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One Step Forward: Towards A Blockchain based Trust Model for WSNs


Abstract and Figures

Nowadays, Wireless Sensor Networks (WSNs) are facing various challenges. Cost efficiency, low energy consumption, reliable data communication between nodes and security are the major challenges in the field of WSNs. On the other hand, blockchain is also a very hot domain in this era. Blockchain has a remedy for some challenges, which are faced by the WSNs, e.g., secure data transactions and trustworthiness, etc. By keeping in mind the security issues, we induce blockchain into the WSNs. In short, we have proposed a trust model to avoid the malicious attacks and keep the transact data using the blockchain property of immutability. Moreover, an enhanced version of Proof of Stack (PoS), i.e., the Proof of Authority (PoA) consensus mechanism is being used to add a new node in the network. Additionally , the smart contract is also written to check the working status of nodes. Simulations are performed in order to record the transaction cost and execution cost.
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One Step Forward: Towards a Blockchain
Based Trust Model for WSNs
Abdul Mateen1, Jawad Tanveer2, Ashrafullah1, Nasir Ali Khan1,
Mubariz Rehman1, and Nadeem Javaid1(B
1COMSATS University Islamabad, Islamabad, Pakistan,,,,
2Sejong University, Seoul, South Korea
Abstract. Nowadays, Wireless Sensor Networks (WSNs) are facing var-
ious challenges. Cost efficiency, low energy consumption, reliable data
communication between nodes and security are the major challenges in
the field of WSNs. On the other hand, blockchain is also a very hot
domain in this era. Blockchain has a remedy for some challenges, which
are faced by the WSNs, e.g., secure data transactions and trustworthi-
ness, etc. By keeping in mind the security issues, we induce blockchain
into the WSNs. In short, we have proposed a trust model to avoid the
malicious attacks and keep the transact data using the blockchain prop-
erty of immutability. Moreover, an enhanced version of Proof of Stack
(PoS), i.e., the Proof of Authority (PoA) consensus mechanism is being
used to add a new node in the network. Additionally, the smart contract
is also written to check the working status of nodes. Simulations are
performed in order to record the transaction cost and execution cost.
Keywords: Blockchain ·Trust model ·Security ·ProofofAuthority
1 Introduction
Blockchain attracts great courtesy of researchers as they have faith in that,
this technology will bring remarkable changes and opportunities to industries.
Blockchain is a very powerful technology for resolving trusted communications
in a decentralized fashion. This technology was introduced back in 2008 and
circulated by the cryptography mailing group. The main strength of blockchain
is in decentralization, which allows direct (peer to peer) transactions. This app-
roach is also used in distributed systems where trust is not needed for nodes
to do transactions. Blockchain adopts many means such as: time stamping, dis-
tributed consensus, data encryption and economic incentives. It is used to solve
the problems having inefficiency, high cost and insecure data storage. In contem-
porary years, the research on blockchain technology is excited to grow quickly
with the rapid acclaim and development of blockchain.
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): 3PGCIC 2019, LNNS 96, pp. 57–69, 2020.
58 A. Mateen et al.
Wireless Sensor Network (WSN) comprises of different type of small sens-
ing devices. These small devices are being used to monitor physical conditions.
Applications of WSNs are smart cities, military purpose, medicine and most com-
monly monitor an environment [1]. Sensing nodes are deployed in the desired area
with desired fashion (may be random or static) for the purpose of monitoring,
detecting and collecting information. It has some challenges, e.g., throughput,
routing, connectivity, void holes, small memory and most importantly security
The threats which are faced by the WSNs majorly come from two sources.
Firstly, the external attacks on the network and secondly, internal nodes of
the network become malicious [2]. Therefore, it is an essential security enigma
for WSNs to have the capability to recognize and exclude internal malicious
nodes. So, how to solve this security issue for sensor nodes becomes a major
challenge. The malicious node problem in WSNs can be avoided by using one
of the following two categories; (1) either propose a secure model or (2) WSN
protocol. In this paper, we adopt the first one and propose a trust model for
WSNs by using the concepts of blockchain.
