Comparison of Lossless Data Compression Techniques in Low-Cost Low-Power (LCLP) IoT Systems

Abstract

With the recent advances and proliferation of the Internet of Things (IoT) devices, there is a huge demand placed on its infrastructure requirements. The amount of data generated by these small low-cost, low-power (LCLP) IoT devices is phenomenal and at the same time due to the devices being low-powered, they cannot be used to perform complex computations and other algorithm implementations. There are also limitations in communication data rates at different stages in a Wireless Sensor Network (WSN), which mainly uses wireless technologies such as Bluetooth, Zigbee, LoRa, etc to achieve low power communication. These technologies come with limited bandwidth and are not very reliable at high data rates. Hence the challenge of handling high amounts of data with low bandwidth communication technologies is one of the main hurdles inefficient LCLP IoT system deployments. To address this problem, we propose a combination of data compression techniques, which will result in reduced data size, without compromising affecting the quality of the data. This paper describes the implementation of a combination of Delta and RLE compression techniques on specific sensor data, particularly those used in our deployment of the World's First Wireless Sensor Network-based System for Early warning and Monitoring of Rainfall induced Landslides in Southern India. The test results show a good compression ratio of 52.67% for 12bit ADC, without compromising on the quality of the data. This has been implemented on a Programmable System-on-a-Chip (PSoC) system and the results presented.