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Blockchain based Authentication and Trust
Evaluation Mechanism for Secure Routing in
Wireless Sensor Networks
Saba Awan1, Maimoona Bint E Sajid1, Sana Amjad1, Usman Aziz2, Muhammad Usman Gurmani1, Nadeem Javaid1,∗
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2Department of Computer Science, COMSATS University Islamabad, Attock 43600, Pakistan
Email: sabaawan046@gmail.com, maimoonasajid176@yahoo.com, sanaamjad702@gmail.com,
usmanaziz91@gmail.com, usmankhangurmani@gmail.com
∗Corresponding author: nadeemjavaidqau@gmail.com; www.njavaid.com
Abstract—In this paper, a blockchain based authentication
model is proposed where the identity of each node is stored on
the blockchain. The public and private blockchains are used for
authentication. The authentication of Sensor Nodes (SNs) is per-
formed at the private blockchain, whereas the public blockchain
authenticates the cluster heads. The existing malicious node
detection methods do not guarantee the authentication of the
entities in Wireless Sensor Networks (WSNs). The unregistered
nodes can easily access the resources of the network and perform
malicious activities. Moreover, the malicious nodes broadcast
wrong route information that increases packet delay and lowers
packet delivery ratio. In the proposed model, the trust value is
calculated in order to remove the malicious nodes. The secure
routing is performed on the basis of the most trustworthy nodes
in the network. The aim is to reduce the packet delay and increase
the packet delivery ratio. The simulation results show that the
high throughput and packet delivery ratio is achieved due to the
presence of highly trusted nodes. Moreover, our proposed model
detects the malicious nodes effectively.
Index Terms—Wireless Sensor Networks, Blockchain, Authen-
tication, Trust Evaluation, Smart Contract, Secure Routing.
I. INTRODUCTION
The Wireless Sensor Networks (WSNs) play a significant
role in many areas, such as medical, military, surveillance,
industrial, etc., [1]–[3]. A WSN is a self-organizing network
where Sensor Nodes (SNs) are randomly deployed and have
limited storage, energy and computational power [4]–[6]. They
monitor the parameters like temperature, humidity, etc. and
transmit the data towards the Base Stations (BSs) [7]. However
due to the limited resources, the attackers can easily attack the
SNs [8].
A blockchain is an emerging technology that consists of
nodes. The nodes maintain the state of a distributed ledger.
It efficiently keeps a record of transactions among multiple
parties [9], [10]. No one can easily perform data tampering
as blockchain is immutable. The data in blockchain is secure
because the blocks are connected via hashes. Moreover, each
block also consists of the block header and the block body
[11]. The root hash of the Merkle Tree is present in the block
header. While the block body comprises of the transactions.
If any data tampering is performed, it can be easily identified
by comparing the hash of the data with the root hash [12]. In
WSNs, the security threats are becoming more serious day by
day [13], [14]. There are two types of attacks namely internal
and external in WSN. In internal attack, nodes behave selfishly
while in external attack, attackers force the nodes to perform
malicious activities. Therefore, it is very crucial to identify
malicious nodes and remove them from the network.
In WSNs, the detection of malicious nodes is widely studied
and divided into WSNs protocols and trust model. In a
network without authentication, any node can enter in the
network and forge the identity of benign nodes. The adversary
nodes broadcast wrong route information on the behalf of the
compromised nodes. The existing malicious node detection
methods do not guarantee the authentication of nodes. More-
over, the malicious node detection is performed on the basis of
trust value only. Different metrics are used to check the node’s
fairness; however, detection is performed at a later stage which
consumes more resources of the network [8], [15], [16].
The presence of a large number of malicious nodes in the
network have negative impact on the routing [17], [18]. The
malicious nodes modify the data and broadcast wrong route
information which affects the performance of the network.
The attacks in WSNs occur in two ways one of them is
data and other is routing. In data attack, the adversary nodes
perform data tampering. While in routing attack, adversary
nodes choose a least energetic route that depletes the energy
of SNs. The efficiency of the network is affected in terms of
high delay and low packet delivery ratio [19], [20]. Moreover,
the SNs are resource constrained devices that are vulnerable
to different attacks like spoofing and impersonation, etc.