In [8], Lin et al. proposed a blockchain based solution for Long Range
Wide Area Networks (LoRaWAN). Authors integrate blockchain and LoRaWAN
by considering the crowd sensing and sharing economy. They developed a
LoRaWAN server to solve the problems of trust on private network operators
and lack of network coverage. In this study, a mechanism is proposed to verify
the existence of data at a specific time on a network. Authors in [14]propose
a blockchain based location privacy protection incentive mechanism. Confusion
Mechanism Algorithm (CMA) is introduced to protect the user’s information by
encrypting the received information from the sensors. Blockchain is also there
to further secure the user’s information and issue incentives based on the fre-
quency of participation. Results show that the proposed mechanism increased
the user’s participation largely from 20% in traditional to 80% in the proposed
mode. However, the results obtained may be one-sided as a limited data (100
pieces of data) was collected.
In this paper, we proposed a blockchain based trust model for WSNs. This
model is used to avoid the malicious attacks and performs secure data transfer
from one ordinary sensor node to the sink node. The main contributions of this
paper are as follows:
Trust model is proposed for avoiding the malicious attacks.
A smart contract is composed and simulations are performed using the fol-
lowing tools: Remix IDE, Ganache, MetaMask and MATLAB R2018a.
A comparative analysis table for transaction and execution costs is presented.
Remaining of this paper is proceeded as follows: Sect. 2covers state of the
art work. In Sect. 3, system model of this work is presented. Section4shows the
results of simulations. At the end, Sect.5concludes this work.
One Step Forward: Towards a Blockchain Based Trust Model for WSNs 59
2 Literature Review and Problem Statement
Authors in [35] proposed a blockchain oriented secure service provisioning mech-
anism for the lightweight Internet of Things (IoT) devices. Authors applied smart
contracts to check the validity of acquired services. High throughput and low
latency using consortium-blockchain with Proof of Authority (PoA) is achieved.
Analysis of packaging time, throughput and latency by comparing PoA with
Proof of Work (PoW) is also done in this work. Moreover, the results show that
the proposed scheme guards lightweight devices from untrusted edge service
providers and insecure services.
In [6], it is expected by the researchers that conventional blockchain tech-
nology cannot be effortlessly applied to mobile devices. It is due to the reason
that PoW prerequisites large computational ability and storage volume during
the mining process. For this, authors proposed Mobile Edge Computing (MEC)
enabled wireless blockchain framework in [7]. In which they used stochastic geom-
etry theory and Alternating Direction-Method of Multipliers (ADMM) based
algorithm. The proposed algorithm is also compared with the existing central-
ized solution. Simulation results demonstrate that the proposed algorithm is
Blockchain based incentive mechanism was proposed in [9] for data storage
by the nodes of a WSN. The authors used Provable Data Possession (PDP)
technique instead of PoW to obtain better results. They also applied preserving
hash function to compare the existing data of nodes with the new one. The only
problem with PDP is that it can identify the damaged data on nodes, but is
unable to recover it.
The authors in [10] proposed a data transmission scheme based on the multi-
link concurrent communication tree model. It is to handle the failure nodes
in blockchain. Results show that proposed scheme works effectively for 15% of
failed nodes. However, if this number reaches the 30%, communication time and
delay will increase. In [11], problem of user access control for network optimiza-
tion in a data-intensive application was identified. Proposed solution considers
authenticity of Channel State Information (CSI) using blockchain consensus and
deep learning. Analysis shows that the proposed scheme increases the spectral
Branch based blockchain technology for Intelligent Vehicles (IVs) was pro-
posedin[12]. Branching is done at Locally Dynamic Blockchain (LDB). It is
to handle the large amount of data generated by IVs. While blockchain is used
to keep track of the data generated by IVs and to verify it. Additionally, the
concept of Intelligent Vehicle Trust Point (IVTP) is also introduced to build
the trust. Problem with branching is that duplicate state changes increase with
increasing load.
Orchestration is the automated configuration, coordination and management
of computer systems and softwares. A Blockchain-based Distributed Applications
(DApps) framework for multi domain service orchestration was proposed in [13].
The authors used blockchain, smart contract and DApps to solve the automation
and distributed harmony issues in networking. Results show that it is essential for
60 A. Mateen et al.
the Multi-domain Orchestrators (MdO) blockchain network to be secure. Also,
transaction confirmation time should be well defined for better performance.
At the same, representing smart contract and interpreting it in the proposed
framework is still an open topic to be covered.
In [16], blockchain is integrated with Internet of Vehicles (IoVs) to provide
large and secure data storage. The authors designed multi-blockchain architec-
ture consists of five blockchains according to the different data blocks to be
stored. Results show that this integration provides large and secure data storage.
They achieved high throughput with increasing data, but delay also increases. In
[17], the authors proposed a vehicular network architecture based on blockchain
for smart city. The authors used the blockchain with smart contract. However,
the service providers are not rewarded so they will not provide services effectively.
