EWGGO: Exponentially Weighted Greylag Goose Optimization for UAV Trajectory Planning in IoT

DOI

10.2991/978-94-6463-718-2_156How to use a DOI?

Keywords

Internet of Things; Trajectory planning; Unmanned Aerial Vehicle; Exponentially Weighted Moving Average; Greylag Goose Optimization

Abstract

Unmanned Aerial Vehicles (UAVs) can be utilized as wireless relays or mobile Base stations (BS) for providing dependable communications and supreme attention for ground devices. UAVs is flexibly employed for enabling fast network access in diverse applications, like disasters, and monitoring. It effectively improves the ability of Internet of Things (IoT) devices through data processing and to levitate the domain to gather data from IoT networks. Nevertheless, limitations like task scheduling and privacy measures, task allocation between UAVs in multi-UAV systems necessitate thorough evaluation and research. To conquer this limitation, a module for trajectory planning in IoT based on UAV is proposed utilizing Exponentially Weighted Greylag Goose Optimization (EWGGO). First, UAV-IoT environment model is simulated and then UAV constraints, like range constraints and collision avoidance between UAVs are considered for trajectory generation. At last, trajectory path generation is accomplished by EWGGO, where the proposed EWGGO is combined by Exponentially Weighted Moving Average (EWMA) and Greylag Goose Optimization (GGO). The evaluation measures like path length, speed, energy and fitness achieved 11.568, 23.307 m/s, 0.727 J and 0.803.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Anand Umarji
AU  - Dharamendra Chouhan
PY  - 2025
DA  - 2025/05/23
TI  - EWGGO: Exponentially Weighted Greylag Goose Optimization for UAV Trajectory Planning in IoT
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1893
EP  - 1904
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_156
DO  - 10.2991/978-94-6463-718-2_156
ID  - Umarji2025
ER  -