In this paper, a new layered architecture is proposed for big data-driven processing and management of future smart homes. The proposed Representational State Transfer (REST)-based architecture includes seven layers as: physical, fog-computing, network, cloud-computing, service, session, and application for efficient data exchange and processing tasks of future smart homes. The smart home physical layer includes all the sensing technologies and smart devices within the smart home which monitor the home environment and its residents. The data of these sensors will be sent to smart home fog-computing layer which can do limited data storage and processing. Then all the required data will be sent to the cloud-computing layer using smart home network layer. The cloud-computing layer provides the scalable solution for data processing and storage. The processed data in cloud-computing layer will be provided as data-driven services to different smart home and third-party (e.g., smart city) applications via smart home service layer. Based on the proposed architecture, the applications will utilize session layer and RESTFUL APIs to use the data-driven services of smart home. The proposed smart home architecture can provide a ubiquitous and shared data environment as the key aspect of Internet of Things (IoTs) systems.

Activity recognition through ambient sensors in a smart home can efficiently monitor residents' abnormal behaviors in daily life, while not adding extra burdens caused by wearable sensors. Monitoring and separating the activities of a specific individual in a multi-residential home is still, however, a significant challenge for these smart home applications. This paper proposes a new human identification sensor, which can efficiently differentiate multiple residents in a home environment to detect their height as a unique bio-feature. This sensor includes three sensing/communication modules: pyroelectric infrared (PIR) occupancy, ultrasound array, and bluetooth low energy (BLE) communication modules. The PIR occupancy module is used to detect the moving direction, while the ultrasound array module detects the moving residents' height. The combination of these two sensing technologies can be used to then detect the moving velocity. The BLE advertising mode is then used to communicate these data to the data server. A new embedded algorithm increased the energy efficiency of this identification technology. A comprehensive modeling and experiments are done to assess the performance of this sensor and the results are provided.

With increasing photovoltaic (PV) penetration in low-voltage (LV) distribution networks, voltage violation during the peak PV generation period is one of the main power quality concerns. To minimize the PV power curtailment caused by voltage violations, this paper proposes a new approach utilizing one dynamic voltage restorer (DVR) at the secondary of an existing urban distribution transformer as a continuous voltage compensator with an accompanying control algorithm. The voltage compensation algorithm controls the DVR in real-time to prevent over-voltage and under-voltage at all network nodes, thereby maximizing PV injection and preventing equipment damage. The controller uses the equivalent line impedance of network to estimate the average voltage of load points without the need for any communication links for measuring load voltages. In this approach, the fixed tap changer of the distribution transformer is also optimally adjusted to minimize the DVR rating using a proposed offline optimization method. Simulation results on IEEE LV test feeder prove the ability of the DVR in maintaining all network node voltages within the allowable range in peak demand and peak PV generation periods with the benefit of injecting only a small amount of active and reactive power to the system, in contrast to alternative algorithms.

Distributed Control and Management of Renewable Electric Energy Resources for Future Grid Requirements | InTechOpen, Published on: 2016-07-13. Authors: Ghassem Mokhtari, Amjad Anvari-Moghaddam and Ghavameddin Nourbakhsh