and UAV-based systems do not support flexibility, transparency, security, and traceability. In this paper, we propose a novel Internet-of-Drones (IoD) architecture using blockchain technology. We implement the proposed architecture with different drones, edge servers, and a Hyperledger blockchain network. The proof-of-concept design demonstrates that the proposed architecture can offer high-level services such as prolonging the operating time of a drone, improving the capability of detecting humans accurately, and a high level of transparency, traceability, and security.
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Public fog nodes extend cloud services for the Internet of Things (IoT) clients and smart devices to provide additional computation capabilities, storage space, and reduce latency and response time. The openness and pervasiveness of public fog nodes leads to the requirement of using trust models to ensure reliability, security, privacy, and meet the service-level agreements (SLAs). Conventional trust models for public fog nodes are centrally configured, deployed, and maintained considering security, privacy, and SLA requirements. However, these trust models enforce centralized governance policies across the system which leads towards the single-point-of-failure and single-point-of-compromise over IoT devices' and users' personal data. This paper proposes a decentralized trust model in order to maintain the reputation of publicly available fog nodes. The reputation is maintained considering users' opinions about their past interactions with the public fog nodes. The proposed trust model is designed using public Ethereum blockchain and smart contract technologies in order to enable decentralized trustworthy service provisioning between IoT devices and public fog nodes. The proposed approach is tested and evaluated in terms of security, performance, and cost. The results show that using blockchain for decentralized reputation management could become more advantageous when compared to the existing centralized trust models.
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The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases. A use case of edge AI for ECG feature extraction and real-time support of multiple sensor nodes is analyzed in the experiments.
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Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90\% and an average recall over 95\% in fall detection.
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The smart healthcare environment offers plenty of opportunities to help organizations and healthcare practitioners offer better services to the patients. The increasingly networked nature of the healthcare environment coupled with the introduction of Internet of Things (IoT) devices in the mix allow physicians to both deliver critical-care and preventive medicine services to their patients more effectively and efficiently. However, the smart healthcare environment exposes the patients’ data to various risks including exposure. The two biggest threats to patients’ data privacy are 1) Lack of understanding of various policies and regulations that are in place and how they affect the handling of patients’ data and 2) the threats that are posed by the hackers. A recent study indicated the lack of knowledge of general population as to how information is processed, transmitted and stored in a corporate environment. This chapter intends to educate the reader regarding various prevalent legislation and the threats that can potentially expose corporate digital assets and patients’ sensitive data.
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