LAUSR

Recently, the energy efficiency is improved in the clustered wireless sensor networks (WSNs) using sink mobility in restricted path. However, due to path restriction, a constant speed is assigned with mobile sink and this has limited the time for communication to collect the sensor data in randomly deployed sensor networks. Further, the collection of sensor data increases the consumption of power in such network. Hence to improve this cluster based block wise compressed path constrained sensing is introduced in clustered sensor networks. Here, two techniques are deployed to reduce the consumption of power in sensor network. To limit the communication time in collecting the sensor data, the shortest path tree computation is used. Also, to reduce the inherent data sparsity block wise compression over spatially correlated data is used. The collection of data is done by the cluster heads and forwarded to the base stations (BSs) using shortest path tree computation. This is formulated as a mixed linear integer programming problem, which is solved using adaptive amoeba algorithm. The block wise compression method uses compressed sensing (CS) in clustered WSN and the measurement is done through block diagonal matrix. The forwarding of CS measurements is done through shortest path algorithm and this relays the measurements to the BSs. The validation is carried out in terms of total consumed power due to the effect of sparsity and transferring the CS measurements to BS. The performance is evaluated based on optimal clustering for attaining reduced power consumption. The experimental results show that the proposed method has higher throughput with increased energy efficiency than the other conventional methods.