Our research approach is to design and develop reliable, efficient, flexible, economical, real-time and realistic wellness sensor networks for smart home systems. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed into the home environment. These nodes generate real-time data related to the object usage and movement inside the home, to forecast the wellness of an individual. Here, wellness stands for how efficiently someone stays fit in the home environment and performs his or her daily routine in order to live a long and healthy life. We initiate the research with the development of the smart home approach and implement it in different home conditions (different houses) to monitor the activity of an inhabitant for wellness detection. Additionally, our research extends the smart home system to smart buildings and models the design issues related to the smart building environment; these design issues are linked with system performance and reliability. This research paper also discusses and illustrates the possible mitigation to handle the ISM band interference and attenuation losses without compromising optimum system performance.

https://www.mdpi.com/1424-8220/15/5/10350/pdf

WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings.

Energy monitoring is one of the important aspects of the energy management, as such there is a need to monitor the power consumption of a premises before planning some of the technical measures to minimize the energy consumption. This paper presents the current state of art of appliances’ energy management through Intrusive Load Monitoring (ILM) and Non-intrusive Load Monitoring (NILM), ILM is referred to as distributive sensing and NILM is based on single point sensing. In an effort to explore the recent trend in Energy Management for researchers in the area, various techniques of Home Energy Management (HEM) using ILM and NILM have been analyzed and categorized. Different contributions made by the researchers have been highlighted together with some techniques of reducing power consumption of a building for cost saving and greener environment. This study reveal that there are issues concerning load monitoring and load management that need the attention; issues such as more accurate recognition and the need for monitoring system that can recognize as many kind of loads as possible. More efforts to apply NILM in appliances’ energy management is also required. Finally there is need for promoting the energy management culture among the electricity consumers whether in homes, offices or industries. The study will help the researchers in the area to have a clear vision of the recent trends in the area.

Application of load monitoring in appliances’ energy management – A review

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

https://www.mdpi.com/1424-8220/21/4/1038/pdf

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.

Smart grid is a modified form of electrical grid where generation, transmission, distribution and customers are not only connected electrically but also through strong communication network with each other as well as with market, operation and service provider. For achieving good communication link among them, it is very necessary to find suitable protocol. In this paper, we discuss different hardware techniques for power monitoring, power management and remote power controlling at home and transmission side and also discuss the suitability of Zigbee for required communication link. Zigbee has major role in monitoring and direct load controlling for efficient power utilization. It covers enough area needed for communication and it works on low data rate of 20Kbps to 250Kbps with minimum power consumption. This paper describes the user friendly control home appliances, power on/off through the internet, PDA using Graphical User Interface (GUI) and through GSM cellular mobile phone.

Monitoring and Controlling Power Using Zigbee Communications

State-of-the-Art Review of Energy Smart Homes

Real-time Energy Monitoring and Controlling System based on ZigBee Sensor Networks

In this paper, we present a method of extracting a foreground object from multiview images with layer quantization and boundary refinement. The method has two steps, quantization of the disparity map with PSO (Particle Swarm Optimization) algorithm and refinement step. The disparity map estimated by a simple window-based method is quantized into three layers, and the initial foreground mask is created with it. After detecting suspicious regions with errors in boundaries of the initial foreground mask, they are refined using the statistical measure, the Bhattacharyya distance. The proposed method is not dependent on specific disparity estimation methods, and the experimental results show that it extracts a foreground object from multiview images accurately and robustly.

Jongwoon Hwang

Papers

Real-time Energy Monitoring and Controlling System based on ZigBee Sensor Networks

Foreground object extraction from multiview images with layer quantization and boundary refinement