Some other authors also involve blockchain in their research works [1927]for
multiple purposes.
2.1 Problem Statement
In conventional routing protocols, central authority is required for facilitating
the authentication and identification of every device. The research work in [18]
implements the blockchain in networks for avoiding malicious attacks. Two types
of attacks are considered in this paper: greyhole attacks and blackhole attacks.
However, the performance of the network was gradually decreased and some
unnecessary computations was involved due to the Proof of Work (PoW) consen-
sus algorithm. In current work, a trust model is proposed to avoid the malicious
attacks and provide the security to the sensor networks using the concepts of
blockchain. Moreover, PoW is replaced with PoA to avoid unnecessary compu-
tations, which were involved earlier due to the PoW.
3 Proposed System Model
In this section, the blockchain based system model for malicious node avoidance
in WSNs is presented. The key components for this system model are classified
as follow:
3.1 Ordinary Sensor Nodes
These sensor nodes only monitor the environment and collect the real-time data
and upload this data to the associated sink node.
3.2 Sink Nodes
These nodes perform three major responsibilities; first, data collection from ordi-
nary sensor node; second, new node addition using PoA consensus mechanism
and last, smart contract execution which is published by the main server. Sink
nodes differentiate data on the base of id and location of the ordinary node.
One Step Forward: Towards a Blockchain Based Trust Model for WSNs 61
Fig. 1. Blockchain based system model
Each sink node has its own database consists of hashes to keep the record of
transactions. Every sink node has the ability to communicate with ordinary sen-
sor node, other sink nodes and the main server. Sink node uses the private keys
for accessing the data from the main server.
3.3 Main Server
The main server is also known as endpoint or base station. The major tasks
for base station are to publish the smart contracts, issuance of activity and
processing of sensed data. The main server records each and every transaction
along with sink id and location in its immutable database. This database can
only be accessed by the main server itself or pre-authorized sink nodes.
It can be observed from the Fig. 1that the ordinary nodes are connected with
sink nodes. Every sink node will get the data from ordinary sensor nodes. Sink
nodes can send their data to other sinks as well as to the main server. Where,
a smart contract is implemented on sink nodes and issued by the main server.
Sink nodes can authenticate and blacklist any ordinary sensor node at any time
on the detection of malicious activity. Each sink has a communication record of
its own as well other nodes in its distributed ledger.
In this system model, the validity of data is checked at sink nodes. Noticeable
thing is that access on the main server is only granted to the sink nodes. The
main server checks the working status of sink nodes and ordinary nodes. It can
also remove any node if (1) it is dead or (2) involved in any suspicious activity.
62 A. Mateen et al.
3.3.1 Hash Function
For each transaction, a hash is generated which is called transaction hash. A hash
is just a function which takes the input value and generates the output value.
This output is a deterministic value against the input value. This is mathemat-
ically written as follows:
f(a)=b, (1)
where, ais any input and bis associated output against the a; e.g., hash
value for “hi” in keccak-256 is “7624778dedc75f8b322b9fa1632a610d40b85e106
c7d9bf0e743a9ce291b9c6f”. Hash values are generally ‘irreversible’ which means
that input cannot be figured out by knowing the output except hit and trial
4 Simulation Results
In this section, we discuss the simulation results which are obtained using dif-
ferent tools and the reasons for these results. In Sect. 4.1, simulation tools are
explained. Moreover, results and their reasoning are discussed in Sect.4.2.
4.1 Simulation Tools
In order to take the simulation results, we have used multiple tools. Four tools are
reviewed for developing and testing the smart contract. First, we write a smart
contract on Remix IDE. Second, Ganache is used to show the clear deployment
visualization of the smart contract. Third, to connect the Etherium node with
browser, MetaMask which is the extension of Chrome browser, is used. Fourth,
execution cost and transaction cost of different transactions are obtained from
Remix IDE and later plotted with MATLAB.
