CSSA-based collaborative optimization recommendation of users in mobile crowdsensing

With the large-scale popularization of mobile terminals, crowd sensing technology has gradually replaced the existing static sensors with its efficient and low-cost advantages as an emerging data collection method. How to quickly allocate the sensing task to the optimal execution user under the premise of ensuring the perceived quality and reducing the cost is the focus of the research. In this regard, this paper proposes a Crowd sensing Sparrow Search Algorithm (CSSA) collaborative optimization recommendation method that combines fitness priority, collaboration, and intelligent optimization algorithms, and uses it for task allocation problems. Firstly, the concept of fitness is proposed to calculate the location, power, equipment and reputation of the perceived user, and analyze the matching degree of the user to the task. Secondly, according to the different fitness, the user is divided into explorers and followers, and the two cooperate to complete the perception task. Thirdly, in the process of solving the optimal task allocation scheme, CSSA intelligent optimization algorithm is used to simulate the process of users completing tasks, and the selected user results can be obtained after limited iterations. Through the comparative experiments of the proposed algorithm and other optimization algorithms in the same environment, the results show that it has higher performance in solving the task allocation problem.