Fault Tolerant Sensor Query Framework using Pattern based Data Projection Scheme

Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The wireless sensor node is a tiny device that is used to capture environment information. Sensor devices are used to capture temperature and pressure details from the environment. The sensor devices are used in hospitals, home and production plants. The main components of a sensor node are microcontroller, transceiver, external memory and power source. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices. Sensors are used to cooperatively monitor physical or environmental conditions. Sensor network is equipped with a radio transceiver or other wireless communications device. The sensor networks are deployed with consideration of sensing and transmission coverage factors. Wireless sensor networks (WSNs) perform the data collection over a large geographic area. Computing and tracking the spatial-average of the sensor measurements are estimated over a region of the WSN. Sensor faults and heterogeneous measurement noise factors affects the sensor data average process. Robust averaging fixed-point iteration algorithm is used to compute a weighted average of sensor measurements with noise and faults. Each sensor performs projections on its sensor measurements to produce a lower bandwidth compressed data stream. Data approximation process is used to assign relevant data values for missed or noisy data values. The sensors in the WSN use random projections to compress the data and send the compressed data to the data fusion centre. The data fusion centre only need to perform decompression once to compute the robust average to reducing the computational requirements. The compressive data collection scheme is enhanced with Bayesian approach to support projection count prediction. Sensing and transmission coverage factors are integrated in the data collection process. Data projection accuracy is improved with frequent pattern identification mechanism. The data collection scheme is tuned to support scheduling schemes to increase network lifetime.

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