Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs
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Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs Ahmed Aziz1,4
•
Walid Osamy1,5 • Ahmed M. Khedr2,6 • Ahmed A. El-Sawy1 • Karan Singh3
Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to combine between compressive sensing (CS) and routing techniques. However, this combination faces many challenges that makes it an attractive point for research. This paper proposes an Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid CS (EMCA-CS) for IoT based heterogeneous wireless sensor networks (WSNs). EMCA-CS efficiently combines between CS and routing protocols to extend the network lifetime and reduces the reconstruction error. EMCA-CS includes the following: a new algorithm to partition the field into various hexagonal cells (clusters) and based on multiple criteria, selects a node from each cluster as cluster head (CH). Each CH will then compress its cluster data using hybrid CS method. Also, a new Grey Wolf based algorithm to create optimal path for CHs to deliver the compressed data to base station (BS) and a CSMO-GWO algorithm to optimize the CS matrix construction process is introduced. Moreover, a new Grey Wolf and reversible Greedy based Reconstruction Algorithm is proposed to recover the actual data. The simulation results indicate that the performance of the proposed work exceeds the existing baseline techniques in terms of prolonging WSN lifetime, reducing the power consumption and reducing normalized mean square error. Keywords Compressive sensing Cluster-based Energy consumption Grey Wolf Optimization Algorithm Network lifetime Internet of Things Routing techniques Wireless sensor networks
1 Introduction
& Ahmed Aziz [email protected] 1
Department of Computer Science, Faculty of Computers and Artificial Intelligence, University of Benha, Benha, Egypt
2
Computer Science Department, University of Sharjah, Sharjah 27272, UAE
3
School of Computer and System Science, Jawaharlal Nehru University, New Delhi, India
4
Faculty of Engineering and Technology, Sharda University, Andijon, Uzbekistan
5
Department of Applied Science, College of Community in Unaizah, Qassim University, P.O. Box 931, Buridah 51931, Unizah, Kingdom of Saudi Arabia
6
Mathematics Department, Zagazig University, Zagazig, Egypt
The major task of sensor nodes in many Internet of Things (IoT) applications, such as Health monitoring systems, is to periodically sense and transmit the data to BS through multi-hop or single-hop transmission. Data communication contributes to a significant amount of energy consumption in WSNs. Thus, a lot of researchers have focused their attention on decreasing the count of data transmissions in WSNs. Compressive Sensing (CS) is an efficient method for signal processing that allows to
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