Data Exchange in Cluster Structure for Longevity of IoT In the Internet of Things (IoT), the scope of wireless sensor nodes is extended to things deployed in a pervasive world. For various IoT service applications, things can gather and share their information with each other through selfdecisionmaking. Therefore, we cannot apply the existing information aggregation methods of wireless sensor networks to the IoT environment, which aim to transmit the collected data to only a sink node or a central server. Moreover, since the existing methods involve all the sensor nodes in the process of data exchange, they can cause an increase in the network traffic, delay of data transmission, and amount of energy consumed by things. In this paper, we propose a clusteringpropertybased data exchange method for efficient energy consumption in IoT networks. First, the proposed method assigns properties to each thing according to the characteristics of the obtained data. Second, it constructs a cluster network considering the location of things and their energy consumption. Finally, the things in a cluster communicate with other things in a different cluster based on their properties. In the experiment, the proposed method exhibits a better performance than the existing method. Owing to the energysaving effect, we demonstrate that the proposed method results in a more reliable network and improves the longevity of IoT networks.

Dynamic clustering method based on power demand and information volume for intelligent and green IoT Abstract   The Internet of Things (IoT) is an integrated part of the future network which requires comprehensive awareness, reliable delivery and intelligent processing. Therefore, it faces the challenge of energy efficiency during realtime implementations based on respective applications. The work proposes an efficient and green dynamic clustering mechanism based on power demand and information volume. The machine learning approach is based on dynamic cluster formation by avoiding any information loss. Initially, among all the available Poisson distributed nodes, the master node divides them into two temporary clusters based on their power requirements. Then, the power requirements within these two clusters are equalized by a set threshold and based on application requirements, the dynamic clusters are formed by measuring the amount of information in each cluster. The simulation results presents that the proposed method equalizes the power demand of the network and maximizes the information in the cluster, which further improves the energy efficiency of the whole network. The results are also compared with traditional methods to justify the proposed work.