Adaptive TrustBased Framework for Securing and Reducing Cost in LowCost 6LoWPAN Wireless Sensor Networks
Abstract
:1. Introduction
 Propose an automatic security model for wireless sensor networks based on the level of trust between nodes. Building this trust depends on monitoring communications acts in a WSN environment. However, the main objective of this proposal is to provide security for the wireless sensor network as well as reduce its cost as much as possible.
 A new method for CH selection based on trust and other factors has been proposed.
 Suggest an adaptive encryption method based on the trust ratio of neighbouring nodes.
 The results were analysed based on the lifetime of the network and compared with existing algorithms.
2. Background and Related Works
3. Proposed TrustCH Framework
3.1. TrustBased Calculation
 Packet of Transmission Behavior
 2.
 Number of Repetitions
Algorithm 1: The WSN trustbased calculation. 
1. for each i∊ $n$do // all nodes 
2. ${x}^{i}$ represent the normal node 
3. CH represents the Clusterhead node 
4. for each x^{i} ∊ CH do 
5. search x^{i} in CH.T // CH table 
6. Find P${x}^{i}$F () 
7. Find AF(${x}^{i}$) 
8. Calculate (TB(${x}^{i}$)) 
9. Execute Equation (4) 
10. if T.x^{i}.TL is malicious do 
11. Broadcast a warning message to all WSN trusted nodes 
12. Block the malicious node 
13. goto end // line 21 
14. elseif T.x^{i}.TL <> Null do 
15. Execute Equation (5) 
16. Send a message to all trusted WSN nodes and CHs 
17. Do private authentication and encryption 
18. else 
19. T.x^{i}.TL= $TB$ 
20. Send a message to all trusted WSN nodes and CHs 
21. Do private authentication and encryption 
22. endif 
23. endfor 
24. endfor 
 3.
 Security Level
3.2. Dynamic Selection of Cluster Heads
 1
 The radio frequency computing power (P) model for the proposed is based on [21]. As most of the energy consumption of the WSN nodes is caused by the correspondence between them. Moreover, distance affects how much power is consumed for both WSN nodes (transmitter and receiver). The relationship based on the power consumption of the transmitter and receiver with distance and message size is shown in Equation (7) [17].$${P}_{TX}\left(q,d\right)={P}_{TXelec}\left(q\right)\times {P}_{TXamp}\left(q,d\right)$$$${P}_{RX}\left(q\right)={P}_{RXelec}\left(q\right)={P}_{elec}\times q$$
 2
 The WSN node energy threshold $\left(Th\right)$ Since the CH node works centrally to the neighboring WSN nodes, the power consumption and computations will be higher, thus we must keep the node from collapsing by choosing the $Th$ value. If the node energy reaches that value, it will be excluded from running for the CH position. Moreover, if it is also in this position and the amount of energy falls below the $Th$ value, it is also replaced. This factor balances energy consumption between the nodes and maintains the lifetime of the entire network. The $\left(Th\right)$ for each WSN node is calculated in Equation (9) [17].$$Th\left(n\right)={N}_{adj}\times q\times {P}_{elec}+{N}_{adj}\times q\times \left[{P}_{elec}+{\beta}_{fsm}\right]\times {d}^{2}$$
 3
 The average of the set of interactive factors. The number of adjacent nodes $\left(\mathsf{\alpha}\right)$ for each trusted node. The greater the number of WSN nodes close to it (nominated trusted node), the greater the chance of it becoming a CH node. Moreover, the average distances between the candidate node (CH) and the adjacent node will be taken into account, as shown in Equation (10) [17].$$\sigma \left(N\right)=\frac{{{\displaystyle \sum}}_{j=1,j\ne n}^{\mathsf{\alpha}}{d}_{\mathrm{n}\u21e2\mathrm{j}}}{a}$$
4. Implementation and Evaluation
4.1. Simulation and Performance Metrics
 The energy consumed and the remaining energy of the WSN nodes: It depends on the energy difference between different times to know the degree of impact of the proposed algorithm on the energy consumed.
 Network lifetime: The life of the network is based on a measurement of the time period for the first node to reach zero in its amount of remaining power.
 Packet loss: This metric measures the rate at which packets fail to reach their destination when traveling over the network. Packet loss has several factors for its occurrence, but this proposal will refer to link the effect of attacks on its increase and energy consumption.
4.2. Experimental Results
4.3. Threats Resistance
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Refs  Technique  Key Idea  Strengths  Weaknesses 

