Optimal Sensing Task Distribution Algorithm for Mobile Sensor Networks With Agent Cooperation Relationship

We propose a task distribution algorithm based on agent correlation to enhance the comprehensive research on the distribution sensing tasks in mobile sensor networks. First, in the proposed algorithm, the score and feature factors of the mobile agents are considered comprehensively in a direct correlation model, which is constructed by combining the direct and indirect correlation samples. Second, we introduce a mobility model based on the exponential distribution, and obtain a calculation method of probability parameter λ according to the analysis presented in this article. Finally, we integrate the constructed correlation model and mobility model; which are then applied to the distribution algorithm. By experimental research, the task distribution algorithm based on relationships of agents we proposed, has a precision improvement of 57.23% over MTPS, which is a fine-grained multitask allocation framework algorithm, and 12.31% of that to sequential MF, which is a recommendation algorithm based on collaborative filtering by exploiting sequential behaviors. These results indicate that the proposed algorithm improves the task distribution performance significantly, and offers a more accurate and reliable service.