Transaction Cost Execution Cost
Cost (gas)
Fig. 2. Network deployment cost
One Step Forward: Towards a Blockchain Based Trust Model for WSNs 63
Table 1. Network deployment cost
Parameter Val u e
Status 0x1 Transaction mined and execution succeed
Transaction hash 0x98933, ..., 75f15
Contract address 0x08970, ..., 659fb
From 0xca35b, ..., a733c
To Clustering.(constructor)
Gas 300000000 gas
Transaction cost 5674465 gas
Execution cost 4272505 gas
Hash 0x98933, ..., 75f15
Input 0x608...40029
Decoded input {}
Decoded output -
Logs []
Valu e 0wei
Current states of Sink nodes Current states of Sink nodes on main server
Cost (gas)
Transaction Cost
Execution Cost
Fig. 3. State checking cost of sink nodes
Sink Node 1 Sink Node 2 Sink Node 3 Sink Node 4 Sink Node 5
Cost (gas)
104Transaction Cost Execution Cost
Fig. 4. Individual state checking cost of sink nodes
64 A. Mateen et al.
Sink Node 1 Sink Node 2 Sink Node 3 Sink Node 4 Sink Node 5
Cost (gas)
104Transaction Cost Execution Cost
Fig. 5. Transaction cost of sink nodes
4.1.1 Remix IDE
Integrated Development Environment (IDE) is an open source tool which allows
you to debug, test and compile smart contract from browser. It helps program-
mers for designing the software and other different tasks relating to software
Table 2. Current active/de-active state of nodes
Parameter Val u e
Transaction hash 0xfbaef, ..., 840f96
From 0xca35b7, ..., fa733c
To Clustering.StateOfSNs() 0xbbf28, ..., 732db
Transaction cost 27886 gas (Cost only applies when called by a contract)
Execution cost 6614 gas (Cost only applies when called by a contract)
Hash 0xfbaef, ..., 840f96
Input 0x38e, ..., fccad
Decoded input {}
Decoded output {“0”: “string: Current state of Sink Node 1 is: 1”
“1”: “string: Current state of Sink Node 2 is: 1”
“2”: “string: Current state of Sink Node 3 is: 0”
“3”: “string: Current state of Sink Node 4 is: 0”
“4”: “string: Current state of Sink Node 5 is: 1”
“5”: “string: State of selected Ordinary Node is: 1”}
Logs []
One Step Forward: Towards a Blockchain Based Trust Model for WSNs 65
Table 3. Comparison of different costs
Function Transaction cost (gas) Execution cost (gas)
Network deployment cost 5674465 4272505
Per transaction cost
Sink Node 1 46325 23837
Sink Node 2 46019 23595
Sink Node 3 46237 23749
Sink Node 4 46193 23705
Sink Node 5 46457 23969
Current state (Active/De-active)
of sink nodes
27886 6614
Current state (Active/De-active)
of sink nodes according to main
27911 6639
Individual state checking cost
Sink Node 1 30427 9155
Sink Node 2 30493 9221
Sink Node 3 31964 10692
Sink Node 4 32118 10846
Sink Node 5 30515 9243
4.1.2 Ganache
Ganache provides a clear visualization of the smart contract deploying trans-
actions. Ganache grants you access to 10 accounts and each having 100 Ethers
for testing purpose. When a transaction or a smart contract is deployed on the
blockchain, Ganache immediately confirms this transaction or smart contract. In
the result of any transaction or smart contract deployment, the transaction log
is increased and Ether is deducted. Each transaction has details of fund trans-
fer, contract creation and contract call, etc. along with the sender’s address and
transaction hash.
4.1.3 MetaMask
MetaMask is a Google Chrome extension which connects the Etherium node
and browser. It allows us to send and receive Ethers from Ganache. For this, the
connection of Ganache wallet with MetaMask is possible by using a private key.
MetaMask is also connected with Remix IDE. When Remix IDE and MetaMask
are connected, Ether will be deducted on each transaction.
4.1.4 MATLAB R2018a
MATLAB R2018a is a comfortable tool for researchers to work within. It is
a multi-dimensional mathematical computing environment used to solve linear
66 A. Mateen et al.
programming problems within seconds. However, we have used this tool for get-
ting plots by passing the values which were obtained by the Remix IDE.
4.2 Simulations Reasoning
In this section, the simulation tools, results and reasoning of these results will be
discussed. Nevertheless, we provide an overview of some important terms which
require for understanding the term gas price and about its calculation.
4.2.1 Execution Cost
Execution cost is a cost which requires the gas as a fuel on the execution of the
functions (code lines). It also requires storage allocation for different variables
as a result of the execution of operations. On the other hand, the transaction
cost requires for sending the data on the blockchain.
4.2.2 Estimating Transaction Cost
The total Ether cost of a transaction is based on the following two factors:
Gas used: it can be defined as total gas consumed by the transaction.
Gas price: it is a price of per unit gas specified in the transaction.
