Abstract
This paper proposes optimal clustering (OC) for magnetic induction (MI) communication-based 3-D Non-Conventional Wireless Sensor Networks (3-D Non-Conv WSNs) leveraging compressive sensing (CS) and principal component analysis (PCA) with and without consideration of relay node. These WSNs are resource constrained with limited energy reserves. OC for 3-D media is performed using analytical modeling to minimize the energy consumption in the network. Clustering efficacy is further improved by applying the CS and PCA data compression techniques. The performance of the proposed model is evaluated in terms of energy efficiency and network lifetime for three different media (viz., sea water, dry soil, and sedimentary wet rock) by considering three different positions of base station (BS) (viz., center, lateral mid point, and outside of sensing field). Furthermore, from the results, we observed that our proposed techniques save energy up to \text{84.37}{\%} for all base station (BS) positions.
Original language | English |
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Article number | 8734782 |
Pages (from-to) | 2585-2588 |
Number of pages | 4 |
Journal | IEEE Systems Journal |
Volume | 14 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2020 |
Keywords
- Compressive sensing (CS)
- magnetic induction (MI) communication
- optimal clustering (OC)
- principal component analysis (PCA)
- three-dimensional Non-Conventional WSNs (3-D Non-Conv WSNs)
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering