Summary With the development of Internet of Things (IoT)‐related technologies and artificial intelligence (AI) technologies, various IoT services are becoming more intelligent, and their use range is increasing and diversifying. IoT hardware and IoT software must support AI‐related functions to provide an intelligent IoT service. In general, IoT devices powered by batteries have limited computing performance when compared to general computing environments. Therefore, it is essential to provide AI‐related functions at low power in IoT devices to implement and offer various intelligent services. Neuromorphic computing devices or neuromorphic computing architectures can operate with low power energy consumption. If applied to IoT devices, AI‐related functions can be implemented in a resource‐constrained IoT device environment. The proposed neuromorphic architecture abstraction (NAA) model dynamically selects the proper neuromorphic architecture by comparing the parameter size of a given SNN model. It also considers the specifications and error probability of the available neuromorphic architecture. We also implement the proposed model in a real IoT computing environment and show that the proposed NAA model and dynamic selection scheme can reduce the execution time for training and inferencing. It reduces the training and inferencing time of a given model compared with the method of randomly specifying the neuromorphic architecture.

Summary Online social network users provide considerable amounts of personal information and share this information with friends without space‐time limitations. The tight connectivity among users of social networks causes the rapid spreading of information. Given the popularity of social networking sites, there is a high probability of attacks. Worms target popular users with interesting information to infect them, as their higher reputations have more power in social networks. Therefore, timely patch propagation schemes must be able to inhibit the activity of worms. To improve the patch propagation speed, it is important to select key nodes that are the starting points of the patch process. In this paper, we proposed a key node selection scheme based on a genetic algorithm to find the most significant contribution nodes of patch propagation. We modeled the usage patterns of an online social network user and simulated the proposed scheme with data from this user. Simulation results show that the proposed scheme propagates patches more rapidly than existing schemes.

Mobile devices such as personal digital assistants (PDAs) and smartphones are widely used not only in our everyday lives but also in various industrial fields. Most of these mobile devices have multiple wireless network interfaces, such as Bluetooth, 3G, and Wi-Fi. A considerable amount of energy is consumed to transfer the data through wireless communication. Moreover, most of these mobile devices operate on limited battery power. In industrial environments, changes in the communication environment are severe due to significant noise sources and due to distortion in the transceiver circuitry of strong motors, static frequency changers, electrical discharge devices, and other devices. It is necessary to select the network interface efficiently in order to extend the lifetimes of mobile devices and their applications. Therefore, in this paper, we propose an energy-efficient adaptive wireless network interface-selection scheme (AWNIS). Our scheme is proposed based on the mathematical modeling of energy consumption and data transfer delay patterns. Our scheme selects the best wireless network interface in terms of energy consumption by considering the link quality and adapting a dynamic network interface-selection interval according to the network environment. The simulation results show that proposed scheme effectively improves the energy efficiency while guaranteeing a certain level of data transfer delay.