Abstract
In response to the vast and ever-changing task demands of vehicle terminals, the edge-assistant vehicular network (EAVN) supported by the mobile computation offloading (MCO) technic constituted a new paradigm for improving system performance. The existing edge resource trading mechanisms in EAVN were all centralized processing and suffered from several critical drawbacks of the centralized systems, which inspired the research design of distributed trading mechanisms. In this paper, we proposed an efficient distributed reverse combinatorial auction-based trading mechanism under the anti-manipulation check, namely DRCA, to solve the joint multi-task offloading and multi-resource allocation problem in EAVN with overlapping areas, and prevent the participants from manipulating the auction results. We proved that DRCA has achieved the property of faithfulness and analyzed its network complexity. Besides, compared with existing auction-based mechanisms, DRCA could achieve suboptimal social welfare with relatively low system overhead.
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Guo, D., Zhou, Y., Ni, S. (2022). Distributed Anti-manipulation Incentive Mechanism Design for Multi-resource Trading in Edge-Assistant Vehicular Networks. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_3
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