In-network data processing in wireless sensor networks using compressed sensing Online publication date: Thu, 01-Mar-2018
by Vishal Krishna Singh; Manish Kumar
International Journal of Sensor Networks (IJSNET), Vol. 26, No. 3, 2018
Abstract: One of the major energy consuming tasks in wireless sensor networks (WSNs) is the task of data transmission. The lifetime of such a network can be significantly enhanced by, minimising the in-network transmissions and dividing the transmission load symmetrically over the network. To overcome the issues of non-uniform energy dissipation and network's lifespan, a novel hierarchical compressive sampling (HCS) scheme is proposed. On the basis of the well-known hybrid compressed sensing (CS) scheme, the proposed HCS aims at minimising the overall in-network communication during the data gathering process by obtaining correlated sensor readings through a hierarchical clustering scheme. The proposed HCS is able to identify the optimal position for the application of CS in the routing structure, to achieve symmetric load distribution in a randomly deployed network. The equal distribution of transmission load is validated through a heat map generated for showing the receiving and transmission activity at each node. An energy consumption model, based on the energy required by the radio, the processor and in the CS process, is proposed and the lifetime of the network is simulated for different sink positions. Simulations prove the efficacy of the proposed HCS over various CS-based data processing schemes.
Online publication date: Thu, 01-Mar-2018
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