Task Allocation in Mobile Crowdsensing for Smart Agriculture: A Survey

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. By synthesizing existing research, this survey summarizes current trends, identifies unresolved issues, and proposes future directions for developing more robust task allocation mechanisms, with the goal of fully leveraging the potential of MCS in precision agriculture.

• Publication year

  2025

• Venue

  Lecture Notes in Education, Arts, Management and Social Science

• Publication date

  2025-11-26

• Fields of study

  Not labeled

• Identifiers
  DOI 10.18063/lne.v3i10.1095
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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