A distributed prediction–compression-based mechanism for energy saving in IoT networks

Nowadays, the number of Internet of things (IoT) devices has rapidly increased due to their increasing use in different real-world applications. The sensor devices represent the basic element of the IoT network because they gather data from various environments and situations, while the sink node serves as the network’s brain because it processes the data and makes decisions. However, the large amount of data that the sensor devices gather and send to the gateway toward the sink, on the one hand, causes the sensor’s limited energy to be depleted and, on the other hand, makes it more difficult to achieve the decisions using these data at the sink. Therefore, before sending data to the gateway, it is important to get rid of any duplicate data while maintaining a high level of data quality. In this paper, a distributed prediction–compression-based mechanism (DiPCoM) for saving power in IoT networks is suggested. DiPCoM makes periodic decisions on sending the data to the gateway. It uses the autoregressive integrated moving average prediction method in each period to predict the next period’s data and decide whether the current data should be sent to the gateway. When the decision is made to send the data to the gateway, an effective compression approach is used by DiPCoM to get rid of the duplicate data. It combines different data transmission reduction techniques such as adaptive piecewise constant approximation, differential encoding, symbolic aggregate approximation, and Lempel–Ziv–Welch. Simulation results based on real-world data show that the DiPCoM method is better than other techniques in terms of energy consumption, data reduction ratio, transferred data size, and data accuracy.