Volume 59, Issue 333, September - December 2023

An improved energy-efficient data clustering in UAV-Aided Wireless Sensor Networks for uneven topology

Fagbohunm, Griffin Siji

Department of Computer Engineering Abia State University, Uturu, Abia State, Nigeria

ABSTRACT

It is not uncommon to see sensor nodes deployed in an uneven or hilly terrain. This can be found in many parts of Nigeria. In the same vain, sensor nodes may be deployed in very hostile areas such as in northern parts of Nigeria where insurgents are heavily present. In areas such as those stated above, the use of unmanned aerial vehicles can be used for energy-efficient data collection from the scattered sensor nodes. Unmanned Aerial Vehicles operating at low altitudes can be used to lower the energy consumption of the wireless sensor network by using an intelligent data collection methodology to distribute the UAVs for data collection from the nodes. This paper proposes an energy-efficient and optimized data aggregation (EEODA) scheme in UAV-assisted wireless sensor network for hilly and uneven terrain is designed, using UAVs as data collection points. This can be achieved through the following steps, firstly, a distributed clustering algorithm based on reinforcement learning was proposed to organize the wireless sensor nodes, secondly, a mono-objective simulated annealing search method will be used to efficiently distribute the UAVs for optimum collection of data from the various cluster heads in the network, thirdly the city section mobility model will be used to compute the optimum position for the UAVs to each of the cluster heads in the network. Simulation results show that EEODA scheme proposed in this paper outperforms the EFDC, the closest-performing algorithm to it with an average of 12%. It also outperforms the other two compared algorithms, LEACH with UAV and HEED with UAV with between 17% and 36%, respectively with performance metrics such as energy consumption of nodes, scalability, delay in data aggregation and collection, control overhead and number of dead nodes in each round of clustering.

Keywords: Aggregation, data collection, genetic algorithm, reinforcement algorithm, HEED, Unmanned Aerial vehicle, drone, modified simulated annealing, wireless sensor network.

Discovery, 2023, 59, e114d1355

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DOI: https://doi.org/10.54905/disssi.v59i333.e114d1355

Published: 14 October 2023