To overcome the challenges mentioned above, blockchain
based authentication and trust evaluation mechanism for secure
routing in the WSNs is proposed. In [19], authors propose a
trust aware localized routing scheme for WSNs. However, the
authentication and malicious node detection are not consid-
ered. Due to this, unregistered nodes can access the network
resources and data [15]. The malicious nodes can enter in the
network and forge the real identity of the nodes. Moreover,
they drop the data packets, which increases the network delay
and decreases the packet delivery ratio [20]. The contributions
of this paper are summarized as follows.
1) Any node can easily enter and access the data and the
resources in the network without authentication. The
authentication of the nodes is important to secure the
network from the intruders.
2) After the authentication of nodes, they may selfishly
behave and drop the data packets. Therefore, the trust
of the nodes is evaluated based on the trust value.
3) The victim nodes drop the data packets, which increases
the number of retransmissions and delay. Therefore,
highly trustworthy nodes are used to perform the secure
and efficient routing.
The rest of the paper is organized as follows: Section 2 con-
tains related work, the proposed system model is discussed in
Section 3. The simulation of the proposed model is presented
in Section 4. The conclusion and future work are explained in
Section 5.
II. RE LATE D WOR K
This section discusses the literature review of the related
studies.
A. Trust Evaluation for Malicious Nodes Detection
In a hostile and remote environment, whenever any
malicious activity is performed on beacon nodes, getting
an accurate node location is challenging. Incorrect location
estimation affects the localization accuracy and energy
dissipation affects the lifetime of the WSNs [8]. According
to [15], traditional methods of WSNs do not keep the record
of original data for later use. In [16], the dynamic behavior
of SN makes it a challenging concern for the localization
process. Due to the bisected information, the SNs cannot
broadcast the accurate location in the network.
B. Node’s Authentication to Ensure Data Confidentiality and
Validity
Intra-platform authentication of specific users and random
access of unauthorized users in IoT platform requires an
access control process for its protection [21]. Conventional
IoT identity protocols are centralized-based Trusted Third
Party Protocol (TTP) that are vulnerable to a single point of
failure [22]. Dynamic WSNs have more uncertainty and large
coverage as compared to static WSNs, thus cause trust issues.
The traditional WSNs are mostly homogeneous that involve
complex design protocols and additional overhead [23]. Exist-
ing models do not allow content access, reliable authentication
and trust management [12]. Lack of traceability of each node
in IoT network leads to inefficiency and significant loss in
industrial growth. However, interconnection, implementation
and communication among IoT devices lead to personal and
confidential concerns [24]. The existing encryption protocols
such as Secure Socket Layer (SSL) and Transport Layer
Security (TLS) allow secure communications from one end
to another. These protocols do not ensure user anonymity and
authentication of data [25].
C. Secure Routing protocols in a WSN
The existing solutions have concentrated on the evolution
of static topology and ignored the dynamic behavior of the
normal nodes. Data collection of static nodes may be imprecise
and leads to unreliability and network disruption. The dynamic
nature of the network causes energy deficiency and degrada-
tion of packet delivery [26], [27]. The presence of adversaries
in a WSN introduces different challenges and threats. Many
solutions have been proposed to tackle security issues, but
they do not ensure network performance. In the secure routing,
feedback process performed based on the most trusty beacon
nodes increase network overhead [19]. Due to the central
nature of trust issues that arise between IoT vendors and the
high cost of implementing trust management such as PKI [28].
The existing routing strategies do not distinguish the nodes’
behaviors and suspicious nodes take part in the routing. When
a malicious node gets a data packet by their neighbors, discard
them and does not forward to the next neighbor, which creates
a black hole attack [29].
