Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays
- URL: http://arxiv.org/abs/2308.01511v1
- Date: Thu, 3 Aug 2023 02:49:50 GMT
- Title: Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays
- Authors: Jiahui Li, Geng Sun, Lingjie Duan, Qingqing Wu
- Abstract summary: Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
- Score: 55.736718475856726
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 often increases the energy
and time costs, resulting in a conflict with the minimization of the costs.
Moreover, the importance of these goals may vary relatively in different
applications. Thus, we formulate a multi-objective optimization problem (MOP)
to simultaneously minimize the mission completion time, signal strength towards
the eavesdropper, and total energy cost of the UAVs. We prove that the
formulated MOP is an NP-hard, mixed-variable optimization, and large-scale
optimization problem. Thus, we propose a swarm intelligence-based algorithm to
find a set of candidate solutions with different trade-offs which can meet
various requirements in a low computational complexity. We also show that swarm
intelligence methods need to enhance solution initialization, solution update,
and algorithm parameter update phases when dealing with mixed-variable
optimization and large-scale problems. Simulation results demonstrate the
proposed algorithm outperforms state-of-the-art swarm intelligence algorithms.
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