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

## TL;DR

This paper introduces a new PSO-based framework for optimizing the deployment of edge-enabled UAVs in flying IoT environments to maximize coverage and minimize latency and energy use.

## Contribution

The novel contribution is a resilient distributed Pareto-based PSO framework for edge-UAV deployment optimization in dynamic Internet of Flying Things environments.

## Key findings

- Pareto-PSO achieves the highest throughput and largest coverage envelope in simulations.
- The method shows moderate and scalable convergence times across varying bandwidth and swarm sizes.
- Treating objectives as a vector-valued function improves real-time and energy-aware deployment.

## 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.