期刊
IEEE ACCESS
卷 9, 期 -, 页码 18649-18669出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3053605
关键词
Location awareness; Clustering algorithms; Global Positioning System; Fires; Energy efficiency; Routing; Monitoring; Bio-inspired algorithm; cluster head; clustering; energy efficiency; gray wolf optimization; localization; network lifetime; routing protocol; unmanned aerial vehicle network
资金
- National Research Foundation of Korea (NRF) - Korean Government (MIST) [2019R1F1A1060501]
- National Research Foundation of Korea [2019R1F1A1060501] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This article presents bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring, which significantly outperform conventional schemes in terms of various performance metrics. The proposed algorithms enhance localization accuracy, clustering efficiency, and data transmission efficiency.
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.
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