Region Based Incentive Mechanisms for Utility Maximization in Mobile Crowd Sensing

This paper proposes the Enhanced Utility & Reverse Auction (EURA) framework as an incentive mechanism for mobile crowdsensing. EURA integrates reverse auction principles with utility optimization, forming an innovative region-based strategy that enhances data sensing efficiency and coverage maximization. Through an adaptive bidding model, EURA ensures fair and strategic participant selection, maintaining optimal resource allocation across large-scale sensing networks. EURA optimizes participation by assigning efficiencies based on users’ regions, fostering localized engagement and fair competition across diverse sensing environments. This paper introduces a greedy incentive mechanism called EGAIN (EURA with greedy auction incentive) that dynamically adjusts bid evaluations based on data quality and regional significance, optimizing both competition fairness and efficiency. Additionally, the coverage-aware auction strategy mitigates redundancy while fostering an equitable distribution of sensing responsibilities. A variant model is also proposed called ERAIN (EURA with reputation auction incentive), incorporating reputation-based bid evaluations to further refine selection criteria and strengthen incentive alignment. Performance evaluations demonstrate EURA’s superiority in maximizing utility by 20–50%, boosting participation by 30–50% compared to RADP-VPC, Random, and RADP_EWMA, while effectively minimizing bid exploitation and enabling cost efficient regional sensing, establishing its clear advantage over these existing mechanisms.