Towards Intra-cluster Data Prediction in IoT for Efficient Energy Consumption

Optimization of energy consumption in Internet of Things (IoT) Wireless Network and routing of payload data are major concerns. In this chapter, we combine several machine learning algorithms to address these issues. The proposed sequential approach is based on two algorithms. First, we put forward the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which IoT nodes are grouped into clusters. Second, we built an intra-cluster prediction model based on multiple linear regression, in which the cluster head (CH) predicts the next information of each cluster member. This helps to limit the communication between CH and members and thus reduce the network energy consumption. Third, we performed CH selection according to its residual energy, the distance from the base station, and the base station and the number of cluster members. Simulations and a comparative study have been carried out to prove the relevance of the proposed method. From experimental analyses, we found that the proposed method increases network lifetime by a factor of 13.16, 28.75, and 47.66 as compared to LEACH, k-means-SDR, and DBSCAN-SDR approaches, respectively.