Truthful reverse auction-based incentive mechanisms for task offloading in mobile edge computing

Abstract

Offloading computation-intensive tasks of mobile devices (MDs) to the nearby base stations (BSs) equipped with edge servers has been a promising approach to effectively alleviate the problem of insufficient computation capacity of MDs. Nevertheless, task offloading needs to consume numerous resources (e.g., computation and communication resources, etc.). Assumed that the owners of BSs are selfish and rational, they will be reluctant to take part in the task offloading without acquiring suitable economic reimbursement. Therefore, it is necessary to design an effective incentive mechanism to motivate BSs to engage in the process of task offloading. In this paper, we present a truthful reverse auction-based incentive mechanism (TRAIM) with explicit consideration of the locality constraint, overlapped coverage and capacity constraint to offload tasks remotely. To be specific, we first devise the MD assignment based on the dynamic programming approach to acquire the set of optimal BS-MD associations. Next, we devise the winning BS selection using the greedy method to decide the set of winning BSs. Then, we compute the charge of each winning BS according to its critical value, aiming to prevent the strategic behaviors. Strict theoretical analysis demonstrates our proposed TRAIM is truthful, individually rational and computationally efficient. Extensive simulations also demonstrate the effectiveness of our proposed TRAIM.