Research on Crowd-Sensing Task Assignment Based on Fuzzy Inference PSO Algorithm

Abstract

To solve the problem of load unbalance in the case of few users and multi-task, a fuzzy inference PSO algorithm (FPSO) crowd sensing single objective task assignment method is proposed. With task completion time, user load balancing and perceived cost as the optimization goals, the fuzzy learning algorithm dynamically adjusts the learning factor in the PSO algorithm, so that the PSO algorithm can perform global search in the scope of the task space, thus obtaining the optimal task assignment solution set. Finally, the FPSO algorithm is compared with the PSO, GA and ABC algorithms on the optimization objectives, such as the algorithm convergence, task completion time, perceived cost and load balance. The experimental results show that the FPSO algorithm not only has faster convergence rate than the other algorithms, and shorten the task completion time, reduce the platform’s perceived cost, improve the user’s load balance, and have a good application effect in the crowd sensing task assignment.

RSU Cluster Deployment and Collaboration Storage of IoV Based Blockchain

“…The particle swarm optimization (PSO) algorithm is a general heuristic exploration method used to solve allocation problems [27], similar to GA. The PSO algorithm can perform practical search tasks through feedback and memory.…”