Internet of things data compression based on successive data grouping

Internet of things (IoT) is a useful technology in different aspects, and it is widely used in many applications; however, this technology faces some major challenges which need to be solved, such as data management and energy saving. Sensors generate a huge amount of data that need to be transferred to other IoT layers in an efficient way to save the energy of the sensor because most of the energy is consumed in the data transmission process. Sensors usually use batteries to operate; thus, saving energy is very important because of the difficulty of replacing batteries of widely distributed sensors. Reducing the total amount of transmitted data from the perception layer to the network layer in the IoT architecture will save the energy of the sensor. This paper proposes a new IoT data compression method; it is based on grouping similar successive data together and sending them as one row with a total number of occurrences. The decision of similarity is done by comparing the root-mean-square successive difference calculated on the training dataset. The evaluation of the proposed method was performed on the Intel Lab dataset and the compression performance of the proposed method was compared with other compression methods, where a great enhancement was achieved; the compression ratio was 10.953 with a reconstruction of temperature data error equal to 0.0313 degrees C.