Scheduling Enabled Compressive Data Collection Scheme For Wireless Sensor Networks
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. Sensors are used to cooperatively monitor physical or environmental conditions. 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.