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Blockchain-Based MIMO AAV-Aided Mobile Edge Computing
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AI
DOI：10.1109/TMC.2025.3649700.png)
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Abstract
En 中文
Autonomous aerial vehicle (AAV)-aided mobile edge computing (MEC) with a blockchain consensus algorithm is a promising solution for addressing mobile offloading of computation-intensive or latency-sensitive tasks with extensive service range, while ensuring the authenticity of the offloading. Existing autonomous aerial vehicle (AAV)-aided MEC studies focus on task offloading or trajectory planning of single-antenna AAVs, missing multiple-input multiple-output (MIMO) AAV systems. In addition, most blockchain-based AAV-aided edge computing studies adopt energy-consuming proof-of-work-based consensus mechanisms, which are unsuitable for energy-limited AAV scenarios. To address the above issues, in this paper, we develop a joint deadline-aware task offloading and AAV trajectory planning scheme, utilizing the effects simulated through an energy-efficient consensus protocol. In order to prevent the exhaustion of AAVs’ energy and ensure the authenticity of decision-making, we propose an energy-based Raft (E-Raft) consensus algorithm, enabling dynamic leader selection through a series of decreasing energy thresholds. Subsequently, we present a computational profit maximization problem to jointly optimize deadline-aware task offloading and the AAV swarm’s trajectory planning. To address this NP-hard problem, we develop an online priority-based task offloading and trajectory selection algorithm, which is performed by the selected leader of the E-Raft consensus in each round. Simulation results demonstrate that our proposed scheme achieves up to 40% performance improvement over other approaches.
Keywords:
Mobile edge computing
AAV
blockchain
task offloading
trajectory planning
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