Exploration on load balancing data aggregation algorithm in wireless sensor network based on big data artificial intelligence

Abstract

Based on BD‐AI(Big Data Artificial Intelligence) technology, an efficient LB data collection algorithm was designed and implemented for experimental verification and performance evaluation. The results showed the average load of algorithms A and B tested five times was between 44–48 and 60–64, respectively; the algorithms A and C were tested five times with data transmission speeds between 10–11Mbps and 4–5Mbps, respectively. The LB data aggregation algorithm based on BD‐AI in WSN is more efficient than traditional LB algorithms and traditional data aggregation algorithms. It can effectively help solve problems such as imbalanced data exchange, data redundancy, and energy waste among wireless sensor nodes, thereby improving the data collection efficiency and energy conservation of sensor networks, laying the foundation for implementing application scenarios such as intelligent perception and remote monitoring. The load balancing data aggregation algorithm of wireless sensor networks based on Big data artificial intelligence is faced with some challenges and difficulties. For example, wireless sensor networks usually produce a large amount of data, which needs real‐time aggregation and processing; Nodes in wireless sensor networks typically have limited computing, storage, and communication resources, among other issues.