An intermittent transmission control in the MAC layer protocol is very important for managing the energy consumption of wireless sensor networks. This research focuses on an intermittent receiver-driven data transmission (IRDT) protocol in which communication starts when multiple receiver nodes transmit their own IDs intermittently and a sender node receives them. This method aims at prolonging network life time under the conditions that data-generating frequency is comparatively small. In this paper, we clarify the performance characteristics of this method by comparing it with the low power listing (LPL) method, which is a sender-driven protocol. By simulation, we show that IRDT can result in a higher reduction of energy consumption than LPL, especially at small loads. We also propose an improved IRDT scheme. While all nodes have equal and constant intermittent intervals in the original IRDT, the improved IRDT changes each node's intermittent intervals adaptively. This achieves more than 98% packet collection ratio and 50% lower power consumption than adaptive LPL, which also sets the intermittent interval adaptively.

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO₂ emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO₂ emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

This paper proposes a total optimization method of a smart community (SC) by Differential Evolution (DE) considering reduction of search space. Japanese experts has developed various sectors of SC models such as an electric utility model, an industry model, and a building model. This paper utilizes the models and tries to optimize whole of a SC in order to minimize total energy costs and shift electric power load peak of the SC. The simulation results by the proposed method are compared with the results by a Particle Swarm Optimization (PSO) based method.

This paper proposes a fault detection method for hydroelectric generators by robust random cut forest (RRCF) with feature selection using Hilbert-Schmidt Independence Criterion (HSIC). It is difficult to obtain fault data because faults unlikely occur in hydroelectric generators. Therefore, only normal data can be utilized for developing a fault detection method. Since data of hydroelectric generators have non-linear correlation, the fault detection method must consider the correlation. In this paper, the fault detection method of hydroelectric generators is assumed to be installed as cloud services in a data center. Therefore, there are two requirements for the applied method in order to develop a practical fault detection system. The first one is that the method has to generate an appropriate fault detection model for each hydroelectric generator in a short time in order to reduce CPU cost in the data center. The second one is that the limited number of features must be selected in order to save storage cost in the data center and communication cost through the internet. Therefore, if all data of hydroelectric generators are gathered, communication and CPU usage fees must be expensive. In order to reduce the costs, it is required to utilize only data with effective features for fault detection. The proposed method can construct models using only normal data, detect faults using data with non-linear correlation, and select effective features for fault detection of hydroelectric generators. The proposed method is compared with an isolation forest (IF) based method with all features, an IF based method with features of the highest accuracy, a RRCF based method with all features, and a RRCF based method with features of the highest accuracy. Area Under the Curve (AUC) values and calculation time are evaluated from the point of view of practical application of fault detection in data center. The proposed method is verified to be the most effective from the point of view. The results are verified using the Mann-Whitney U test.