Energy efficient network clustering and hierarchical compressive sensing for wireless sensor network applications

Abstract

In recent years, wireless sensor network has witnessed a steady emergence in real time healthcare applications due to its significant advantage of resource constrained sensor network. In any WSN system, balancing energy consumption and minimizing communication cost is essential especially when the WSN has a large number of sensor nodes. For large scale WSN network with thousands of sensors, communication cost overhead significantly affects the system performance as huge amounts of data needs to be sensed from all the sensors and have to be transmitted to the Base Station (BS) for processing. In addition to this, correlations among the gathered data from across the network can be used to reduce communication cost. Data compression techniques and transform coding were previously employed to reduce communication costs. These optimizations also reduced the cost of communication that was incurred as a result of network traffic rates. However, data compression approaches present considerable computational complexity, thereby making them unsuitable for WSN based applications. newlineIn this work, a novel energy efficient WSN design methodology is introduced to maximise network lifetime by suitably addressing three major challenges (i) using the most appropriate Compressive Sensing (CS) algorithm to reduce communication cost and computational cost overhead and increase data transfer to energy consumption ratio (ii) using improved clustering protocol to select the Cluster Head (CH) and reduce traffic rate and optimally minimize transmission distance and (iii) using optimal path routing to regulate data gathering in large-scale WSNs. newline