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Stability-aware data offloading optimization in edge-based mobile crowdsensing
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AI
DOI：10.1007/s11704-024-40620-6.png)
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Abstract
En 中文
Mobile CrowdSensing (MCS) has become a powerful sensing paradigm for information collection recently. As sensing becomes more complicated, it is beneficial to deploy edge servers between users and the cloud center with a so-called mobile edge computing. Instead of directly offloading the sensing data to the cloud center, mobile users offload the sensing data to the edge servers. Then, the edge server processes and transmits the data to the cloud center in a distributed and parallel manner. It's however critically important to balance cost, such as energy consumption, and the stability of the queues on both mobile users and edge servers. Therefore, to minimize the data offloading cost while maintaining system stability, we should carefully design the sensing data offloading strategy for edge-based crowdsensing. To this end, we formulate a double-queue Lyapunov optimization problem and propose a sensing data offloading strategy. We analyze the upper bounds of the total offloading cost and queue backlog. We further formulate the heterogeneous sensing data problem as the minimum weight bipartite graph matching problem and develop an approach that is based on Kuhn-Munkres algorithm. Finally, we conduct simulations based on three mobility sets. Simulation results show that the proposed techniques outperform several state-of-art algorithms in overall cost, system stability, and other performance metrics.
Keywords:
mobile crowdsensing
mobile edge computing
lyapunov optimization
bipartite graph matching
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