The Industrial Internet of Things (IIoT) refers to the multitude of connected devices and sensors used in industrial settings such as manufacturing plants, transportation systems, and energy grids. These devices can collect and exchange data with the goal of improving efficiency, productivity, and safety of the systems within which they are used and sometimes beyond.
IIoT devices are typically designed to monitor and control various aspects of industrial processes, such as machine performance, inventory levels, energy use, and environmental conditions. The data collected can be processed using conventional statistical tools or analyzed using artificial intelligence to detect trends and patterns and predict how changes in various parameters might change outcomes with a view to optimising the various industrial processes.
Overall, we can see the IIoT is an important part of the digitization and automation of industry, which is having an increasing impact on the economy and society.
But, there is an issue.
While the IIoT will be critical in making production more efficient and sustainable across various industries, it currently uses Wi-Fi for its connectivity (Standard IEEE 802.11), and Wi-Fi can consume a lot of energy because of the size of the data packets sent back and forth and the maximum transmission unit (MTU).
Researchers in Brazil have investigated data compression as a possible solution to this problem. Writing in the International Journal of Embedded Systems, the team describes two new methods they suggest can reduce significantly the amount of data sent by IIoT devices. Their methods use data compression to minimize the size of transmitted packets and the MTU. The first method involves using a customized binary Huffman-tree. This approach analyses the frequency of characters in a data stream and assigns a variable-length code to each in a way that minimizes the total number of bits required to represent the data. The second method utilizes a Lempel-Ziv-Welch algorithm with a flexible dictionary. This lossless data compression algorithm works by identifying repeated patterns or sequences of data in a given data stream and replaces those sequences with shorter codes.
The team’s experiments with these compression techniques show that they can reduce energy consumption by 8% compared to existing solutions for IIoT. A manufacturing plant currently using an IIoT system might consume 1000 kilowatt-hours (kWh) of energy each month. With the proposed data compression methods in place reducing energy consumption by 8% that might result in a monthly savings of around 1 megawatt-hour per year, the equivalent energy consumption of hundreds of typical homes in the developed world.
Silva, M.V., Mosca, E.E. and Gomes, R.L. (2022) ‘Green industrial internet of things through data compression’, Int. J. Embedded Systems, Vol. 15, No. 6, pp.457–466.