Compressed sensing for different sensors: A real scenario for WSN and IoT

Wireless Sensor Networks (WSN) are integrable basic elements of Internet of Things (IoT). WSN deployment is constrained with sensor node's energy, communication range, limited on-board resources etc. Optimization of the energy consumption over the network to improve network lifetime is a challenging problem. Compressed Sensing (CS) involvement in WSN brought a solution to energy efficient data aggregation. This article presents a method, which exploits compressed sensing and dictionary learning to achieve energy efficiency in the scenario of data aggregation in WSN, where sensor node measures different sensors data. We demonstrate performance analysis of multiple sensors method with metrics, probability of successful recovery and network transmission cost. Extensive simulations on practical data set shows that our data aggregation method for practical scenario can deliver data to sink with minimum transmission cost which inherently saves significant energy to prolong the network lifetime. The probability of successful recovery shows that our method can recover compressed data with maximum probability.