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Abstract
IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in such networks suffer from the perturbing concern of limited energy resources, restricting their potential in the successful detection of wildfire. To extenuate this concern, we propose an intelligent framework, Sleep scheduling-based Energy Optimized Framework (SEOF), that works in two folds. Firstly, we propose an energy-efficient Cluster Head (CH) selection employing a recently developed meta-heuristic method, Tunicate Swarm Algorithm (TSA), that optimizes the five novel fitness parameters by integrating them into its weighted fitness function. Secondly, we perform a sleep scheduling of closely-located sensor nodes based on the distance threshold calculated through a set of experiments. Sleep scheduling methodology plays a pivotal role in abating the number of data transmissions in SEOF. Finally, we simulate SEOF in MATLAB under different scenarios to examine its efficacy for the various performance metrics and scalability features. Our empirical results prove that SEOF has ameliorated the network stability period for two different scenarios of network parameters by 35.3% and 216% vis-a-vis CIRP.
Highlights
With the evolution of sensing technology, IoT-based Wireless Sensor Network (WSN) have been proliferating in handling multifaceted applications [1]
The performance evaluation of scheduling-based Energy Optimized Framework (SEOF) is done against the Cluster-based Intelligent Routing Protocol (CIRP) [35], Fuzzy Logic-based Effective Clustering (FLEC) [38], Genetic Algorithm-based Optimized Clustering (GAOC) [5], and eeTMFOGA [37]
WORK We propose an intelligent framework namely, SEOF to address the concerns related to the limited energy resources of IoT-based sensor nodes that are deployed in the forest covers for Wildfire Detection (WD)
Summary
With the evolution of sensing technology, IoT-based WSNs have been proliferating in handling multifaceted applications [1]. It is evident that the former method fails in adverse environmental conditions We use the latter approach for WD while considering the energy efficiency of the IoT-based sensor nodes. 1) We propose an intelligent framework named as Sleep scheduling-based Energy Optimized Framework (SEOF) pertinent to WD that uses IoT-based sensor nodes. 3) We employ four DCS around the network periphery to extenuate the concern of hot-spot problem and early data delivery to a sink in large area networks It eradicates the aforementioned problem by introducing single-hop communication between the sensor nodes and the DCS. We use several crucial performance metrics that include stability period, network survival period, network’s remaining energy and throughput to evaluate the efficacy of SEOF This is the first ever work for WD that incorporates multiple DCS to immune the network from the hot-spot problem, and expedite the data delivery through optimized CH selection.
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