D. Lightweight Blockchain for IoT
Blockchain requires high resources and poor performance
that is not well addressed. Furthermore, miners integrate the
huge block data and handle many transactions in a Peer to Peer
(P2P) network, where storage and bandwidth are challenging
to manage in IIoT devices [30]. Existing blockchain technol-
ogy can not handle the Internet of Underwater Things (IoUT)
big data efficiently where multiple nodes have a duplication of
the full ledger. Distributed nature of blockchain requires high
storage [31]. The local copy of the blockchain records is not
feasible for memory constrained and low power devices [32].
Blockchain has a slow update rate due to the chain nature and
update the data in parallel. Whereas in tangle, transactions
validate their two previous transactions before joining it at
that speed. SNs have limited computational resources and in-
validate the prior transactions [33]. In blockchain application,
wireless mobile face many challenges of PoW puzzle in the
mining process, that requires high processing ability and data
storage availability [34].
E. Good Performance regarding Data Storage
In a WSN, nodes have limited energy and storage. Thus,
some nodes may behave selfishly. Moreover, these nodes
do not forward the packet and entire network is effected.
Furthermore, there is no incentive mechanism for the network
nodes to store data [35]. To perform PoW on mobile
devices, high computational power is required which makes
it impossible to implement blockchain on it. Nodes have
limited capacity to store the information of nodes, resulting
in keeping node information for a limited time. To keep the
network updated, all the elements in the network must have
information of other neighboring nodes which is not possible
all the time [36].
F. Data Security and Privacy
The presence of adversaries in a WSN introduces different
challenges and threats. Different solutions are proposed to
tackle security issues, but they do not ensure network per-
formance and secure routing. While, feedback process is per-
formed based on the most trusty beacon nodes which increases
network overhead [19]. The issues identified in the existing
smart city system include bandwidth bottlenecks, high latency,
scalability, privacy and security [37]. The growing number of
the IoT devices increases security issues due to attacks. It is
necessary to protect the devices from cyber attacks. Existing
solutions are not suitable due to the issues such as storage
constraints, single point of failure, high latency and high
computational cost. Furthermore, the traditional systems face
issues such as big data problems (requires a massive amount
of data for accurate decisions and detection of attacks) and
lack of privacy ( collects data without user’s permission). This
leads to wrong decision making [38].
In crowd sensing network, mobile devices sense and compute
the data which saves cost, but there is an issue of privacy
leakage. Low user engagement and upload false information
by users that cause the disclosure of private information [39].
In IoT, central authority is used to store information. As
shellfish products are highly vulnerable, perishable and quality
deterioration generally takes place in a short time. A high
rate of temperature control in shellfish products is a major
issue which affects the shelf life, freshness loss and food
decomposition [40]. In an Information-Centric Network (ICN)
of a WSN, caching scheme is not investigated. Dispersal of
data, i.e., duplication, raises privacy and security issues [41].
G. Blockchain based Fair Non-repudiation Mechanism
The existing service schemes face security challenges which
cause many concerns like malicious clients may refuse the
service provisioning and service providers can provide fake
services [42].
III. SYS TE M MOD EL
The proposed system model is motivated from [15], [22].
In proposed system model, a blockchain based authentication
and trust evaluation mechanism for secure routing in the WSN
is proposed. A WSN comprised of CHs, SNs, BSs and end
users. As the SNs have limited computational power and
storage, they sense the data and send it to their associated
CHs. The CHs receive the data and forward it to the nearby
BSs, which consist of abundant resources like storage, energy,
computational power, etc. In the proposed model, the public
and private blockchains are used as shown in Fig. 1. The public
blockchain is deployed on BSs whereas, the private blockchain
is deployed on CHs.
The steps include in the authentication scheme are initial-
ization, registration and authentication. In initialization, the BS
initializes all the nodes that are present in the network. The BS
generates public and private keys for the CHs, SNs and itself.
The keys are used to verify the integrity of messages. Each
node has its unique MAC (Media Access Control) address.
The identity of BS, CH and SN is marked as BSID, CHID and
SNID. The CHs are registered using smart contract that are
deployed on the public blockchain. The identity information
of CHs is stored in the public blockchain. The smart contract
verifies if a CH node already exists or not. Moreover, the
validity of the CH’s MAC address and correctness of its
identity is checked. When these steps are performed success-
fully, the public blockchain records the identity of CHs. If
the verification of identity fails, an error message is returned.