This transaction cost is calculated by the following formula:
Costtotal =Ugas ×Pgas.(2)
Where, Ugas and Pgas represent the gas used in transaction and price specified
for that transaction, respectively.
4.2.3 Why Ether Is Not Used Instead of Gas?
Actually, it is made to decouple the cost of any operation from the market price
of Ether. As we know that the cryptocurrency prices are volatile and Ether also
has no exception. The gas limit for each action is constant and this is the reason
why we use gas instead of Ether.
Simulations for the calculation of execution and transaction costs are per-
formed in Remix IDE. During simulations, it is observed that the transaction
cost is always high with the comparison of execution cost. We perform a cost
analysis for a smart contract using the aforementioned costs in term of gas
consumption. Figure 2shows the transaction and execution costs required for
network deployment. We can see from the Fig. 2that the operation for network
deployment is the most expensive. It can also be seen from the Table 1that the
transaction cost for network deployment is 5674465 gas which is higher than the
execution cost (4272505 gas).
Figure 3depicts the state checking cost of sink nodes. State checking cost
means that the cost which is used for ensuring the active or de-active status of
sink nodes. To check whether the sink nodes are active or not, their status has
One Step Forward: Towards a Blockchain Based Trust Model for WSNs 67
been checked by other sink nodes and then by the main server. This active or de-
active states must be the same in both places. If the status is same then the node
is working perfectly; otherwise, the node is malicious and need to be removed
from the network. This state checking cost is presented in Fig.3. Similarly, Fig. 4
shows the transaction and execution costs for each sink node’s state. Moreover,
status for each node is presented in Fig. 4and this status is cross-verified by
the main server. Nevertheless, notable thing is that the active status of nodes is
represented by “1” and vice versa by “0”.
Transaction and execution costs per transaction are shown in Fig. 5.Wecan
easily observe the difference between both of aforementioned costs. Transaction
cost is always high and execution cost is low. We have already discussed the
reasons for the higher transaction cost than the execution cost.
4.2.4 Comparison Between Transaction and Execution Costs
We execute different functions and calculate the transaction and execution costs
in terms of gas. After that, these costs are plotted using MATLAB. Moreover,
the aforementioned costs for different functions are provided in Tables1and 2,
respectively. Nevertheless, we have also provided with comprehensive comparison
of different costs in Table 3.
5 Conclusion
WSN has the ability to monitor, collect and send data from one place to another
in unorthodox conditions. However, this network has lots off security risks. In this
paper, we come up with the solution for their security concerns by exploiting the
blockchain concepts. So in this paper, we propose a blockchain based trust model
for WSN to communicate with other nodes without having any security risks.
Moreover, simulations are performed to compare the transaction and execution
costs. In future, we will implement blockchain in any state of the art routing
protocol and compare the performance with the original one.
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... Moreover, PAR is another important parameter to check the reliability of the network. According to FIGURE 6, PAR is maximum during initial rounds of the network, however, it decreases continuously after 1100 th round [28]. At the start, the network shows maximum output because at this time all nodes are active and working properly. ...
Full-text available
Wireless Sensor Internet of Things (WSIoTs) face various challenges such as unreliable data communication, less cost efficiency, security issues and high energy consumption due to their deployment in hostile and unattended environments. Moreover, the node's rapid energy dissipation due to the void holes and imbalanced network deployment has a bad impact on the network performance. To overcome the aforementioned issues, a blockchain based trust model for WSIoTs is proposed in this paper. Moreover, the Dijkstra algorithm is used to propose a routing protocol for performing efficient communication between network nodes while simultaneously avoiding void holes between ordinary sensor nodes and a sink node. Furthermore, to provide transparency in the network, all the transactions performed by the nodes are recorded in the blockchain in an immutable manner. Moreover, the Proof of Authority (PoA) consensus algorithm is used to validate and add the transactions in the blocks. Besides, a distributed platform, known as interplanetary file system, is used in WSIoTs for reliable and cost-effective storage. The simulation results show that PoA performs 13% better than proof of work consensus algorithm. The proposed routing protocol and trust model are validated in terms of gas consumption, throughput, nodes' status and energy consumption.
... AODV is a reactive routing protocol that is suitable for dynamic DMSN nature. At the same time, the standard AODV supports conventional routing procedures with lack of security provisions [43,44]. This proposed system enriches the standard AODV protocol with DSA and light weight Advanced Encryption Standard (AES) technique. ...
