Two Tier Data Reduction Technique for Reducing Data Transmission in IoT Sensors
Ali Kadhum M. Al‐Qurabat, Chady Abou Jaoude, Ali Kadhum Idrees · 2019
The devices that interconnected to the Internet of Things (IoT) will continue to grow exponentially, and in addition, the amount of data that they report. Sensor nodes (SNs) that arranged in WSNs will create some of IoT data and transmit their readings to Gateway (GW), which driving the sensor nodes to quick expenditure their energy and storage. The low costs of SNs impose a restriction on their energy and storage. To handle these problems it's prefer to carry out reduction on data at the source nodes to reduce both of utilized storage and consumed energy. A large portion of proposed solutions implement data reduction just at one level of the IoT design (e.g. at gateways). A Two-Tier Data Reduction (TTDR) technique is proposed to work at two tier of the network that are: sensor nodes and the gateway. At the sensor node tier we use a simple and suitable data compression methods for constrained IoT sensor nodes. The techniques exploit the temporal correlation in sensor data and use Delta Encoding followed by Run-Length Encoding (RLE). At the gateway tier we apply the hierarchical clustering for grouping data sets received from sensor nodes dependent on the Minimum Description Length (MDL) principle. If any pairs of received data sets can be compressed by the MDL principle, they will be combined into one cluster. Consequently, the amount of data sets is decreased gradually, and the merging of sets in clusters is stopped if the discovery of any match of sets to compress is impossible. Finally, the TTDR performance is evaluated based on real sensory data and using OMNeT++ simulator. The acquired outcomes illustrate the proficiency of the proposed system in regarding data transmission and energy.