A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things

Zerrougui, Sabrina; Zaidi, Sofiane; Calafate, Carlos T.

doi:10.3390/s25216554

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A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things

by

Sabrina Zerrougui

^1,*

,

Sofiane Zaidi

¹

and

Carlos T. Calafate

^2,*

¹

Department of Mathematics and Computer Science, Research Laboratory on Computer Science’s Complex Systems (ReLa(CS)2), University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria

²

Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain

^*

Authors to whom correspondence should be addressed.

Sensors 2025, 25(21), 6554; https://doi.org/10.3390/s25216554 (registering DOI)

Submission received: 14 September 2025 / Revised: 17 October 2025 / Accepted: 22 October 2025 / Published: 24 October 2025

(This article belongs to the Special Issue Wireless Communication Technologies for Internet of Things and Wireless Sensor Networks)

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Abstract

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.

Keywords: particle swarm optimization; UAV deployment; multi-objective optimization; epsilon constraint; Pareto archive; weighted sum; Internet of Flying Things

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MDPI and ACS Style

Zerrougui, S.; Zaidi, S.; Calafate, C.T. A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things. Sensors 2025, 25, 6554. https://doi.org/10.3390/s25216554

AMA Style

Zerrougui S, Zaidi S, Calafate CT. A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things. Sensors. 2025; 25(21):6554. https://doi.org/10.3390/s25216554

Chicago/Turabian Style

Zerrougui, Sabrina, Sofiane Zaidi, and Carlos T. Calafate. 2025. "A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things" Sensors 25, no. 21: 6554. https://doi.org/10.3390/s25216554

APA Style

Zerrougui, S., Zaidi, S., & Calafate, C. T. (2025). A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things. Sensors, 25(21), 6554. https://doi.org/10.3390/s25216554

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things

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