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Blockchain based Authentication and Trust Evaluation Mechanism for Secure Routing in Wireless Sensor Networks


Abstract and Figures

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 performed 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.
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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
Corresponding author:;
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.
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.
This section discusses the literature review of the related
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
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].
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
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
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).
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-
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
L3: Insecure routing
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)
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)
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,
Sensor nodes
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.
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.
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A blockchain-based nonrepudiation network computing service scheme
for industrial IoT. IEEE Transactions on Industrial Informatics, 15(6),
... This proposed model is an extension of our work in [45]. In the proposed blockchain based routing and trust evaluation mechanism, the encrypted information of routing and trust values is transferred from BSs to other nodes in the network. ...
... The proposed work uses a RSA technique to secure and reliably transmit data in the network. While in the work done in [45], does not consider to enhance the security of the transmission data. In the proposed model, initially, the data is sensed by the SNs and sent to the associated ANs. ...
In this paper, an encryption and trust evaluation model is proposed on the basis of blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network's resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which SN's energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest-Shamir-Adleman (RSA), a cryptosystem that provides asymmetric key, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.
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As a decentralized, public, and digital ledger technology in Peer-to-Peer network, blockchain has received much attention from various fields, including finance, healthcare, supply chain, etc. However, some challenges (e.g., scalability, privacy, and security issues) severely affects the wide adoption of blockchain technology. Recently, Intel software guard extensions (SGX), as new trusted computing technologies, have provided a new solution to the above challenges in the blockchain area. Although many studies have focused on using SGX technology to enhance their schemes in the blockchain areas, no comprehensive survey has systematically analyzed and delineated these studies. This article is the first to systematically discuss the application status of SGX in the blockchain area. In this article, we study the scheme designs, advantages, and disadvantages of the existing works using a six-layer hierarchical structure of the blockchain. We also summarize the functions of SGX and formally analyze the advantages and disadvantages of SGX. Finally, we review the remaining challenges and present a list of possible directions for future research.
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Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay ( D E 2 E ). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold ( d t h ) and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying d t h based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.
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The modern wireless sensor network has great impact in the development of various domains of applications. The presence of malicious nodes introduces various threats and challenges to the network services. Different algorithms have been proposed towards the data security but not achieved the expected performance. Towards performance hike, a novel trust aware localized routing and class based dynamic encryption scheme has been presented. The method first discovers the route to reach the destination and transmit the data packet. But the localized nature of each hop in the route estimates the trust measure for each neighbor according to their prior involvement in data transmission and number of retransmission of the same packets with other neighbor, number of successful transmission. By identifying the values of those parameters, the value of trusted data forwarding support (TDFS) is measured. According to the TDFS value of several routes, a route with the specific neighbor only selected for route selection. On the other side, the method maintains and classifies the data being transmitting into number of classes. Further, the method uses different signature and encryption schemes for various classes. The data has been encrypted with class specific scheme and key before transmission. The method generates a block chain where each block contains the part of encrypted data and represented by a hash and pointer to the next block. The same has been reversed to produce original data from the encrypted key. The method introduces higher performance data security and improves the overall network performance.
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Industrial IoT in the advancement of organizations consigns to the next level in order to trace and manage every single activity of their entities. However, the interdependence, implementation and communication among such wireless devices also known as IoT devices that lead to various secrecy and personnel concerns. Even though the use of smart sensors in industries assists and reduces human efforts with the increased quality besides of enhanced production cost. Several attacks may further encountered by various attackers by hacking several sensors/objects/devices activities. In this paper, in order to preserve transparency and secure each and every activity of smart sensors, we have proposed a secure wireless mechanism using Blockchain technology that stores extorted proceedings of each record into number of blocks. Further, the simulation results of proposed blockchain mechanism are executed against various security transmission processes. In addition, the simulated results are scrutinized besides traditional mechanism and verified over certain metrics such as Probability of attack success, ease of attack detection by the system, falsification attack, authentication delay and probabilistic scenarios to appraise the authenticity of IoT devices.
