Distributed data acquisition optimization algorithm for wireless sensor networks

With the rapid development of applications such as the Internet of Things and intelligent transportation, wireless sensor networks play an important role in data collection and environmental monitoring. However, wireless sensor networks face low efficiency and high energy consumption in distributed data collection and node configuration. In this context, a sensor node configuration optimization algorithm based on an improved sparrow search algorithm by introducing reverse elite selection, dynamic perturbation, and dynamic warning update strategies is proposed. Secondly, a virtual grid partitioning strategy is designed, and a distributed data collection and transmission optimization algorithm is proposed. The node configuration algorithm achieved the most uniform distribution of nodes in simulation testing and almost achieved complete region coverage. Under 30% node failure, its network coverage rate was 83.5%. When the packet size was 1000kb, the data transmission rate and average communication delay of the data collection algorithm were 4.2Mbps and 42ms, respectively. Compared with existing algorithms, the proposed scheme performs well in coverage retention, energy consumption reduction, and fault recovery capability, and can meet the efficient and reliable distributed data collection needs of wireless sensor networks in complex environments.