A Bi-Level Data Lowering Method to Minimize Transferring Big Data in the Sensors of IoT Applications

Abstract

In the IoT era, the number of devices connected to it continues to grow significantly. This can lead to an increase in the amount of reported data by these IoT devices. The reported data by the Sensor Nodes (SNs) to the Gateway (GW) drives these IoT sensors to consume their energy and storage. These problems can be solved by reducing the amount of data in the source nodes in order to reduce both the amount of energy consumed and the amount of storage required. Energy consumption represents one aspect of the Quality of Service (QoS) in the sensor nodes of the IoT. A Bi-Level Data Lowering (BLDL) approach is suggested in this article that operates at both the sensor node and gateway levels. To function in constrained IoT devices at the first level, lightweight data compression approaches were used. Delta encoding is accompanied by RLE. Two more optimization methods have been proposed for the sake of minimizing the amount of sent data as much as possible at the first level. In the second level, clustering hierarchically based on Minimum Description Length (MDL) theory was used to cluster the first level data sets. After that, the evaluation of BLDL efficiency is based on real data and the use of the OMNeT++ simulator. The findings indicate that the suggested approach reduces the overhead for the network resources as follows: At a maximum of 20.53% in BLDL and 6.14% in LBLDL for the ratio of data remaining, and a maximum of 62% in BLDL and 21% in LBLDL for the ratio of sent data sets to the GW, the required energy to send data sets to the GW is reduced from 6% to 43% in BLDL and from 79% to 85% in LBLDL, and accuracy is higher than 90% for the methods without loss and 80% for the methods with a loss when compared to existing methods.