Hiroshi Mineno

Adaptive data aggregation scheme in clustered wireless sensor networks

Energy efficiency has been known as the most important issue in all facets of wireless sensor network (WSN) operations because of the limited and unrechargeable energy provision of sensor nodes. And WSN has emerged as an event-driven paradigm based on the collective effort of numerous sensing nodes. Data aggregation, eliminating data redundancy to improve the energy efficiency, is essential for WSNs. Moreover, due to dynamic topology and random deployment, incorporating adaptive behavior into protocols for WSNs is very important. Hence, we propose an adaptive data aggregation (ADA) scheme for clustered WSNs in this paper. In ADA scheme, the temporal aggregation degree controlled by the reporting frequency at sensor nodes and the spatial aggregation degree controlled by the aggregation ratio at cluster heads (CHs) are determined by the current scheme state according to the observed reliability. Furthermore, the function of the ADA scheme is mainly performed at sink node, with a little function at CHs and sensor nodes. Performance results show that the scheme state converges to the desired reliability starting from any initial state.

A Meta-Data-Based Data Aggregation Scheme in Clustering Wireless Sensor Networks

Energy efficiency has been known as the most significant problem in all facets of the wireless sensor network operations. In this paper, we propose a meta-data-based data aggregation scheme in the clustering wireless sensor networks. For all sensor nodes in the sensing range of an event can detect the event and generate the sensing data, the cluster head will receive the sensing data packets about the same event, and there is likely to be some redundancy. In our scheme, only one of the sensor nodes within the sensing range of an event in a cluster is selected to transmit the sensing data to the cluster head via the meta data negotiation. We also evaluate this scheme with the LEACH protocol. The simulation results show that the cluster protocol with our data aggregation scheme is effective in prolonging the network lifetime and supporting scalable data aggregation.

An Energy-Aware Routing Scheme with Node Relay Willingness in Wireless Sensor Networks

Energy efficiency has been known as the most significant problem in all facets of the wireless sensor network operations. For energy-aware routing scheme for the wireless sensor networks, it is necessary to use sub-optimal paths occasionally to increase the survivability of networks. In this paper, we present a new network lifetime definition and formulate the energy-aware routing problem with defined network lifetime. And then we propose an energy-aware routing scheme with the node relay willingness for wireless sensor networks. It considers not only to route packets through sensor nodes that have sufficient energy reserves, but also to route packets through light-loaded nodes. Simulation results show that the proposed routing algorithm can achieve longer network lifetime than that of the routing algorithm only considering the energy reserves

Integrated protocol for optimized link state routing and localization: OLSR-L

Localization protocol is important for estimating node positions in a wireless multi-hop network. Routing protocol is also important for controlling paths. In previous research, localization and routing protocols have been discussed and evaluated separately. In this paper, we propose an integrated protocol for optimized link state routing (OLSR) and OLSR based localization (ROULA). Our protocol enables simultaneous localization and routing. ROULA’s localization is performed using OLSR overhead such as hello packets and routing tables. The routing overheads and the processing procedures can be efficiently integrated. We demonstrate that the integrated protocol for ROULA and OLSR enables simultaneous localization and routing.

Adaptive Home/Building Energy Management System Using Heterogeneous Sensor/Actuator Networks

Energy consumption has been increasing over the past half century. Even though ultra-low power consuming appliances are being developed, the amount of energy consumption in homes is increasing because of various appliances being produced for ubiquitous smart home. We propose an adaptive homelbuHding energy management system (A-HEMSIBEMS) for controlling energy consumption using the convergence of heterogeneous sensor/actuator networks. The system goes far beyond traditional auto meter reading (AMR) systems that otTer only limited meter-reading functionality and provides a smart infrastructure management that brings new features and benefits to every aspect of our utilities.

