The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surv...The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance.Our research focuses on a specific type of spatial crowdsourcing that involves ongoing,collaborative efforts for continuous spatial data acquisition.However,due to limited budgets and workforce availability,the collected data often lacks completeness,posing a data deficiency problem.To address this,we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution.We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision.Building on this,we explore a quality-aware task assignment method,corresponding to spatiotemporal assignment strategies.Since the assignment problem is NP-hard,we develop a polynomial-time algorithm with the guaranteed approximation ratio.Novel indexing and pruning techniques are proposed to further enhance performance.Extensive experiments conducted on datasets validate the effectiveness of our methods.展开更多
文摘The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance.Our research focuses on a specific type of spatial crowdsourcing that involves ongoing,collaborative efforts for continuous spatial data acquisition.However,due to limited budgets and workforce availability,the collected data often lacks completeness,posing a data deficiency problem.To address this,we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution.We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision.Building on this,we explore a quality-aware task assignment method,corresponding to spatiotemporal assignment strategies.Since the assignment problem is NP-hard,we develop a polynomial-time algorithm with the guaranteed approximation ratio.Novel indexing and pruning techniques are proposed to further enhance performance.Extensive experiments conducted on datasets validate the effectiveness of our methods.