[19]  Clustering method  Uses two heads in order to balance energy between nodes 


[20]  Clustering method  CH is selected based on various factors 


[33]  Clustering method  CH is selected based on the highest residual energy  Reduce nodes power consumption 

[26]  Automated encryption  Balance nodes security and power 


[29]  lightweight trust algorithm  Monitoring WSN node’s parameters  Support different types of internal attacks 

[34]  Adaptive trustbased  Adjust the security level based on the trust level of nearby vehicles 


[35]  Centric trust framework  Using decision tree classification and artificial neural networks 


[30]  Trust metrics framework  kmeant clusters to categorize interactions as reliable and untrustworthy 


[36]  Trusted and Secure Routing  Cuckoo search algorithm 


[37]  Energy Trust Model  establishing a trustworthy communication pathway 


[38]  Hierarchical trust management  Balanced weight subcluster formation uses Fuzzy Smeans to the CHidentification scheme 


[39]  Trust clustering model  Using GA to choose the appropriate CH 


Notations  Description 

x^{i}  Node number i 
${x}^{j}$  Node number j 
$t$  Time 
$w$  The weight of the node 
$\lambda $  Constant values 
$b$  Block size 
${P}_{TX}$  The power consumed when the node sends the message 
$q$  Message length 
$d$  The distance 
${P}_{TXamp}$  The node transmitter circuit consumption based on signal amplification 
β_{fsm}  Free space model 
β_{trm}  Tworay grounded propagation models 
${P}_{RX}$  The power consumed when the node receives the message 
${P}_{RXelec}$  The power dissipated by the receiving node$\mathrm{in}\mathrm{the}\mathrm{reception}\mathrm{of}q$ 
$Th\left(n\right)$  The power threshold of a node 
${N}_{adj}$  The number of neighboring nodes 
${P}_{elec}$  Power expanded by transmitter and receiver 
$\sigma $  The average of interactive factors 
$n$  Total number of nodes 
$Tn$  Candidate node 
$\partial \left(Tn\right)$  Average distance 
$\mathsf{\omega}$  The average weights 
$a$  The number of adjacent nodes 
${P}_{r}$  Residual energy 
$\partial $  The average distances between the candidate node (Trusted node) and the existing CH nodes 
$c$  Minimum number of host nodes 
${d}_{Th}$  Distance between CH nodes and the host nodes 
$m$  Total number of CHs operating at time (t) 
Parameter  Value 

WSN size  60 m × 120 m 
AP location  X = 30, Y = 90 
n  200 
Node speed  5 meter/second 
q size  64 bytes 
Control message size  25 bytes 
P initial  0.5 J (joule) 
β_{trm}  0.0013 PJ/bit/m^{4} 
β_{fsm}  10 PJ/bit/m^{2} 
P_{elec}  50 nJ/bit 
P_{r}  0.005 J 
P_{b}  0.005 for 16 bits 
d_{T}  87 m 
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Ahmad, R.; Wazirali, R.; AbuAin, T.; Almohamad, T.A. Adaptive TrustBased Framework for Securing and Reducing Cost in LowCost 6LoWPAN Wireless Sensor Networks. Appl. Sci. 2022, 12, 8605. https://doi.org/10.3390/app12178605
Ahmad R, Wazirali R, AbuAin T, Almohamad TA. Adaptive TrustBased Framework for Securing and Reducing Cost in LowCost 6LoWPAN Wireless Sensor Networks. Applied Sciences. 2022; 12(17):8605. https://doi.org/10.3390/app12178605
Chicago/Turabian StyleAhmad, Rami, Raniyah Wazirali, Tarik AbuAin, and Tarik Adnan Almohamad. 2022. "Adaptive TrustBased Framework for Securing and Reducing Cost in LowCost 6LoWPAN Wireless Sensor Networks" Applied Sciences 12, no. 17: 8605. https://doi.org/10.3390/app12178605