Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Abstract

In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

“…The results of their simulation showed that the average amount of energy usage in FECR protocol could be reduced by 9% and by 8% in FEAR. Arafat et al [77] hinted an energy-efficient three-dimensional bioinspired localization algorithm based on the hybrid gray wolf optimization method. They claimed tha their method reduced localization errors, avoided flip ambiguity in bounded distance measurement errors, and achieved high localization accuracy.…”

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…When the wildfire spreads over much larger scales, different measurements (e.g., temperature, wind) collected by a swarm of UAVs are fused within advanced filtering approaches (e.g., Kalman-based) that include wildfire propagation models such as the Rothermel or the Canadian forest fire behavior [169,170]. In addition to data processing, suitable distributed control frameworks need to be devised to design time-varying trajectories that enable a close monitoring of wildfires through multiple coordinated UAVs while minimizing the risk of in-flight collisions or damages, as well as to reduce the total number of transmissions toward the ground control sta-tions [171,172]. Based on these advanced control schemes, some proactive approaches have started to appear in the literature that exploit the payload of UAVs to drop fire retardants or extinguishing agents at the epicenter of the wildfire [173].…”

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

“…They are designed on the basis of cognitive behaviour of certain biologically inspired entity e.g., ant, honeybee, firefly, frog, fish, cat, dolphin, etc. The studies that has used swarm intelligence linking with energy efficiency are as follows: Gray-wolf optimization (Arafat et al [91]), Bat algorithm (Cao et al [92]), flocking control scheme using swarm intelligence (Dai et al [93]), firefly mating optimization (Faheem et al [94]), fish algorithm with k-means clustering (Feng et al [95]), multi-swarm optimization (Hasan et al [96]), Harris' Hawk optimization (Houssein et al [97]), particle swarm optimization (Mukherjee et al [98]), Chicken swarm optimization (Osamy et al [99]), reinforcement learning with swarm intelligence (Wei et al [100]). However, different approaches have their own structure of working which is implemented on WSN on different targets of optimization towards energy efficiency.…”