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Wireless Sensor Networks (WSNs) contain multiple wireless sensor nodes deployed around the geographical locations. The WSN used in military applications need more security and hence the deployment of trustworthy nodes and links in WSN provides more secure data transmission in Decentralized Military Sensor Networks (DMSNs). Moreover, the DMSNs work with different set of significance constraints including higher security requirements. The design of DMSNs targets surveillance tasks, intruder tracking tasks, army resource maintenance tasks and communication security requirements. Therefore, building a secure and dynamic DMSN against multiple threats is a challenging task. In addition, security principles developed for DMSN cause excessive energy consumption. Moreover, DMSN has completely open distributed architecture without having any base stations. Under this situation, the need for effective and secured data communication can be achieved with the help of a secure routing protocol. Block chains are generally used for making secure financial transactions. However, the routing protocols used in DMSN can support autonomous routing transactions from one node to other node. In this situation, block chain enabled routing procedures can ensure the trustworthiness of any data that is forwarded through different sensor nodes. Hence, a new Generative Adversarial Networks (GAN) based Block Chain enabled secured Routing Protocol (GBCRP) is proposed in this paper which authenticates and validates the ongoing routing procedures of DMSN. Moreover, a new intrusion detection system is also proposed in this work using GAN which is deployed in the nodes of the DMSN for enhancing the security of communication. Since block chain based routing protocols do not provide security, the GBCRP works for creating volatile block chains using decentralized authentication principles and effective intrusion detection. The proposed system uses a Fully Decentralized Generative Adversarial Network (FDGAN) for monitoring the secure routing transactions by the development of an intrusion detection system. The results obtained from this work show that the proposed GBCRP providing better secured routing compared to the existing systems with respect to security, energy consumption and routing metrics.
... The results show that it outperforms the existing models in terms of false-positive rates, false-negative rates, and average energy consumption. The authors in [16] proposed a trust management model based on blockchain's immutable property called proof of authority (PoA). According to the model, the sink node adds a new node to the network through the PoA mechanism. ...
Conference Paper
Underwater wireless sensor network (UWSN) has become popular because of its diverse applications and massive improvements in sensing technologies in recent years. However, this sensor network is vulnerable to various types of cyber-attacks due to its inherent characteristics. Among many cyber-attacks, the Sybil attack is one of the fatal attacks and damages the network severely. In this work, we have proposed a blockchain-based Sybil attack detection scheme in UWSN. We have also integrated one of our previous trust model with the blockchain-based method to make it resilient against the attacks detection. We have conducted an experimented in Flask and discussed the implementation with code details.
... The attacking node will then use all the captured information from the victim node to override the authentication checks and decrypt all encoded information. Thus, and to guarantee a high level of [67,70] Malicious nodes isolation Medium/high [68] Secure data sharing Medium/medium [71][72][73] Attacks detection High/medium [74] Base station authentication Medium/high [75] User authentication High/low [76] Faulty data filtering Medium/medium [77] Clustering security Medium/low [78] Access control safety High/medium [79] Black hole and selective forwarding attack prevention Medium/medium [80] Data routing security Medium/low security, a second line of defense is necessary. This second countermeasure represents an intrusion detection system that deals with the detection and the prevention of malicious infiltrations. ...
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Wireless sensor networks are deployed without predefined infrastructure and are generally left unattended. Indeed, the vulnerability of the wireless sensor networks to attacks comes principally from their inherent characteristics. As the data are transmitted over the air, it is very easy for an adversary to spy on traffic. Also, to meet the strict budgetary requirements, the sensor nodes tend to not be tamperproof and thus offer no protection against security attacks. Alongside with these vulnerabilities, the human intervention is always not allowed to deal with adversaries who attempt to compromise the network. Therefore, security systems are mainly needed to secure the network and ensure the protection against security threats. Indeed, cryptographic based systems are generally used to ensure security. However, due to the lack of memory and power (low computing, limited energy reserves) of the sensor nodes, most of these approaches are not suitable. Therefore, providing security while respecting the specific constraints of the sensors, represents one of the most important research issue in wireless sensor networks. Indeed, several studies have been conducted these last decades to propose lightweight and efficient security protocols for wireless sensor networks. In this paper, we review the most leading protocols and classify them based the addressed security issue. Also, we outline the main security constraints and challenges and present the future research directions based on the emerged application fields.
