Energy conservation in WSN through multilevel data reduction scheme
Lifetime is one of the major Quality of Service factors for Wireless Sensor Networks (WSN). As sensor nodes are generally battery-powered devices, the network lifetime can be extended over a reasonable time span by lessening the energy consumption of nodes. Reducing the amount of data transmission can effectively minimize the energy consumption, the bandwidth requirement and network congestions. In a WSN, denser deployment of nodes results in a high spatial correlation between data generated by neighboring nodes. Slow varying nature of many physical phenomenon results in similar sensor observations over the period. In this proposed work, a two level data reduction technique is employed. Here the Data and Energy Aware Passive (DEAP) clustering approach is introduced to divide the sensor network into data similar clusters. A Dual Prediction (DP) based reporting is deployed between cluster members and their Cluster Head (CH). This first level data reduction is attributed to the temporal correlation of data over the time. In CHs, the data from multiple data similar nodes are aggregated to reduce the spatial data redundancy. The proposed method DEAP-DP is verified with real world datum and has achieved up to 68% data reduction at 0.5C error tolerance.
data reduction wireless sensor network dual prediction data aware clustering data aggregation