Open Access

ARTICLE

AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

Syed Kamran Haider^1,2, Abbas Ahmed², Noman Mujeeb Khan², Ali Nauman^3,*, Sung Won Kim^3,*

1 College of Internet of Things (IoT) Engineering, Hohai University, Changzhou, 213001, China
2 Department of Electrical and Electronics Engineering, Beaconhouse International College, Islamabad, 44000, Pakistan
3 School of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38428, Republic of Korea

* Corresponding Authors: Ali Nauman. Email: ; Sung Won Kim. Email:

(This article belongs to the Special Issue: AI-Assisted Energy Harvesting Techniques and its Applications in Wireless Sensor Networks)

Computers, Materials & Continua 2024, 80(3), 4085-4110. https://doi.org/10.32604/cmc.2024.052997

Received 22 April 2024; Accepted 19 July 2024; Issue published 12 September 2024

Abstract

In recent advancements within wireless sensor networks (WSN), the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality. This research introduces a sophisticated framework, driven by computational intelligence, that merges clustering techniques with UAV mobility to refine routing strategies in WSNs. The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads (CHs). This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination. Employing a greedy algorithm for inter-cluster dialogue, our framework orchestrates CHs into an efficient communication chain. Through comparative analysis, the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing (CCMAR) and the energy-efficient cluster-based dynamic algorithms (ECCRA). Specifically, it showcases an impressive 15% increase in energy conservation and a 20% reduction in data transmission time, highlighting its advanced performance. Furthermore, this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN, emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

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Keywords

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