Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays
Jiahui Li, Geng Sun, Lingjie Duan, Qingqing Wu

TL;DR
This paper introduces a multi-objective optimization framework using swarm intelligence to enhance UAV-assisted IoT data harvesting, balancing energy, time, and security considerations efficiently.
Contribution
It proposes a novel multi-objective optimization model for UAV-IoT networks incorporating collaborative beamforming and addresses the NP-hard problem with an improved swarm intelligence algorithm.
Findings
The proposed algorithm outperforms existing swarm intelligence methods.
Collaborative beamforming reduces energy and time costs.
The model effectively balances multiple conflicting objectives.
Abstract
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers…
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Taxonomy
Methodstravel james · Balanced Selection
