A Lightweight Huffman-based Differential Encoding Lossless Compression Technique in IoT for Smart Agriculture

Abstract

Major advantages occur in modern agriculture, including effective position and space needs, sufficient meteorological management, water efficiency, and controlled nutrient use. The Internet of Things (IoT) definition suggests that different "Things," such as communication devices as well as all other physical objects on the world, can be connected and regulated over the Internet. Wireless Sensor Networks (WSNs) in particular may be thought of as important data collection and transmission systems. It is possible to build automated systems for improved agriculture environmental control using IoT and WSN. But WSN is suffering from the motes' limited energy supplies, which decrease the total network's lifetime. Each mote collects periodically the tracked feature and transmitting the data to the sink for additional study. This method of transmitting massive volumes of data allows the sensor node to use high energy and substantial usage of bandwidth on the network. In this article, we suggest a lightweight lossless compression algorithm based on Differential Encoding (DE) and Huffman techniques which is particularly beneficial for IoT sensor nodes, that monitoring the features of the environment, especially those with limited computing and memory resources. Instead of trying to formulate innovative ad hoc algorithms, we demonstrate that, provided general awareness of the features to be monitored, classical Huffman coding can be used effectively to describe the same features that measure at various time periods and locations. Results utilizing temperature measurements indicate that it outperforms common methods developed especially for WSNs, even though the suggested system does not reach the theoretical maximum