An energy-efficient heterogeneous data gathering for sensor-based internet of things

Data gathering in the Sensor-Based Internet of Things is done in the midst of many constraints such as high in-network transmissions, uneven load distribution, high energy depletion, and data heterogeneity. Interestingly, compressed sensing based solutions for heterogeneous data gathering have been widely used to resolve these issues but remain unexplored for Sensor Based Internet of Things. Therefore, with the aim of optimizing the in-network transmissions and achieving uniform load distribution in the network, this work presents a novel compressed sensing based algorithm for heterogeneous data gathering in sensor-based Internet of Things. A random markov model is used to obtain co-relation-based segregation of the region of interest, followed by a novel compressed sensing based sampling and data gathering scheme. Simulation results are obtained for two different scenarios by varying the sink position with respect to the region of interest. Comparative analysis, with state of the art methods, proves the efficacy of the proposed scheme over existing methods where the proposed scheme achieves an improved performance of 81% and 44% for network lifetime and average energy consumption respectively.