A Reliable Wireless Control System for Tomato Hydroponics

Agricultural systems using advanced information and communication (ICT) technology can produce high-quality crops in a stable environment while decreasing the need for manual labor. The system collects a wide variety of environmental data and provides the precise cultivation control needed to produce high value-added crops; however, there are the problems of packet transmission errors in wireless sensor networks or system failure due to having the equipment in a hot and humid environment. In this paper, we propose a reliable wireless control system for hydroponic tomato cultivation using the 400 MHz wireless band and the IEEE 802.15.6 standard. The 400 MHz band, which is lower than the 2.4 GHz band, has good obstacle diffraction, and zero-data-loss communication is realized using the guaranteed time-slot method supported by the IEEE 802.15.6 standard. In addition, this system has fault tolerance and a self-healing function to recover from faults such as packet transmission failures due to deterioration of the wireless communication quality. In our basic experiments, the 400 MHz band wireless communication was not affected by the plants’ growth, and the packet error rate was less than that of the 2.4 GHz band. In summary, we achieved a real-time hydroponic liquid supply control with no data loss by applying a 400 MHz band WSN to hydroponic tomato cultivation.

Energy Monitoring System Using Sensor Networks in Residential Houses

The energy consumption of consumer electronics has been increasing over the past half century. Due to the growing importance of energy saving, many researches about the energy savings of information and communication technology have been presented. Providing residents with information on their energy use has become a feasible option to promote energy savings in the residential sector. In this paper, a home energy management system, particularly an energy monitoring system using sensor networks, is described. The authors designed and developed a smart power strip sensor network and evaluated its accuracy and energy saving efficiency.

Adaptive data aggregation for clustered wireless sensor networks

Wireless sensor network (WSN) has emerged as an event-driven paradigm based on the collective effort of numerous sensing nodes. Due to the dynamic topology and random deployment, incorporating adaptive behavior into protocols in WSNs is important. Hence, we propose an adaptive data aggregation (ADA) scheme for the clustered WSNs. In ADA scheme, the temporal aggregation degree controlled by the reporting frequency at sensor nodes and the spatial aggregation degree controlled by the aggregation degree at Cluster Heads (CHs) are determined by the current scheme state according to the observed reliability. Furthermore, the ADA scheme is mainly performed at the sink node, with a few functions at CHs and sensor nodes. Performance results show that the scheme state converges to the desired reliability starting from any initial state.

Development and evaluation of smart tap type Home Energy Management System using sensor networks

This paper proposes construction technology for a Smart Tap Home Energy Management System (HEMS) that can make the energy consumption of living environment information and the consumer electronics visible using Wireless Sensor Networks (ZigBee). This technology solves the problem of the introduction cost of the HEMS spread Also, HEMS can be easily constructed in homes or the offices without modifying it to fit an existing building or consumer electronics product at all. Moreover, using the features of the ZigBee telecommunication, the technology is described that can display and analyze a detailed breakdown of the amount of electric power an hour. We developed and evaluated the proposed prototype Smart Tap HEMS for Indication. As a result, we found out how much electricity is wasted and shared the energy-saving strategy in our laboratory.

Sliding window-based support vector regression for predicting micrometeorological data

A new methodology for predicting micrometeorological data is proposed.Our proposed method involves a novel combination of SVR and ensemble learning.Weak learners built from efficient extracted data is aggregated dynamically.Large-scale micrometeorological data to compare other methods is used.The best prediction performance and the lowest time complexity are achieved. Sensor network technology is becoming more widespread and sophisticated, and devices with many sensors, such as smartphones and sensor nodes, have been used extensively. Since these devices have more easily accumulated various kinds of micrometeorological data, such as temperature, humidity, and wind speed, an enormous amount of micrometeorological data has been accumulated. In recent years, it has been expected that such an enormous amount of data, called big data, will produce novel knowledge and value. Accordingly, many current applications have used data mining technology or machine learning to exploit big data. However, micrometeorological data has a complicated correlation among different features, and its characteristics change variously with time. Therefore, it is difficult to predict micrometeorological data accurately with low computational complexity even if state-of-the-art machine learning algorithms are used. In this paper, we propose a new methodology for predicting micrometeorological data, sliding window-based support vector regression (SW-SVR) that involves a novel combination of support vector regression (SVR) and ensemble learning. To represent complicated micrometeorological data easily, SW-SVR builds several SVRs specialized for each representative data group in various natural environments, such as different seasons and climates, and changes weights to aggregate the SVRs dynamically depending on the characteristics of test data. In our experiment, we predicted the temperature after 1h and 6 h by using large-scale micrometeorological data in Tokyo. As a result, regardless of testing periods, training periods, and prediction horizons, the prediction performance of SW-SVR was always greater than or equal to other general methods such as SVR, random forest, and gradient boosting. At the same time, SW-SVR reduced the building time remarkably compared with those of complicated models that have high prediction performance.