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A Survey on Task Allocation in Mobile Crowdsensing for Agricultural Data Collection

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  • Liang, Yan
  • Ahmad , Mohammad Nazir

Abstract

Mobile Crowdsensing (MCS) has become a promising paradigm for large-scale data collection in smart agriculture, making applications from soil monitoring to crop health assessment possible. However, it is a major challenge to efficiently allocate sensing tasks to distributed farmers or autonomous vehicle, which directly affects data quality, cost and overall system efficiency. This article provides a comprehensive overview of task allocation in agricultural MCS systems, ranging from classical allocation methods to game theory and metaheuristics, aimed at addressing the unique constraints of agricultural environments, such as dynamic field conditions and resource limitations. By integrating current research, this survey aims to identify current trends, highlight unresolved challenges, and propose future directions for developing robust and efficient task allocation strategies to fully unleash the potential of MCS in advancing precision agriculture.

Suggested Citation

  • Liang, Yan & Ahmad , Mohammad Nazir, 2025. "A Survey on Task Allocation in Mobile Crowdsensing for Agricultural Data Collection," GBP Proceedings Series, Scientific Open Access Publishing, vol. 17, pages 147-159.
    • Handle: RePEc:axf:gbppsa:v:17:y:2025:i::p:147-159
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