An altitude-aware fuzzy approach for energy efficiency in UAV-assisted 3D Wireless Sensor Networks

Abstract

In Wireless Sensor Networks (WSNs) that use multi-hop topologies, issues like energy holes and hotspots have become prominent. To address these, recent research has proposed using mobile sinks with abundant resources. These include mobile robots, drones, and notably, Unmanned Aerial Vehicles (UAVs), as solutions to alleviate these challenges. This paper introduces a novel altitude-aware fuzzy approach aimed at improving energy efficiency in UAV-supported 3D WSNs. The proposed methodology comprises two key components. Firstly, a tailored fuzzy clustering algorithm is developed to manage the spatial structure of the 3D WSN, optimizing energy consumption. Secondly, a hybrid grey wolf optimization algorithm is utilized to fine-tune the parameters of the fuzzy clustering algorithm, ensuring optimal performance. The synergistic and seamless integration of these components addresses the energy efficiency challenges inherent in UAV-assisted 3D WSNs. The significance of this approach lies in its capacity to navigate the escalating complexity and energy demands of modern sensor networks, offering a harmonious blend of theoretical innovation and practical applicability. Experimental analysis and results substantiate the superior performance of the proposed approach compared to existing solutions, as measured by the metrics commonly employed to evaluate the network lifetime of protocols in the literature.