Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

Sensing and subsequent analysis of the environmental data of a given geographical area is an essential requisite for the planned development of that region. Nowadays, IoT Sensor-Cloud ecosystem has been adopted to collect data from IoT sensors and transmit it to the chosen Cloud Server for further processing and dissemination. In a large Wireless Sensor Network formed by the IoT sensors, there will be a significant amount of redundancy in the dataset when the nodes are placed closely, and the sensed data varies slowly and gradually with regard to time and space. Then, avoiding redundant data transmission can lead to lower energy consumption and communication overhead. Adaptive subset selection of sensor nodes for data size reduction in a Wireless Sensor Network is an approach to efficiently managing the amount of data transmitted within the network. Then, in the current time schedule, it is possible to optimally select a subset of the sensor nodes for data collection without very much affecting the overall data fidelity. An optimal sensor node subset selection scheme that reduces the communication load with minimum information loss is proposed to achieve this task. The unselected nodes are put in sleep mode, which consequently results in lower sensor energy expenditure. The subset selection algorithm is implemented based on the derivative-free pattern search optimizer that minimizes the reconstruction error during the associated extrapolation. This approach differs entirely from the Compressive Data Gathering approach. The simulation results reveal that the performance of the proposed scheme is superior to other similar competitive methods in terms of the mean square error, which is found to be 1.95, with the percentage participation nodes equal to 50% and when the sensor data is uniformly distributed over 20 and 30 units.