Particle Swarm Optimization towards Data Collection in the Internet of Things with a Mobile Sink

Aiming at the characteristic that the data collection performance of energy-limited Internet of things is directly affected by its energy efficiency level, an energy optimization method of Internet of things sensing layer nodes based on particle swarm optimization was proposed. Combining location deployment of data aggregation nodes and path planning of mobile sink nodes, the simulation model for energy efficiency optimization was constructed. In this model, the node coverage rate is used as the evaluation function to calculate the deployment position of the data aggregation node by using particle swarm algorithm. Next, the energy cost function is established on account of the energy consumption of data communication and the energy consumption of sink nodes. And it was applied to the particle swarm algorithm improved by the simulated annealing algorithm to plan the movement path of sink nodes. Final simulation results show the feasibility and effectiveness of the method, which not only maintains the integrity of the network, but also meets the actual requirements for reducing energy consumption.