Design and Implementation of an Intelligent Windowsill System Using Smart Handheld Device and Fuzzy Microcontroller

Thermal, Lighting and IAQ Control System for Energy Saving and Comfort Management

The present work proposes a simulation and control framework for home and building automation, focusing on heating, ventilating, and air conditioning processes. Control systems based on different advanced control architectures and different control policies are simulated and compared, highlighting control performances, and energy-saving results in terms of CO2 emissions reduction. Heat, lighting, and natural ventilation phenomena were modelized through first-principles and empirical equations, obtaining a reliable and flexible simulation framework. Energy-consuming and green energy-supplying renewable sources were integrated into the framework, e.g., heat pumps, artificial lights, fresh air flow, and natural illuminance. Different control schemes are proposed, based on proportional–integral–derivative advanced control architectures and discrete event dynamic systems-based supervisors; different control specifications are included, resulting in a multi-mode control system. The specifications refer to energy savings and comfort management, while minimizing overall costs. Comfort specifications include thermal comfort, lighting comfort, and a good level of indoor air quality. Simulations on different scenarios considering various control schemes and specifications show the reliability and soundness of the simulation and control framework. The simulated control and energy performances show the potential of the proposed approach, which can provide energy-saving results greater or equal to 6 [%] (in each season) and 19 [%] (in one year) with respect to more standard approaches.

Application of Low-Cost Sensors for Building Monitoring: A Systematic Literature Review

In recent years, many scholars have dedicated their research to the development of low-cost sensors for monitoring of various parameters. Despite their high number of applications, the state of the art related to low-cost sensors in building monitoring has not been addressed. To fill this gap, this article presents a systematic review, following well-established methodology, to analyze the state of the art in two aspects of structural and indoor parameters of buildings, in the SCOPUS database. This analysis allows to illustrate the potential uses of low-cost sensors in the building sector and addresses the scholars the preferred communication protocols and the most common microcontrollers for installation of low-cost monitoring systems. In addition, special attention is paid to describe different areas of the two mentioned fields of building monitoring and the most crucial parameters to be monitored in buildings. Finally, the deficiencies in line with limited number of studies carried out in various fields of building monitoring are overviewed and a series of parameters that ought to be studied in the future are proposed.

Remote Recognition of Moving Behaviors for Captive Harbor Seals Using a Smart-Patch System via Bluetooth Communication

Animal telemetry has been recognized as a core platform for exploring animal species due to future opportunities in terms of its contribution toward marine fisheries and living resources. Herein, biologging systems with pressure sensors are successfully implemented via open-source hardware platforms, followed by immediate application to captive harbor seals (HS). Remotely captured output voltage signals in real-time mode via Bluetooth communication were reproducibly and reliably recorded on the basis of hours using a smartphone built with data capturing software with graphic user interface (GUI). Output voltages, corresponding to typical behaviors on the captive HS, such as stopping (A), rolling (B), flapping (C), and sliding (D), are clearly obtained, and their analytical interpretation on captured electrical signals are fully validated via a comparison study with consecutively captured images for each motion of the HS. Thus, the biologging system with low cost and light weight, which is fully compatible with a conventional smartphone, is expected to potentially contribute toward future anthology of seal animals.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

An Open Hardware Design for Internet of Things Power Quality and Energy Saving Solutions

An important challenge for our society is the transformation of traditional power systems to a decentralized model based on renewable energy sources. In this new scenario, advanced devices are needed for real-time monitoring and control of the energy flow and power quality (PQ). Ideally, the data collected by Internet of Thing (IoT) sensors should be shared to central cloud systems for online and off-line analysis. In this paper openZmeter (oZm) is presented as an advanced low-cost and open-source hardware device for high-precision energy and power quality measurement in low-voltage power systems. An analog front end (AFE) stage is designed and developed for the acquisition, conditioning, and processing of power signals. This AFE can be stacked on available quadcore embedded ARM boards. The proposed hardware is capable of adapting voltage signals up to 800 V AC/DC and currents up to thousands of amperes using different probes. The oZm device is described as a fully autonomous open-source system for the computation and visualization of PQ events and consumed/generated energy, along with full details of its hardware implementation. It also has the ability to send data to central cloud management systems. Given the small size of the hardware design and considering that it allows measurements under a wide range of operating conditions, oZm can be used both as bulk metering or as metering/submetering device for individual appliances. The design is released as open hardware and therefore is presented to the community as a powerful tool for general usage.

Data Analytics of a Wearable Device for Heat Stroke Detection

When exercising in a high-temperature environment, heat stroke can cause great harm to the human body. However, runners may ignore important physiological warnings and are not usually aware that a heat stroke is occurring. To solve this problem, this study evaluates a runner’s risk of heat stroke injury by using a wearable heat stroke detection device (WHDD), which we developed previously. Furthermore, some filtering algorithms are designed to correct the physiological parameters acquired by the WHDD. To verify the effectiveness of the WHDD and investigate the features of these physiological parameters, several people were chosen to wear the WHDD while conducting the exercise experiment. The experimental results show that the WHDD can identify high-risk trends for heat stroke successfully from runner feedback of the uncomfortable statute and can effectively predict the occurrence of a heat stroke, thus ensuring safety.

Design and Development of a Wearable Device for Heat Stroke Detection

Heat stroke can be potentially damaging for people while exercising in hot environments. To prevent this dangerous situation, we designed a wearable heat-stroke-detection device (WHDD) with early notification ability. First, we used several physical sensors, such as galvanic skin response (GSR), heart beat, and body temperature, to acquire medical data from exercising people. In addition, we designed risk evaluation functional components that were based on fuzzy theory to detect the features of heat stroke for users. If a dangerous situation is detected, then the device will activate the alert function to remind the user to respond adequately to avoid heat stroke.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.