A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks

TL;DR

This paper introduces LAML, a learning automata-based scheduling algorithm for wireless sensor networks that enhances target coverage and significantly prolongs network lifetime through efficient node activity management.

Contribution

It presents a novel scheduling method using learning automata for sensor nodes, improving network lifetime and coverage in remote deployment scenarios.

Findings

LAML extends network lifetime more effectively than existing methods.

The algorithm achieves better coverage with reduced energy consumption.

Simulation results validate the efficiency of the proposed approach.

Abstract

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this pa-per, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the pro-posed scheduling method can better prolong the lifetime of the network in comparison to similar existing method.