In contrast, registration of SNs is performed on the private
blockchain. The SNs are allowed to join the blockchain
network after registration. The registration process of SNs is
same as of CHs. The identity information of SNs are stored
on the private blockchain. The SNs are bound to their CHs
after the deployment.
The WSNs are vulnerable to two kinds of attacks i.e.,
external and internal. The registration and authentication of
the nodes mitigates the external attacks. The intruders are not
allowed into the network.
However, internal nodes may behave maliciously and
broadcast wrong information into the network. If a node
wants to join the network it must be registered. When a
SN communicate with CH, the CH authenticate the identity
of SN using private blockchain. Furthermore when a CH
communicates with BS, the BS verifies the identity of
CH using public blockchain. Additionally when two CHs
communicate, the mutual authentication of CHs is performed.
Both CHs sends request to the BS for authentication. In
internal attack, nodes behave selfishly in the network. It is
crucial to identify and remove the malicious nodes after
registration. The trust value of the nodes is computed in
order to remove the malicious nodes. The following steps are
involved in the trust evaluation of the nodes.
Step 1: The CH checks the state of the SNs either they are
alive or not.
Step 2: The delayed transmission, forwarding rate and
response time information is collected for alive nodes and the
communication quality is computed.
Step 3: The trust value ηis calculated on the basis of nodes
communication quality.
Step 4: If ηis greater than a defined threshold, it is considered
as a legitimate node otherwise malicious.
Step 5: The ηof each node lies between 0 and 1. After the
trust evaluation, CHs sends a ηof the nodes to the BS. The
smart contract of malicious node detection is deployed at the
BS.
Step 6: The nodes with high ηtransmit the packet to their
associated CHs. After receiving the packet from SNs, CHs
forward it to the nearby BS.
Sensor nodes
Sensor node
Cluster Head
Base Station
Public
Blockchain
Private
Blockchain
Fig. 1: Proposed System Model
The Table. I shows the limitation identified and the proposed
solution and their validation. In first solution the authentication
is performed based on the two blockchains, i.e., public and
private. On a public blockchain, BS registers the CH and
the SN is registered by the CH on the private blockchain.
The unique identity of each CH and SN is stored on BS.
Throughput and packet delivery ratio are used to evaluate the
performance of the proposed system.
In second solution, whenever the authentication of the nodes
is performed, the ηof each node is computed that lies between
0 and 1. Trust value is used to identify the legitimate or
malicious nodes.
In third solution, most trustworthy nodes take part in the packet
transmission to reduce the delay. In the network, whenever
malicious nodes get a packet from their neighbor node, they
do not forward and drop it, which increases the delay. The
efficiency of the network is affected due to high delay and
packet drop ratio. The Performance parameters are packet
delivery ratio and throughput (S3).
IV. RES ULT S AN D DISCUSSIONS
This section presents the simulation results and their dis-
cussion. The MATLAB is used to validate the performance
of the proposed model. The SNs are considered stationary for
simulations. The parameters used in the simulations are given
in Table 2.
The Fig. 2 shows the packet delivery ratio to a different
number of rounds. The number of packets increases with the
number of rounds. The ηof the node is shown in Fig. 4.
TABLE I: Mapping of Limitations Identified, Solutions Pro-
posed and Validations Done
Limitations Identi-
fied
Solutions Proposed Validations
L1: Unregistered
nodes access the
network resources
[8], [15], [19].
S1: The WSN
which consist of
two blockchains i.e.
public and private.
V1: Throughput,
packet delivery ratio
L2: Malicious node
detection [19].
