Task Allocation in Mobile Crowdsensing for Smart Agriculture: A Survey MT
作者: Yan Liang; Mohammad Nazir Ahmad
Abstract / 摘要：Mobile Crowdsensing (MCS) serves as a scalable data acquisition paradigm for smart agriculture, supporting a range of applications from soil monitoring to crop assessment. A core challenge lies in efficiently allocating sensing tasks to distributed participants (e.g., farmers), which directly impacts task execution efficiency, cost control, and overall system effectiveness. This paper provides a systematic review of MCS task allocation methods for agricultural scenarios, covering classical greedy strategies, evolutionary algorithms, and others, aimed at addressing practical constraints such as dynamic field environments and limited resources——