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Extending the sensor life time is one of the most important issues in widespread use of Wireless Sensor Networks (WSNs). The Energy Harvesting (EH) sensors have been proposed to overcome the mentioned problem in recent years. These sensors can harvest their required energy from environment in different methods, resulting in longer life time. We consider a TDMA based Wireless Energy Harvesting Sensor Network (WEHSN) in which the time slot consists of two time intervals; the first one is utilized to absorb energy whereas the second one is used to transmit the sensors’ data. We investigate the energy efficient resource allocation in WEHSN with constraints on time scheduling parameters and transmission power consumption, where an EH sensor is allowed to transmit its data if the amount of its harvested energy is more than the consumption power. We derive the closed form expression for the optimization problem, corresponding to the energy efficiency and convert it to a parametric form, using Dinkelbach method. Then, we solve the new problem using Karush-Kuhn-Tucker (KKT) conditions. The numerical results shows the effectiveness of the proposed method.
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IoT-enable monitoring can provide valuable information for the shellfish quality evaluation during cold storage condition. However, IoT based information storage relies on the centralized platform, it is possible to tamper. In this paper, we establish blockchain based multi-sensors (WSN) monitoring system to collect quality parameters and verify captured information for improving transparency and trust during cold storage. The implementation of the K-means and SVM algorithms were used in quality evaluation applications to classify and predict the quality loss of frozen shellfish. The results show blockchain based WSN monitoring can achieve the dynamic indicators continuous monitoring and ensures the data security and reliability. The proportion of the training set and the test set in the allowable deviation range is 88.89% and 87.17%. The root mean square error (RMSE) of training set and test set are 0.1502 and 0.1793 by SVM model. The performance of the K-means and SVM model has higher accuracy than BP model. This paper could help to reduce the risk of food losses and improve quality and safety management of frozen shellfish during cold storage.
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Wireless Sensor Networks are considered to be among the most rapidly evolving technological domains thanks to the numerous benefits that their usage provides. As a result, from their first appearance until the present day, Wireless Sensor Networks have had a continuously growing range of applications. The purpose of this article is to provide an up-to-date presentation of both traditional and most recent applications of Wireless Sensor Networks and hopefully not only enable the comprehension of this scientific area but also facilitate the perception of novel applications. In order to achieve this goal, the main categories of applications of Wireless Sensor Networks are identified, and characteristic examples of them are studied. Their particular characteristics are explained, while their pros and cons are denoted. Next, a discussion on certain considerations that are related with each one of these specific categories takes place. Finally, concluding remarks are drawn.
In the Internet of Things (IoT), sensor networks form the basis for interactions with the environment and are seeing accelerated development. This chapter introduces the IoT challenges that we are going to examine here. These are challenges that are related to functioning, confidentiality and security. The chapter describes the concepts of authentication and integrity as well as the concepts of reputation and trust. It introduces the authors' contribution, the Blockchain Authentication and Trust Module (BATM) architecture. The chapter presents the notations used the general architecture of the BATM, and describes how BATM aims to respond to authentication needs by specifying the mechanisms that we have implemented. It explores the evaluation of BATM architecture through simulations. The chapter concludes the relevance of BATM with respect to the results obtained and also explains the possible future prospects of this work.
A novel trust-based range-free secure algorithm using blockchain technology is considered in hostile WSNs for localization. The trust values of beacon nodes are evaluated against reputation value, mobility, residual energy and neighbor node list. The blockchain technology is implemented then to share the beacon nodes trust value with neighbor nodes. The highly trustworthy beacon nodes are subsequently elected as a miner for the mining process of blocks so that unknown nodes get information from highly honest beacon nodes to perform the localization process correctly. A set of simulations is conducted to validate the effectiveness of the proposed algorithm compared to the existing one.
Dynamic Wireless sensor networks (DWSNs) as an important means of industrial data collection are a key part of IIoT, where security and reliability are important characteristics of trustworthiness. However, due to dynamics, the security of key management is caused by a nontrusted base station (BS) that is easily targeted. The distribution key management schemes that avianize the role of BS also lead to additional and heavy overhead on sensors. To tackle these issues, in this paper, we propose a blockchain-based secure key management scheme (BC-EKM). First, stake blockchain is constructed based on the hybrid sensor network. In addition, we design a secure cluster formation algorithm and a secure node movement algorithm to implement key management, where stake blockchain as a trust machine replaces the majority functions of the BS. Finally, we conduct security analysis and ample simulations. The results indicate that the BC-EKM scheme is effective and efficient.