Scalability Analysis of Data Transfer in Massive Internet of Things Applications This work analyzes the data transfer of massive Internet of Things (IoT) applications through network simulations. We focus on an environment, in which a massive number of IoT devices are sending data to a server located in the cloud. In the future, there will be an enormous number of IoT devices that generate an extensive amount of data to the mobile network. It has been estimated that approximately 28 billion connected IoT devices will exist by 2021. The number of IoT devices and the amount of data sent by the devices requires careful planning of the network infrastructure. This is why scalability analysis of the data transfer is very important. IoT devices are conventionally connected to the Internet via information brokers or gateway nodes. However, a single information broker may not be able to support the data transfer of a massive IoT node population. Our results show that once the sensor node population reaches to a massive level, more information brokers or gateway nodes are needed in the network. The results of this work can be very useful when designing the network infrastructure in massive IoT scenarios.

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.