Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network
Parnasree Chakraborty and C. Tharini
DOI : 10.3844/ajassp.2017.239.244
American Journal of Applied Sciences
Volume 14, Issue 2
Wireless Sensor Networks (WSN) are comprised of spatially distributed sensor nodes, where each node contains sensors, processors and transceivers for communicating data. Regardless of the application in which the sensor network is serving, the data generated in the network eventually must be delivered to the sink. However the limited network bandwidth, frequent node/link failure along with the unreliable communication medium poses great challenges for node to node communication in WSN. Hence, energy efficient data compression algorithms are necessary for sensor nodes as they enhance the transmission efficiency in WSN. Compressive sensing is a new compression algorithm in which the input signal is converted into sparse signal and the sparse signal is further converted into a signal of reduded dimension than original signal. The dimensionality reduction improves the transmission efficiency. This new concept is recently applied in WSN, however suitable threshold selection to sparsify the one dimensional sensor reading and suitable sparifying basis for image input data are not considered in literature. Hence, in this paper analysis of compressive sensing algorithm with a suitable threshold selection is performed in order to increase the level of sparsity for one dimensional data and a suitable sparsifying basis selection is performed for image data. Results indicate that compressive sensing with suitable threshold selection improves transmission and bandwidth efficiency in case of low correlated one dimensional sensor data and a suitable basis improves the quality of transmission for image sensor data and hence the overall lifetime of sensor network can be increased.
© 2017 Parnasree Chakraborty and C. Tharini. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.