Adaptive pursuit learning for energy‐efficient target coverage in wireless sensor networks

Ramesh Upreti,Anis Yazidi,Jeevan Kunwar,Ashish Rauniyar,Hårek Haugerud,Paal Engelstad

doi:10.1002/cpe.5975

Abstract

SummaryWith the proliferation of technologies such as wireless sensor networks (WSNs) and the Internet of things (IoT), we are moving towards the era of automation without any human intervention. Sensors are the principal components of the WSNs that bring the idea of IoT into reality. Over the last decade, WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care monitoring, and so on. However, the energy efficiency of the sensor nodes in WSN remains a challenging issue due to the use of a small battery. Moreover, replacing the batteries of the sensor nodes deployed in a hostile environment frequently is not a feasible option. Therefore, intelligent scheduling of the sensor nodes for optimizing its energy‐efficient operation and thereby extending the life‐time of WSN has received a lot of research attention lately. In particular, this article investigates extending the lifetime of the WSN in the context of target coverage problems. To tackle this problem, we propose a scheduling technique for WSN based on a novel concept within the theory of learning automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, that is, either sleep or active, with an aim to cover all targets with the lowest energy cost possible. Our comprehensive experimental testing of the proposed algorithm not only verifies the efficiency of our algorithm, but it also demonstrates its ability to yield a near‐optimal solution. The results are promising, given the low computational footprint of the algorithm.

Highlights

With the explosive development of the Internet of things (IoT) technologies, various applications have surged up such as smart city, smart homes, smart hospitals, smart transportation, and so on.[1,2] We are witnessing the era of complete automation without any human intervention
Distributed approach based LA to maximize the number of barriers and minimize the energy consumption for the border surveillance in wireless sensor networks (WSNs) Partial coverage learning automata algorithm to implement sleep scheduling approaches to minimize the number of active sensors for covering the desired portion of the region of interest LA solution using the highest ratio of the remaining energy
We focused on solving the problem of target coverage in WSNs in a better way and proposed the -continuous learning automata (CLA) algorithm, a novel-solution based on a cluster of sensors and targets using LA to help the sensor determine whether to be active or to sleep autonomously using the LA pursuit concept

Summary

Introduction

With the explosive development of the Internet of things (IoT) technologies, various applications have surged up such as smart city, smart homes, smart hospitals, smart transportation, and so on.[1,2] We are witnessing the era of complete automation without any human intervention. In the field of automata theory, an automaton[45,46,47,48,49] is characterized as a quintuple made out of a set of states, a set of outputs or actions, an input, a function that maps the present state and the input to the following state, and a function that maps a present state (and input) into the current output. Βm} is the set of inputs or feedback to the automaton.

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