Enhancing mobile crowd sensing with targeted regional incentives

This is the authors' abstract. We don't add key points for this paper.

Efficient budget allocation is essential for sustaining mobile crowdsensing systems (MCS), where resource distribution must balance cost-effectiveness and participant motivation to ensure optimal task execution. This paper introduces two novel winner selection mechanisms designed to maximize system rewards while adhering to financial constraints: a) Targeted Regional Incentive (TRI), and b) TRI with Critical Value (TRI-CV). The proposed incentive mechanisms are structured through regional divisions within the area of interest for effective task allocation. TRI employs a computationally efficient, low-overlap prioritization strategy that supports real-time task allocation across spatial regions. TRI-CV builds on this foundation by introducing region-specific critical value thresholds and a lightweight audit reassignment step, which reallocates budget from overrepresented to underserved regions to enhance spatial coverage and fairness. Simulation results show that TRI improves platform utility by 45–50% and reduces auction costs by 35–36%, while TRI-CV achieves 50–58% higher utility and 40–42% lower costs compared to established mechanisms such as FIRE, GRUS, and BFM. Additionally, TRI-CV demonstrates superior participant retention and scalability, positioning it as a robust and fairness-aware solution for dynamic Mobile Crowd Sensing environments.