Energy efficient data aggregation and adaptive compression model with reduced loss levels in wireless sensor networks

Every Wireless Sensor Network (WSN) has energy saving as a top priority. The sensors in a WSN are dispersed over the network, allowing them to interact with the environment in various ways. An important goal of these networks is to aggregate data, remove redundant data, and improve the quality of the gathered data. This research proposes a Adaptive Data Aggregation with Redundant Data Reduction in transmissions and a Dynamic Compression Algorithm (ADAA-RDR-DCA) that adjusts the degree of compression according to real-time attributes of data. This model was thoroughly simulated and empirically compared, providing a significant decrease in energy consumption as well increases of data accuracy over standard approaches. The findings provided show that this model is particularly suitable for data transmission purposes, being capable of a reliable and efficient operation in any scenario with respect to the network availability or environment variations. The proposed model achieved 98.7% accuracy in data aggregation and 98.8% accuracy in dynamic data compression.