Vehicular crowdsensing employs vehicles as mobile sensing nodes to collect road environmental information and process the collected data. To perform the delay-tolerant crowdsensing tasks in convenience, vehicles with computational demands can offload the data process tasks to a proximal edge server (ES) in a probabilistic manner after entering a parking lot. The ES determines how to price the offloading services to maximize the expected total revenue, causing a joint probabilistic data offloading and service pricing problem between the vehicles and ES. To address the problem, we adopt a Stackelberg game approach to study the interaction between them. Specifically, the ES plays as the leader to determine the uniform price for all offloading vehicles, and to equally allocate the computing resource among them. The vehicles play as the followers to optimize their offloading probabilities to minimize the expected weighted sum of task delay, energy consumption and service fee. We employ the backward induction method to analyze the unique Stackelberg equilibrium. Subsequently, a distributed algorithm is designed to reach the Stackelberg equilibrium without prior knowledge of the vehicles. Numerical results demonstrate that compared with the baseline schemes, our scheme has an advantage in improving the economic benefits of the ES.
A Probabilistic Data Offloading and Pricing Mechanism Based on Stackelberg Game for Vehicular Crowdsensing
2024-06-24
471983 byte
Conference paper
Electronic Resource
English
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