Compression-based Data Reduction Technique for IoT Sensor Networks

• Main Authors: Suha Abdulhussein Abdulzahra, Ali Kadhum M. Al-Qurabat, Ali Kadhum Idrees
• Format: Article
• Language: Arabic
• Published: College of Science for Women, University of Baghdad 2021-03-01
• Series: Baghdad Science Journal
• Subjects: IoT, Sensor Networks, Data Compression, SAX Quantization, LZW.
• Online Access: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5069

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature.