Improved compressive sensing protocol with energy- efficient clustering and routing in WSNS

M. Karthik; S. Mohanapriya

Author(s)

M. Karthik Department of Computer Science, Periyar University,Salem, Tamil Nadu,India

S. Mohanapriya Department of Computer Applications, Sona College of Arts and Science,Salem, Tamil Nadu,India

Abstract

In the recent digitalized world, Wireless Sensor Networks (WSNs) are highly deployed for data transfer between sensor nodes through wireless channels. Normally, these sensor nodes have limited power resources and so the use of these resources has become the most challenging process during Data Aggregation (DA). To avoid this condition, a Power-aware Clustering and Routing with Compressive sensing Protocol (PCRCP) has been recommended for balancing the power use during transferring and aggregating the data between Cluster Head (CH) and Base Station (BS). In this protocol, Compressed Sensing (CS) method is proposed for aggregating the data from CHs with the aid of Forwarder Node (FN). However, still DA process has high data transmission cost and number of measurements. Therefore in this article, a Poweraware Clustering and Routing with Improved Compressive sensing Protocol (PCRICP) is suggested to ensure the energy efficiency of DA in WSNs. Primarily, a coalition formation-based CS solution is proposed that utilizes the signal's sparsity distribution for assembling nodes into many coalitions and the CS is executed inside each coalition. Also, a 2-stage Belief Propagation (BP)-based restoration strategy is applied to achieve an acceptable data quality during DA process. This BP algorithm is an iterative data transfer method which determines the marginal distribution or discovers the Most Probable Assignment (MPA) in the Bayesian networks. On the other hand, it has a convergence problem and the design accuracy is differed with the graph cyclicity. So, an improved generalized BP-based algorithm is proposed that can guarantee better convergence in Markov Random Fields (MRFs). In this algorithm, a caching method and chessboard transitory policy are employed to speed-up the convergence. Also, the computational difficulty of group information from quadric to cubic is reduced. Finally, the simulation results exhibit that the PCRICP achieves superior effectiveness than the PCRCP in terms of different network metrics.

Publication Details

Page(s) 209-218

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 17, Issue: 2, Year: 2022

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Keywords

clustering, Data aggregation, WSN, coalition formation, data Aggregation, Routing, Belief Propagation, data aggregation, markov random fields, PCRCP

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