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Underwater Wireless Sensor Network (UWSN) is quite useful in monitoring different tasks including: from instrument monitoring to the climate recording and from pollution control to the prediction of natural disasters, etc. Recently, different routing protocols have been proposed in UWSN to explore the underwater environment for military and scientific purposes. In this regard, traditional transmission approaches increase the transmission overhead, i.e., packets' collision and congestion, which affect reliable data delivery. In addition, replacement of the sensors' battery in the harsh aquatic environment is also a challenging task. Therefore, to avoid the drastic failure of the network and to prolong the lifespan of the network, efficient routing protocols are needed. However, there are some challenges which affect the performance of the network, i.e., high Energy Consumption (EC), high End to End (E2E) delay, low Packet Delivery Ratio (PDR), minimum network lifetime, high probability of void hole occurrence, limited bandwidth and high bit error rate.~Thus, fast, energy efficient, reliable, collision and interference free routing protocols are required to improve the throughput of a network. Therefore, in this thesis, firstly, two routing protocols are proposed namely: Improved GEogrphic Depth Adjustment Routing (Im-GEDAR) and Co-Improved GEographic Depth Adjustment Routing (Co-Im-GEDAR) to maximize the PDR by minimizing the probability of void hole occurrence (with minimum EC). This enhanced PDR is attained by prohibiting the immutable forwarder nodes selection using three parameters including energy, depth and number of neighbor nodes. Moreover, the probability of void hole occurrence is minimized up to 30\% using fixed nodes deployment at different strategic locations in the network. Secondly, two energy efficient routing protocols namely: Shortest Path-Collision avoidance Based Energy-Efficient Routing (SP-CBE2R) protocol and Improved-Collision avoidance Based Energy-Efficient Routing (Im-CBE2R) protocol are proposed. These routing protocols minimize the probability of void hole occurrence, which minimizes the EC and E2E delay. In addition, both proposed routing protocols enhance the PDR and throughput of the network. In both routing protocols, greedy forwarding is opted to forward the data packets. Moving towards Wireless Sensor Networks (WSNs), during the data transmission, maximum energy is consumed in void hole recovery. In addition, location error and nodes' battery consumption are inevitable. Meanwhile, the loss of data packets and more EC degrade the performance of the network, significantly. Thirdly, three energy conservation routing protocols are implemented. These routing protocols are proposed to maximize the network stability (by avoiding void hole). Fourthly, a Proactive routing Approach with Energy efficient Path Selection (PA-EPS-Case I) is proposed to provide interference free communication. The proposed protocol adaptively changes its communication strategy depending on the type of the network, i.e., dense network, partially dense network and sparse network. Similarly, Bellman-Ford Shortest Path-based Routing (BF-SPR-Three) and Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three) protocols are proposed for an efficient, reliable, collision and interference free communication. Afterward, the algorithms for the proposed routing protocols are also presented. Feasible regions for proposed routing protocols using linear programming are also computed for optimal EC and maximum network throughput. Moreover, the scalability of the proposed routing protocols is also analyzed by varying the number of nodes. In the end, extensive simulations have been performed to authenticate the performance of the proposed routing protocol. Meanwhile, comparative analysis is performed with state-of-the-art reactive and proactive routing protocols. The comparative analysis clearly shows that proposed routing protocols namely: Im-GEDAR and Co-Im-GEDAR achieved 21\% higher PDR and minimized 7\% EC than GEographic and opportunistic routing with DA based topology control for communication Recovery (GEDAR). The proposed routing protocols outperformed Transmission Adjustment Neighbor-node Approaching Distinct Energy Efficient Mates (TA-NADEEM) and minimized the void hole occurrence up to 30\%. Meanwhile, Im-CBE2R, SP-CBE2R, HA-ECMAE, HA-ECMAE2H and GTBPS-3H outperformed the counterparts. Furthermore, in PA-EPS-Case I, comparative analysis is performed with two cutting edge routing protocols namely: Weighting Depth and Forwarding Area Division Depth Based Routing (WDFAD-DBR) and Cluster-based WDFAD-DBR (C-DBR). Results demonstrate that proposed protocol achieve 12.64\% higher PDR with 20\% decrease in E2E delay than C-DBR. Furthermore, the proposed routing protocol outperformed C-DBR in terms of packet drop ratio up to 14.29\% with an increase of EC up to 30\%. In the end, comparative analysis of BF-SPR-Three and EP-VIR with benchmarks disclose that the proposed routing protocols outperformed in order to provide efficient path selection and to minimize the void hole occurrence.