S2: Malicious node
detection is based on
trust value
V2: Credibility of the
SNs
L3: Insecure routing
[19]
S3: Most trust worthy
nodes perform a se-
cure routing to reduce
the delay
V3: Throughput,
packet delivery ratio
TABLE II: Simulation Parameters
Parameter Value
Sensing area 100 m ×100 m
Deployment Random
Sensor nodes 8
Cluster head 4
Initial energy 0.5 J
The route is selected on the basis of η. Therefore there is
a high value of packet delivery ratio. As SNs have limited
computational power, they do not directly send their packets
to the BS which consumes high energy. Therefore, each CH
of the cluster collects data from its all associating SNs and
transmits it to the forwarder CH or BS based on the minimal
10 20 30 40 50 60 70 80 90 100
Number of rounds (r)
0.1
0.15
0.2
0.25
0.3
0.35
Packet delivery ratio
Fig. 2: Packet Delivery Ratio
distance. The proposed model improves the possibility of
receiving packets successfully. A large number of cooperating
nodes are involved in packet transmission, thus reduces the
computational overhead. High reliability is achieved due to
the authentication of the nodes, outsiders can not participate
in it.
10 20 30 40 50 60 70 80 90 100
Number of rounds (r)
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
Average throughput (MB/s)
Fig. 3: Throughput
Fig. 3 depicts that the packets are successfully received at
the BS. Packets sent to the CHs depend on the number of
high trusted nodes. The more trustworthy nodes send more
packets to the BS, thus increases the network throughput and
packet delivery ratio. To transmit packets to the BS, different
forwarder CHs forward the data to the BS in each round.
Therefore, nodes have enough energy to transmit more packets
to the BS and the throughput of the network is also increased.
Fig. 4 shows the trust value of SNs. The malicious SNs are
detected based on the obtained η. An appropriate threshold
is set and if the ηof SN is less than the threshold. The
node is considered malicious; otherwise, it is marked as a
legitimate node. When authentication of SN is performed, only
a registered node can participate in the network. However,
12345678
Sensor nodes
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Trust value
Fig. 4: Trust value of sensor nodes
due to the limited resources, the node may behave selfishly.
Therefore trust value is calculated based on the delayed
transmission, response time and forwarding rate.
V. CONCLUSION AND FUTURE WORK
In this paper, we have proposed a blockchain based au-
thentication and trust model for secure routing. To achieve
the goal of authentication, the smart contract, public and
private blockchain is used. The local blockchain is established
between the CHs and the BS are added to global blockchain.
The identity registration between SNs and CHs are completed.
When registration is completed, trust value of a nodes is
computed. The simulation results show higher throughput and
packet delivery ratio in the presence of highly trusted nodes.
As credibility of each node is computed, the nodes having high
trust value participates in the network otherwise removed from
the network. The packet transmission is performed in the intra-
network. In future, we will extend our work in multi WSNs;
therefore, routing will be performed in inter-network.
REFERENCES
[1] Kandris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications
of wireless sensor networks: an up-to-date survey. Applied System
Innovation, 3(1), 14.
[2] Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A
survey of network lifetime maximization techniques in wireless sensor
networks. IEEE Communications Surveys & Tutorials, 19(2), 828-854.
[3] Noel, A. B., Abdaoui, A., Elfouly, T., Ahmed, M. H., Badawy, A., &
Shehata, M. S. (2017). Structural health monitoring using wireless sensor
networks: A comprehensive survey. IEEE Communications Surveys &
Tutorials, 19(3), 1403-1423.
[4] Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim, H. J. (2019). Energy
efficient routing algorithm with mobile sink support for wireless sensor
networks. Sensors, 19(7), 1494.
[5] Azarhava, H., & Niya, J. M. (2020). Energy efficient resource allocation
in wireless energy harvesting sensor networks. IEEE Wireless Commu-
nications Letters, 9(7), 1000-1003.
[6] Khan, Z. A., Latif, G., Sher, A., Usman, I., Ashraf, M., Ilahi, M., &
Javaid, N. (2019). Efficient routing for corona based underwater wireless
sensor networks. Computing, 101(7), 831-856.
[7] Lee, H. C., & Ke, K. H. (2018). Monitoring of large-area IoT sensors
using a LoRa wireless mesh network system: Design and evaluation. IEEE
Transactions on Instrumentation and Measurement, 67(9), 2177-2187.
