Data Reduction and Cleaning Approach for Energy-saving in Wireless Sensors Networks of IoT

Abstract

The wireless sensor devices of the Internet of Things (IoT) networks will represent one of the most providers of the big data on the network because it is implemented in the widespread of real-world applications. The large volume of gathered data from the sensor devices leads to increase the communication overhead and thus decrease the limited lifetime of the sensor devices of IoT. Therefore, it is necessary to clean and reduce the redundant sensed data to minimize the cost of communication and save the energy of sensor devices. In this paper, a Data Reduction and Cleaning Approach (DaReCA) for Energy-saving in Wireless Sensor Networks (WSNs) of IoT is proposed. This approach is based on two-level of data cleaning and reduction: the sensor level and the aggregator level. In the latter, we implement a divide and conquer method to merge the near similar data sets which are received from the sensor devices and reduce the transmitted data sets to the sink. In the former, the sensor node will employ a cleaning algorithm based on the leader cluster algorithm to remove redundant data from the sensed data before sending them to the aggregator. The proposed approach is evaluated and implemented using real sensed data of wireless sensor devices with the OMNeT ++ network simulator. The proposed DaReCA approach can clean and reduce the sensed data and save energy whilst keeping suitable data accuracy.