Joint UAV deployment, SF placement, and collaborative task scheduling in heterogeneous multi‐UAV‐empowered edge intelligence

To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. Existing CPU‐oriented task scheduling algo...

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Veröffentlicht in:IET communications Jg. 17; H. 5; S. 641 - 657
Hauptverfasser: Wang, Yangang, Wei, Xianglin, Wang, Hai, Fan, Jianhua, Chen, Juan, Zhao, Kuang, Hu, Yongyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage John Wiley & Sons, Inc 01.03.2023
Wiley
Schlagworte:
Algorithms
Artificial intelligence
Central processing units
Collaboration
CPUs
Decision making
Edge computing
Energy consumption
Evolutionary algorithms
Evolutionary computation
Field programmable gate arrays
Greedy algorithms
Heuristic
iterative methods
Linear programming
Microprocessors
Mixed integer
mobile computing
non‐linear programming
Optimization
Placement
Pricing policies
Response time
Scheduling
Software
Task scheduling
Unmanned aerial vehicles
ISSN:1751-8628, 1751-8636
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Zusammenfassung:To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. Existing CPU‐oriented task scheduling algorithm cannot apply to the CPU+CP heterogeneous architecture. In this backdrop, this paper first formulates the joint service function placement, collaborative task scheduling, UAV deployment, and MD position determination problem as a Mixed Integer Non‐Linear Programming problem. Then, an alternating optimization‐based algorithm is put forward to derive a sub‐optimal solution of the problem utilizing Differential Evolution and Greedy‐based Hungarian algorithms. A series of experiments are conducted to evaluate the performance of the proposal. Results show that authors' proposal can achieve an overall revenue that is roughly 50% higher than those of existing methods. To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. In this backdrop, this paper first formulates the joint service function (SF) placement, collaborative task scheduling, UAV deployment, and MD position determination problem as a Mixed Integer Non‐Linear Programming (MINLP) problem. Then, an alternating optimization‐based algorithm is put forward to derive a sub‐optimal solution of the problem utilizing Differential Evolution (DE) and Greedy‐based Hungarian algorithms.
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ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12570