Optimization of Data Aggregation Techniques in Wireless Sensor Networks

Data aggregation is an essential factor in the Wireless Sensor Networks (WSNs) since it determines energy consumption and the precision of the data as well as the lifespan of the network. This work describes a new data aggregation algorithm that solves these problems since it reduces unnecessary broadcasts and groups information while still retaining critical details. The EM algorithm has been evaluated through extensive simulation and its application in real-world scenarios proved that it offers a 35% reduction in energy consumption, data accuracy, and network life cycle that is 30% longer than the existing approaches. The ability to work with different types of networks including the dynamic topologies and high data rate also exhibit the benefits of the algorithm. These results can help build better and more effective WSNs in the future as applicable in environment, health and industry sectors. This research presents a viable solution to the issue of data aggregation in WSN thus opening up the possibility of apply in different fields.