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Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end to end delay and maximum energy dissipation. The provisioning of the aforementioned factors has become a challenging task for the research community. In UWSNs, battery consumption is inevitable and has a direct impact on the performance of the network. Most of the time energy dissipates due to the creation of void holes and imbalanced network deployment. In this work, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems due to which lifespan of the network will increase. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform their counterpart schemes. By keeping in mind the emerging security issues in sensor networks, we have proposed a blockchain based trust model for sensor networks to enrich the security of the network. Additionally, this model provides security along with data immutability. We have used a private blockchain because it has all the security features that are necessary for a private sensor network. Moreover, private blockchain cannot be accessed by using the Internet. In the proposed trust model, the Proof of Authority (PoA) consensus algorithm is used due to its low computational power requirement. In PoA consensus mechanism, a group of the validator is selected for adding and maintaining blocks. Moreover, smart contracts are used to validate and transfer cryptocurrency to service providers. In the end, transaction and execution costs are also calculated for each function to testify the network suitability.
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In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to involvement of TTP, such systems lack trust, transparency, security and immutability. To over come these issues, this thesis proposed a blockchain based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, a user is encouraged to register reviews about data by announcing customer incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption and decryption schemes and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real setup. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows least computational time as compared to advanced encryption standard (AES) 128 and 256.
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Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. Decentralized classification fusion problem was the reason to use belief function based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are fallible to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. These challenges require efficient fault detection methods to be used to detect faults in WSNs in a timely manner. We induced four type of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM has given the first best result for the improvement of belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN have provided the second, third, and fourth best results, respectively. Proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics ,i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). In this thesis, the owner of the (Internet of Thing) IoT device can generate revenueby selling IoT device’s data to interested users. However, on the other hand, users do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional data trading systems have many limitations, as they involve third party and lack: decentralization, security and reputation mechanisms. Therefore, in this thesis, we have leveraged the IoTs with blockchain technology to provide trustful data trading through automatic review system for monetizing IoT’s data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. Review system encourages the owner to provide authenticated data and solve the issues regarding data integrity, fake reviews and conflict between entities. Data quality is ensured to users through reviews and ratings about the data, stored in blockchain. To maintain the data integrity, we have used Advanced Encryption Standard (AES)-256 encryption technique to encrypt data. All transactions are secure and payments are automated without any human intervention. Arbitrator entity is responsible to resolve problems between data owner and users. Incentive is provided to users and arbitrator in order to maintain the user participation and honesty. Additionally, Ethereum blockchain system requires gas for every transaction. Simulations are performed for the validation of our system. We have examined our model using three parameters: gas consumption, mining time and encryption time. Simulations show that the proposed methods outperform the existing techniques and give better results for belief function and fault detection in datascience WSNs. Additionally, blockchain based data trading in IoT system requires gas for every transaction. We have examined our model using three parameters: gas consumption, mining time and encryption time.
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Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failure such as software, hardware, and communication failures. Due to the sensor’s limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Convolutional Neural Network (CNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. Likewise, sensors embedded mobile phones are used for the collection of data for some specific task which can effectively save cost and time in Crowd Sensing Network (CSN). The quality of collected data depends on the participation level from all entities of CSN, i.e., service provider, service consumers and data collectors. In comparison with the centralized traditional incentive and reputation mechanisms, we propose a blockchain based incentive and reputation mechanism for CSNs, which mainly consists of three smart contracts. The incentives are used to stimulate the involvement of data collectors and motivate the participants to join the network. Also, the issue of privacy leakage is tackled by using Advanced Encryption Standard (AES128) technique. In addition to that, a reputation system is implemented to tackle the issues like untrustworthiness, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent and reliable data. Furthermore, the results of first scenario are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this thesis, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets and simulations demonstrate that the RF algorithm secures a better rate of fault detection than the rest of the classifiers. Similarly, the second scenario is evaluated through analyzing the gas consumption of all the smart contracts, whereas, the encryption technique is validated through comparing the execution time with base paper technique. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Conference Paper
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Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches, although these approaches are not suitable for IoT devices due to their limited storage and limited computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. With the help of blockchain, data sharing mechanism is also possible. In this paper, we proposed a novel secure service providing mechanism for IoTs by using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The rating and cryptocurrency is given to edge servers. Given rating and incentive is stored in cloud node and updated with respect to time. The smart contract is proposed to check the validity state of the edge server as well as compare and verify the service provided by edge servers. In our proposed system we perform service authentication at cloud layer as well as edge server layer. Moreover, by using Proof of Authority (PoA) consensus mechanism overall performance of our proposed system also enhanced.By experimental analysis it is shown, our proposed model is suitable for resource constrained devices.