TRUST-SC: truthful multi-task double auction for quality-aware spatial crowdsourcing in strategic environment

Abstract

Spatial crowdsourcing (SC) enables the assignment of location-based tasks to mobile users who must travel to specific locations to perform sensing or service activities. However, SC systems often operate in strategic environments where both task requesters and task executors possess private valuation information, posing challenges for designing efficient and truthful incentive mechanisms. To address these issues, this paper proposes a truthful multi-task double Auction for quality-aware spatial crowdsourcing (TRUST-SC). The proposed framework adopts a three-tier architecture. First, task executors are grouped into spatial clusters to improve scalability and reduce allocation complexity. Second, reliable executors are identified through a majority–voting–based quality evaluation process. Third, tasks are allocated, and payments are determined through a multi-unit double-auction mechanism that guarantees incentive compatibility and individual rationality. Theoretical analysis and simulation results demonstrate that the TRUST-SC achieves truthfulness, individual rationality, quality task executors determination, and computational efficiency. Simulation also demonstrate the impact of clustering and quality task executors on the discussed set-up.