Physical Sensor Devices Research Articles

Evaluation of characteristics on the indoor dwelling environments of traditional villages in Hehuang area of China based on basic environmental parameters under remote sensing technology

In Chinese northwestern rural households where the indoor air quality is worse than the outside, rural residents face much higher health risks than urban residents. In this study, remote physical sensor devices and detectors technology were used to monitor and calculate the dwelling environmental quality index of 12 dwellings of traditional villages in the Hehuang area of China based on four environmental parameters to assess the indoor air quality of local dwellings. The results show that (1) the DEQI value reached 76 in summer but only 50 in winter, and the indoor dwelling environment quality in the Hehuang area was better in summer than in winter. (2) The ST was only 34 in summer and even lower in winter, at only 10, and low indoor temperatures were a chronic problem for the indoor dwelling spaces throughout the year. (3) In both summer and winter, the SRH reached 86, which was satisfactory overall. (4) In summer, the SCO2 value was 89 and the SPM10 value was 96, reflecting relatively good overall conditions. In winter, the SCO2 value was 65, the concentrations generally increased, and the SPM10 value was equal to 40, with concentrations exceeding the national standard value more than half the time. This study enables local governments and residents to accurately evaluate and analysis existing indoor dwelling environment problems based on this remote sensing technology, enhance the environmental health awareness of local residents, and provide an objective basis for improving the local dwelling environment and developing relevant policies.

WSN Architectures for Environmental Monitoring Applications

Wireless sensor networks (WSNs) have become ubiquitous, permeating every aspect of human life. In environmental monitoring applications (EMAs), WSNs are essential and provide a holistic view of the deployed environment. Physical sensor devices and actuators are connected across a network in environmental monitoring applications to sense vital environmental factors. EMAs bring together the intelligence and autonomy of autonomous systems to make intelligent decisions and communicate them using communication technologies. This paper discusses the various architectures developed for WSNs in environmental monitoring applications and the support for specific design goals, including machine learning in WSNs and its potential in environmental monitoring applications.

Big-Sensor-Cloud Infrastructure: A Holistic Prototype for Provisioning Sensors-as-a-Service

The proposed work relates to the development of Big-Sensor-Cloud Infrastructure (BSCI) that enhances the usability and management of the physical sensor devices. Traditional sensor networks are manufactured in a proprietary, vendor-specific design, and cannot handle application switching dynamically at runtime due to the presence of monolithic kernel. Thus, the applications are inaccessible to the common people who do not own physical sensor devices. Recently, sensor-cloud infrastructure has been viewed as a substitute for traditional sensor networks. However, with the increasing growth in the velocity, variety, and variability of data, the management becomes a serious concern and difficulty. Thus, existing systems are not able to capture, analyze, and control the present data efficiently, in real-time. BSCI is a distributed framework for Big sensor-data storage, processing, virtualization, leveraging, and efficient remote management. The framework interfaces between the physical and cyber worlds, thereby acquiring real-time data from the physical WSNs into the cloud platform. This data are processed and delivered to the end-users as a simple service - Sensors-as-a-Service (Se-aaS). Multiple organizations with heterogeneous demand can be successfully served with Se-aaS. From a user-perspective, BSCI allows the naive users to envision the typical sensor devices as simple accessible services like electricity, and water.

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models.While, due to the non-linear relationship to reference instruments, fine particulate matter (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> ) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) does not present correlation to reference instrument. As a result, the LCS for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is not feasible to be calibrated. Hence, to estimate the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentration,mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

Lightweight Mashup Middleware for Coal Mine Safety Monitoring and Control Automation

Recently, the frequent coal mine safety accidents have caused serious casualties and huge economic losses. It is urgent for the global mining industry to increase operational efficiency and improve overall mining safety. This paper proposes a lightweight mashup middleware to achieve remote monitoring and control automation of underground physical sensor devices. First, the cluster tree based on ZigBee Wireless Sensor Network (WSN) is deployed in an underground coal mine, and propose an Open Service Gateway initiative (OSGi)-based uniform devices access framework. Then, propose a uniform message space and data distribution model, and also, a lightweight services mashup approach is implemented. With the help of visualization technology, the graphical user interface of different underground physical sensor devices could be created, which allows the sensors to combine with other resources easily. Besides, four types of coal mine safety monitoring and control automation scenarios are illustrated, and the performance has also been measured and analyzed. It has been proved that our lightweight mashup middleware can reduce the costs efficiently to create coal mine safety monitoring and control automation applications.

RESTful M2M Gateway for Remote Wireless Monitoring for District Central Heating Networks

In recent years, the increased interest in energy conservation and environmental protection, combined with the development of modern communication and computer technology, has resulted in the replacement of distributed heating by central heating in urban areas. This paper proposes a Representational State Transfer (REST) Machine-to-Machine (M2M) gateway for wireless remote monitoring for a district central heating network. In particular, we focus on the resource-oriented RESTful M2M gateway architecture, and present an uniform devices abstraction approach based on Open Service Gateway Initiative (OSGi) technology, and implement the resource mapping mechanism between resource address mapping mechanism between RESTful resources and the physical sensor devices, and present the buffer queue combined with polling method to implement the data scheduling and Quality of Service (QoS) guarantee, and also give the RESTful M2M gateway open service Application Programming Interface (API) set. The performance has been measured and analyzed. Finally, the conclusions and future work are presented.