[8] Kim, T. H., Goyat, R., Rai, M. K., Kumar, G., Buchanan, W. J., Saha,
R., & Thomas, R. (2019). A novel trust evaluation process for secure
localization using a decentralized blockchain in wireless sensor networks.
IEEE Access, 7, 184133-184144.
[9] Bao, Z., Wang, Q., Shi, W., Wang, L., Lei, H., & Chen, B. (2020). When
Blockchain Meets SGX: An Overview, Challenges and Open Issues. IEEE
Access.
[10] Gourisetti, S. N. G., Mylrea, M., & Patangia, H. (2019). Evaluation and
demonstration of blockchain applicability framework. IEEE Transactions
on Engineering Management, 67(4), 1142-1156.
[11] Xu, Y., & Huang, Y. (2020). Segment blockchain: A size reduced storage
mechanism for blockchain. IEEE Access, 8, 17434-17441.
[12] Moinet, A., Darties, B., & Baril, J. L. (2017). Blockchain based
trust & authentication for decentralized sensor networks. arXiv preprint
arXiv:1706.01730.
[13] Jiang, Q., Zeadally, S., Ma, J., & He, D. (2017). Lightweight three-factor
authentication and key agreement protocol for internet-integrated wireless
sensor networks. IEEE Access, 5, 3376-3392.
[14] Shin, S., & Kwon, T. (2019). A lightweight three-factor authentication
and key agreement scheme in wireless sensor networks for smart homes.
Sensors, 19(9), 2012.
[15] She, W., Liu, Q., Tian, Z., Chen, J. S., Wang, B., & Liu, W. (2019).
Blockchain trust model for malicious node detection in wireless sensor
networks. IEEE Access, 7, 38947-38956.
[16] Goyat, R., Kumar, G., Rai, M. K., Saha, R., Thomas, R., & Kim, T. H.
(2020). Blockchain Powered Secure Range-Free Localization in Wireless
Sensor Networks. Arabian Journal for Science and Engineering, 45(8),
6139-6155.
[17] Alghamdi, W., Rezvani, M., Wu, H., & Kanhere, S. S. (2019). Routing-
aware and malicious node detection in a concealed data aggregation for
WSNs. ACM Transactions on Sensor Networks (TOSN), 15(2), 1-20.
[18] Yadav, M., Fathi, B., & Sheta, A. (2019). Selection of WSNs inter-cluster
boundary nodes using PSO algorithm. Journal of Computing Sciences in
Colleges, 34(5), 47-53.
[19] Kumar, M. H., Mohanraj, V., Suresh, Y., Senthilkumar, J., & Nagalalli,
G. (2020). Trust aware localized routing and class based dynamic block
chain encryption scheme for improved security in WSN. Journal of
Ambient Intelligence and Humanized Computing, 1-9.
[20] Latif, K., Javaid, N., Ullah, I., Kaleem, Z., Abbas, Z.,& Nguyen, L.
D. (2020). DIEER: Delay-Intolerant Energy-Efficient Routing with Sink
Mobility in Underwater Wireless Sensor Networks. Sensors, 20(12), 3467.
[21] Hong, S. (2020). P2P networking based internet of things (IoT) sensor
node authentication by Blockchain. Peer-to-Peer Networking and Appli-
cations, 13(2), 579-589.
[22] Cui, Z., Fei, X. U. E., Zhang, S., Cai, X., Cao, Y., Zhang, W., & Chen,
J. (2020). A hybrid BlockChain-based identity authentication scheme for
multi-WSN. IEEE Transactions on Services Computing, 13(2), 241-251.
[23] Y., Wang, Z., Xiong, J., & Ma, J. (2020). A blockchain-based secure key
management scheme with trustworthiness in DWSNs. IEEE Transactions
on Industrial Informatics, 16(9), 6193-6202.
[24] Rathee, G., Balasaraswathi, M., Chandran, K. P., Gupta, S. D., &
Boopathi, C. S. (2020). A secure IoT sensors communication in industry
4.0 using blockchain technology. Journal of Ambient Intelligence and
Humanized Computing, 1-13.
