AN IMPROVED MULTI-OBJECTIVE GREY WOLF OPTIMIZATION TASK ALLOCATION METHOD IN MOBILE CROWD SENSING FOR SMART AGRICULTURE
DOI:
https://doi.org/10.35631/JISTM.1142016Keywords:
Canal Inspection, Grey Wolf Optimizer, Mobile Crowd Sensing, Multi-objective Optimization, Smart Agriculture, Task AllocationAbstract
Smart agriculture is the key to ensuring China's food and water resource security. However, the operation and maintenance efficiency of the irrigation canal network that spreads across farmlands is constrained by the traditional inefficient inspection methods. Mobile crowd sensing (MCS) offers a new approach for the inspection of farmland water channels. By mobilizing farmers' daily mobile resources, it is expected to achieve low-cost and wide-coverage monitoring. The core of its efficiency lies in the intelligent allocation of sensing tasks. However, most of the existing mobile crowd sensing methods are designed based on general scenarios and have not been fully adapted to the scene characteristics of farmland water channel detection, such as "numerous points, long lines, complex terrain, and hidden problems", making it difficult to effectively coordinate and optimize the two mutually restrictive core goals of platform benefits and total costs for farmers. To solve these problems, this paper proposed a multi-objective optimization task allocation model in mobile crowd sensing for the inspection of smart agricultural canals. In order to solve the proposed model, we designed an improved multi-objective grey wolf optimization algorithm (IMOGWO-SA/D) that integrates simulated annealing (SA) mechanism and decomposition strategy. This algorithm collaboratively optimizes multiple single-objective sub-problems through Chebyshev decomposition and enhances the global search ability with the aid of SA mechanism, thereby effectively balancing convergence and the distribution of solution sets. Through simulation experiments and comparisons with traditional benchmark algorithms, the experimental results show that the algorithm proposed in this paper has significant advantages in convergence, diversity of solution sets and scene adaptability. This article provides innovative methods and technical solutions for building an efficient and low-cost distributed monitoring system for smart agriculture.
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