scholarly journals Effects of Positioning of Multi-Sensor Devices on Occupancy and Indoor Environmental Monitoring in Single-Occupant Offices

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6296
Author(s):  
Shoaib Azizi ◽  
Ramtin Rabiee ◽  
Gireesh Nair ◽  
Thomas Olofsson
Keyword(s):  
Environmental Monitoring ◽  
Energy Use ◽  
Environmental Parameters ◽  
Communication Technologies ◽  
Sensor Data ◽  
Occupancy Detection ◽  
Sensor Devices ◽  
Fusion Analysis ◽  
Sensor Positions ◽  
Sensor Positioning

The advancements in sensor and communication technologies drive the rapid developments in the applications of occupancy and indoor environmental monitoring in buildings. Currently, the installation standards for sensors are scarce and the recommendations for sensor positionings are very general. However, inadequate sensor positioning might diminish the reliability of sensor data, which could have serious impacts on the intended applications such as the performance of demand-controlled HVAC systems and their energy use. Thus, there is a need to understand how sensor positioning may affect the sensor data, specifically when using multi-sensor devices in which several sensors are being bundled together. This study is based on the data collected from 18 multi-sensor devices installed in three single-occupant offices (six sensors in each office). Each multi-sensor device included sensors to measure passive infrared (PIR) radiation, temperature, CO2, humidity, and illuminance. The results show that the positions of PIR and CO2 sensors significantly affect the reliability of occupancy detection. The typical approach of positioning the sensors on the ceiling, in the middle of offices, may lead to relatively unreliable data. In this case, the PIR sensor in that position has only 60% accuracy of presence detection. Installing the sensors under office desks could increase the accuracy of presence detection to 84%. These two sensor positions are highlighted in sensor fusion analysis as they could reach the highest accuracy compared to other pairs of PIR sensors. Moreover, sensor positioning can affect various indoor environmental parameters, especially temperature and illuminance measurements.

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu
Keyword(s):  
Data Fusion ◽  
Environmental Monitoring ◽  
Real Time ◽  
Monitoring System ◽  
Intelligent System ◽  
Evidence Synthesis ◽  
Environmental Parameters ◽  
Monitoring Data ◽  
Sensor Data ◽  
Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

A Capacitive-Type Humidity Sensor Using Porous Ceramics for Environmental Monitoring

2014 ◽  
Vol 975 ◽  
pp. 194-198 ◽  
Author(s):  
Rodrigo de Matos Oliveira ◽  
Maria do Carmo de Andrade Nono ◽  
Gustavo de Souza Oliveira
Keyword(s):  
Environmental Monitoring ◽  
Environmental Parameters ◽  
Capacitive Sensor ◽  
Porous Ceramics ◽  
Water Dynamics ◽  
Landslide Risk ◽  
Mechanical Resistance ◽  
Water Addition ◽  
Electrical Measurements ◽  
Sensor Devices

The growing interest for the environmental monitoring in order to minimize the potential risk of landslide in hillsides and to prevent new disasters, has led the improvement in the development of new materials for manufacturing of capacitive sensor devices more reliable, more versatile and at lower cost. In this sense, ceramics have shown advantages from the point of view of mechanical resistance, resistance to chemical attacks and physical and chemical stability in aggressive environments. In addition, these materials have a unique structure, consisting of grains, grain boundaries, surfaces and pores, the control of which permit the attainment of suitable microstructures to be used as moisture sensors. The goal of this work is to investigate the capability of porous ceramics sensor devices, developed in National Institute of Space Research (INPE), to monitor the soil water dynamics. For that, ceramics sensors microstructures were characterized through scanning electron microscopy (SEM), X-ray diffractometry (XRD) and Hg porosimetry techniques. Electrical measurements were performed in function of water addition in soil samples, up to the saturation limit, for different time intervals, in the same way it happens in area with landslide risk in periods of rain. The analyses of the results evidenced that the ceramics devices are promising ones concerning to their potential in the monitoring of environmental parameters.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals Integrated sensor data processing for occupancy detection in residential buildings

2021 ◽  
Vol 237 ◽  
pp. 110810
Author(s):  
Chenli Wang ◽  
Jun Jiang ◽  
Thomas Roth ◽  
Cuong Nguyen ◽  
Yuhong Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Residential Buildings ◽  
Sensor Data ◽  
Integrated Sensor ◽  
Occupancy Detection

scholarly journals Strategies for Improving the Sustainability of Data Centers via Energy Mix, Energy Conservation, and Circular Energy

2021 ◽  
Vol 13 (11) ◽  
pp. 6114
Author(s):  
Matteo Manganelli ◽  
Alessandro Soldati ◽  
Luigi Martirano ◽  
Seeram Ramakrishna
Keyword(s):  
Environmental Impact ◽  
Information And Communication Technologies ◽  
Power Distribution ◽  
Energy Use ◽  
Data Centers ◽  
Communication Technologies ◽  
Use Of Data ◽  
Research Perspectives ◽  
Information And Communication ◽  
Energy Efficiency And Conservation

Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT. This involves increasing implementation and use of data centers, which increased energy use and environmental impact. The scope of this work is to summarize the present situation on data centers as to environmental impact and opportunities for improvement. First, we introduce the topic, presenting estimated energy use and emissions. Then, we review proposed strategies for energy efficiency and conservation in data centers. Energy uses pertain to power distribution, ICT, and non-ICT equipment (e.g., cooling). Existing and prospected strategies and initiatives in these sectors are identified. Among key elements are innovative cooling techniques, natural resources, automation, low-power electronics, and equipment with extended thermal limits. Research perspectives are identified and estimates of improvement opportunities are mentioned. Finally, we present an overview on existing metrics, regulatory framework, and bodies concerned.

Monitoring and Warning System of Highway Environmental Parameters Based on GSM

2013 ◽  
Vol 329 ◽  
pp. 22-25
Author(s):  
Zhuo Jun Shen
Keyword(s):  
Data Transmission ◽  
Detection System ◽  
Warning System ◽  
Environmental Parameters ◽  
Mobile Network ◽  
Communication Technologies ◽  
Short Message ◽  
Single Chip ◽  
Message Service ◽  
Highway System

Mobile network signal is the basic coverage along the highway, and the GSM mobile communication technologies provide a powerful and reliable short message service and data transmission services, a variety of applications based on GSM data transmission platform is also being developed. The paper focuses on highway visibility detection and speed detection, system is constitute of a communication unit that uses the TC35i module and STC89C54 single chip, As well as hardware and software design of the method, GSM precautions in the design process of the highway system are briefly described.

scholarly journals Possibility analysis of environmental threat monitoring with the Arduino platform

2018 ◽  
Vol 8 ◽  
pp. 277-281
Author(s):  
Krzysztof Lenart ◽  
Małgorzata Plechawska-Wójcik
Keyword(s):  
Environmental Monitoring ◽  
Air Temperature ◽  
Environmental Parameters ◽  
Sound Level ◽  
Environmental Threat ◽  
Temperature And Humidity

The paper describes results of the possibility analysis of environmental monitoring and detection threats with the Arduino platform. Sensors compatible with Arduino enabling environmental monitoring were used to conduct research. The research consisted in monitoring environmental parameters, monitoring among others air temperature and humidity, sound level or gases harmful to health., Capabilities of the platform have been analyzed based on the obtained results.

scholarly journals INCORPOARATION OF GEOSENSOR NETWORKS INTO INTERNET OF THINGS FOR ENVIRONMENTAL MONITORING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 259-261 ◽  
Author(s):  
R. Habibi ◽  
A. A. Alesheikh
Keyword(s):  
Internet Of Things ◽  
Environmental Monitoring ◽  
Communication Technologies ◽  
Environmental Data ◽  
Pollution Monitoring ◽  
The Internet ◽  
Air Pollution Monitoring ◽  
Sensor Web ◽  
The Internet Of Things ◽  
Resolution Data

Thanks to the recent advances of miniaturization and the falling costs for sensors and also communication technologies, Internet specially, the number of internet-connected things growth tremendously. Moreover, geosensors with capability of generating high spatial and temporal resolution data, measuring a vast diversity of environmental data and automated operations provide powerful abilities to environmental monitoring tasks. Geosensor nodes are intuitively heterogeneous in terms of the hardware capabilities and communication protocols to take part in the Internet of Things scenarios. Therefore, ensuring interoperability is an important step. With this respect, the focus of this paper is particularly on incorporation of geosensor networks into Internet of things through an architecture for monitoring real-time environmental data with use of OGC Sensor Web Enablement standards. This approach and its applicability is discussed in the context of an air pollution monitoring scenario.

Žemės stebėjimas iš kosmoso

2021 ◽  
Author(s):  
Justinas Kilpys ◽  
Laurynas Jukna ◽  
Edvinas Stonevičius ◽  
Rasa Šimanauskienė ◽  
Linas Bevainis
Keyword(s):  
Remote Sensing ◽  
Risk Management ◽  
Environmental Monitoring ◽  
Satellite Remote Sensing ◽  
Disaster Risk ◽  
Satellite Observations ◽  
Sensor Data ◽  
Earth Observations ◽  
The Earth ◽  
Insight Into

Title in English: Earth Observations from Space. There are more than 150 environmental satellites orbiting the Earth, and they are constantly monitoring its surface and the processes happening on it. This textbook offers an introduction to the physical concepts of satellite observations, describes how sensor data is transformed into information about the Earth’s surface and how it can be applied. The scientific background of satellite remote sensing is illustrated using examples from applications in agriculture, forestry, environmental monitoring, disaster risk management, and many other areas. Book provides insight into how satellite remote sensing is used to explore and monitor natural and anthropocentric processes on the Earth and serves as introduction to the practical remote sensing.

Sign in / Sign up

Export Citation Format

Share Document