Task Scheduling and Trajectory Optimization Based on Fairness and Communication Security for Multi-UAV-MEC System

Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurre...

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Published in:IEEE internet of things journal Vol. 11; no. 19; pp. 30510 - 30523
Main Authors: He, Yejun, Xiang, Kun, Cao, Xiaowen, Guizani, Mohsen
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autonomous aerial vehicles
Communication
Communications systems
Cybersecurity
Data transmission
Eavesdropping
Edge computing
Energy consumption
Fairness
Line of sight communication
Mathematical analysis
Mixed integer
Mobile computing
mobile edge computing (MEC)
multiagent deep deterministic policy gradient (MADDPG)
Multiagent systems
multiunmanned aerial vehicles (UAVs)
offloading strategy
Privacy
Resource management
Security
Task analysis
Task scheduling
Three-dimensional displays
Trajectory
Trajectory optimization
transmit power
Unmanned aerial vehicles
ISSN:2327-4662, 2327-4662
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Abstract Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurrently, potential conflicts in their trajectories need to be mitigated. Thus, the 3-D trajectory needs to be properly designed in a highly reliable manner. Besides, such an infrastructure-free communication paradigm also exposes a potential risk of misuse by malicious parties, which allows them to eavesdrop on private communications, posing a threat to the security and privacy. Therefore, we consider a multi-UAV-assisted MEC communication system, where a UAV maliciously eavesdrops on the data transmission from the user devices (UDs) while a jammer is deployed on the ground to interfere with the eavesdropping channel. In specific, our objective is to minimize the energy consumption and latency while incorporating fairness metrics by optimizing the 3-D trajectories of UMs, transmission power of UDs, and the offloading strategies under the constraints of ensuring communication security and load fairness. Given the complexity of this mixed-integer nonconvex programming problem, we decompose the formulated problem into three subproblems. Specifically, at each time slot, we optimize the transmit power and offloading strategies using theoretical derivation and mathematical analysis, respectively. Additionally, a multiagent deep deterministic policy gradient (MADDPG) algorithm is employed to optimize the trajectories of UMs. Simulation results demonstrate that our proposed joint optimization algorithm successfully minimizes the system energy consumption and delay as compared to benchmarking schemes.
AbstractList Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurrently, potential conflicts in their trajectories need to be mitigated. Thus, the 3-D trajectory needs to be properly designed in a highly reliable manner. Besides, such an infrastructure-free communication paradigm also exposes a potential risk of misuse by malicious parties, which allows them to eavesdrop on private communications, posing a threat to the security and privacy. Therefore, we consider a multi-UAV-assisted MEC communication system, where a UAV maliciously eavesdrops on the data transmission from the user devices (UDs) while a jammer is deployed on the ground to interfere with the eavesdropping channel. In specific, our objective is to minimize the energy consumption and latency while incorporating fairness metrics by optimizing the 3-D trajectories of UMs, transmission power of UDs, and the offloading strategies under the constraints of ensuring communication security and load fairness. Given the complexity of this mixed-integer nonconvex programming problem, we decompose the formulated problem into three subproblems. Specifically, at each time slot, we optimize the transmit power and offloading strategies using theoretical derivation and mathematical analysis, respectively. Additionally, a multiagent deep deterministic policy gradient (MADDPG) algorithm is employed to optimize the trajectories of UMs. Simulation results demonstrate that our proposed joint optimization algorithm successfully minimizes the system energy consumption and delay as compared to benchmarking schemes.
Author Xiang, Kun
Guizani, Mohsen
He, Yejun
Cao, Xiaowen
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Snippet Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their...
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SubjectTerms Algorithms
Autonomous aerial vehicles
Communication
Communications systems
Cybersecurity
Data transmission
Eavesdropping
Edge computing
Energy consumption
Fairness
Line of sight communication
Mathematical analysis
Mixed integer
Mobile computing
mobile edge computing (MEC)
multiagent deep deterministic policy gradient (MADDPG)
Multiagent systems
multiunmanned aerial vehicles (UAVs)
offloading strategy
Privacy
Resource management
Security
Task analysis
Task scheduling
Three-dimensional displays
Trajectory
Trajectory optimization
transmit power
Unmanned aerial vehicles
Title Task Scheduling and Trajectory Optimization Based on Fairness and Communication Security for Multi-UAV-MEC System
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