A Truthful Incentive Mechanism for Movement-Aware Task Offloading in Crowdsourced Mobile Edge Computing Systems

• Published in: IEEE internet of things journal Vol. 11; no. 10; pp. 18292 - 18305
• Main Authors: Jiang, Changkun, Luo, Zhiheng, Gao, Lin, Li, Jianqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computation offloading; Crowdsourced mobile edge computing (MEC) systems; Crowdsourcing; Delays; device-to-device (D2D); Device-to-device communication; Economics; Edge computing; incentive mechanism design; Mobile computing; movement-aware task offloading; Optimization; Resource allocation; Resource management; Servers; social relationships; Task analysis
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3362406

While computing task offloading in mobile edge computing (MEC) has been extensively studied, existing research has primarily focused on the mobility-awareness arising from opportunistic contact between edge devices. However, in a crowdsourced MEC system, it is essential to incentivize edge users to relocate for offloading tasks to crowdsourced edge devices. This movement-aware task offloading is particularly important for the crowdsourced system operation and has not yet been thoroughly explored. Moreover, the situation becomes more complex when users are socially connected and device-to-device (D2D)-enabled, yet few works have considered these characteristics in combination, particularly from an economic incentive perspective. Therefore, a new incentive framework is needed to analyze the economic issues comprehensively. In this work, we focus on designing a truthful incentive mechanism, where socially-connected D2D users can be incentivized to move around for offloading tasks. To truthfully elicit private information, we model the resource allocation between edge devices and users as a multiseller multibuyer double auction mechanism with realistic MEC constraints, such as delay and storage limitation. Theoretically, we show that the proposed mechanism is computationally efficient and achieves desirable economic properties, including truthfulness, individual rationality, and budget balance. Simulations demonstrate that the mechanism achieves good system efficiency, with a performance improvement of 20% compared to state-of-the-art baselines.