[25] Kolumban-Antal, G., Lasak, V., Bogdan, R., & Groza, B. (2020). A
Secure and Portable Multi-Sensor Module for Distributed Air Pollution
Monitoring. Sensors, 20(2), 403.
[26] Haseeb, K., Islam, N., Almogren, A., & Din, I. U. (2019). Intrusion
prevention framework for secure routing in WSN-based mobile Internet
of Things. Ieee Access, 7, 185496-185505.
[27] Javaid, N., Shakeel, U., Ahmad, A., Alrajeh, N., Khan, Z. A., &
Guizani, N. (2019). DRADS: depth and reliability aware delay sensitive
cooperative routing for underwater wireless sensor networks. Wireless
Networks, 25(2), 777-789.
[28] Ramezan, G., & Leung, C. (2018). A blockchain-based contractual
routing protocol for the internet of things using smart contracts. Wireless
Communications and Mobile Computing, 2018.
[29] Yang, J., He, S., Xu, Y., Chen, L., & Ren, J. (2019). A trusted routing
scheme using blockchain and reinforcement learning for wireless sensor
networks. Sensors, 19(4), 970.
[30] Liu, Y., Wang, K., Lin, Y., & Xu, W. (2019). LightChain: A Lightweight
Blockchain System for Industrial Internet of Things. IEEE Transactions
on Industrial Informatics, 15(6), 3571-3581.
[31] Uddin, M. A., Stranieri, A., Gondal, I., & Balasurbramanian, V. (2019).
A lightweight blockchain based framework for underwater iot. Electron-
ics, 8(12), 1552.
[32] Danzi, P., Kalør, A. E., Stefanovi´
c, ˇ
C., & Popovski, P. (2019). Delay
and communication tradeoffs for blockchain systems with lightweight IoT
clients. IEEE Internet of Things Journal, 6(2), 2354-2365.
[33] Rovira-Sugranes, A., & Razi, A. (2019). Optimizing the Age of Infor-
mation for Blockchain Technology With Applications to IoT Sensors.
IEEE Communications Letters, 24(1), 183-187.
[34] Liu, M., Yu, F. R., Teng, Y., Leung, V. C., & Song, M. (2018). Computa-
tion offloading and content caching in wireless blockchain networks with
mobile edge computing. IEEE Transactions on Vehicular Technology,
67(11), 11008-11021.
[35] Ren, Y., Liu, Y., Ji, S., Sangaiah, A. K., & Wang, J. (2018). Incentive
mechanism of data storage based on blockchain for wireless sensor
networks. Mobile Information Systems, 2018.
[36] Sergii, K., & Prieto-Castrillo, F. (2018). A rolling blockchain for a
dynamic WSNs in a smart city. arXiv preprint arXiv:1806.11399.
[37] Sharma, P. K., & Park, J. H. (2018). Blockchain based hybrid network
architecture for the smart city. Future Generation Computer Systems, 86,
650-655.
[38] Rathore, S., Kwon, B. W., & Park, J. H. (2019). BlockSecIoTNet:
Blockchain-based decentralized security architecture for IoT network.
Journal of Network and Computer Applications, 143, 167-177.
[39] Jia, B., Zhou, T., Li, W., Liu, Z., & Zhang, J. (2018). A blockchain-
based location privacy protection incentive mechanism in crowd sensing
networks. Sensors, 18(11), 3894.
[40] Feng, H., Wang, W., Chen, B., & Zhang, X. (2020). Evaluation on frozen
shellfish quality by blockchain based multi-sensors monitoring and SVM
algorithm during cold storage. IEEE Access, 8, 54361-54370.
[41] Mori, S. (2018). Secure caching scheme by using blockchain for
information-centric network-based wireless sensor networks. Journal of
Signal Processing, 22(3), 97-108.
[42] Xu, Y., Ren, J., Wang, G., Zhang, C., Yang, J., & Zhang, Y. (2019).
A blockchain-based nonrepudiation network computing service scheme
for industrial IoT. IEEE Transactions on Industrial Informatics, 15(6),
3632-3641.
