Essence of Blockchain Technology in Wireless Sensor Network: A brief study

Verma, Sandeep; Kaur, Satnam; Manchanda, Rachit; Pant, Diwaker

doi:10.1109/icaccm50413.2020.9212970

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…These models are appropriate for minimizing complete network communication cost. On the other hand, it also recognizes the grouping of routing principles which possesses data aggregation characteristics that tend to increase network's effectiveness [6]. This method considers 'power efficiency' as a basic demand due to power restraints in nodes.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Pigeon Inspired Optimization Technique for Clustered Wireless Sensor Networks

Hilal¹,

Hashim²,

Elkamchouchi³

et al. 2022

Computers, Materials &Amp; Continua

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Wireless Sensor Networks (WSN) interlink numerous Sensor Nodes (SN) to support Internet of Things (loT) services. But the data gathered from SNs can be divulged, tempered, and forged. Conventional WSN data processes manage the data in a centralized format at terminal gadgets. These devices are prone to attacks and the security of systems can get compromised. Blockchain is a distributed and decentralized technique that has the ability to handle security issues in WSN. The security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network system. This breaches the mutual trust and allows data immutability which in turn permits the network to go on. At some instances, few nodes die or get compromised due to heavy power utilization. The current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB technique. The primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain (BC) for inter-cluster communication in the network. To select Cluster Heads (CHs) and organize the clusters, the presented EACPIO-CB model designs a fitness function that involves distinct input parameters. Further, BC technology enables the communication between one CH and the other and with the Base Station (BS) in the network. The authors conducted comprehensive set of simulations and the outcomes were investigated under different aspects. The 6548 CMC, 2022, vol.73, no.3 simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.

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Section: Introductionmentioning

confidence: 99%