Monitoring urban environmental phenomena through a wireless distributed sensor network

2018 ◽  
Vol 7 (1) ◽  
pp. 68-79 ◽  
Author(s):  
Niek Bebelaar ◽  
Robin Christian Braggaar ◽  
Catharina Marianne Kleijwegt ◽  
Roeland Willem Erik Meulmeester ◽  
Gina Michailidou ◽  
...  
Keyword(s):  
Air Quality ◽  
Built Environment ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Environmental Information ◽  
Environmental Data ◽  
Area Network ◽  
Content Type

Purpose The purpose of this paper is to provide local environmental information to raise community’s environmental awareness, as a cornerstone to improve the quality of the built environment. Next to that, it provides environmental information to professionals and academia in the fields of urbanism and urban microclimate, making it available for reuse. Design/methodology/approach The wireless sensor network (WSN) consists of sensor platforms deployed at fixed locations in the urban environment, measuring temperature, humidity, noise and air quality. Measurements are transferred to a server via long range wide area network (LoRaWAN). Data are also processed and publicly disseminated via the server. The WSN is made interactive as to increase user involvement, i.e. people who pass by a physical sensor in the city can interact with the sensor platform and request specific environmental data in near real time. Findings Microclimate phenomena such as temperature, humidity and air quality can be successfully measured with a WSN. Noise measurements are less suitable to send over LoRaWAN due to high temporal variations. Research limitations/implications Further testing and development of the sensor modules is needed to ensure consistent measurements and data quality. Practical implications Due to time and budget limitations for the project group, it was not possible to gather reliable data for noise and air quality. Therefore, conclusions on the effect of the measurements on the built environment cannot currently be drawn. Originality/value An autonomously working low-cost low-energy WSN gathering near real-time environmental data is successfully deployed. Ensuring data quality of the measurement results is subject for upcoming research.

scholarly journals REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY

2016 ◽  
Vol XLI-B2 ◽  
pp. 729-733 ◽  
Author(s):  
L. Marek ◽  
M. Campbell ◽  
M. Epton ◽  
M. Storer ◽  
S. Kingham
Keyword(s):  
Quality Of Life ◽  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Low Cost ◽  
Monitoring Network ◽  
Environmental Data ◽  
Chronic Obstructive ◽  
Actual Usage

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

scholarly journals REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY

2016 ◽  
Vol XLI-B2 ◽  
pp. 729-733
Author(s):  
L. Marek ◽  
M. Campbell ◽  
M. Epton ◽  
M. Storer ◽  
S. Kingham
Keyword(s):  
Quality Of Life ◽  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Low Cost ◽  
Monitoring Network ◽  
Environmental Data ◽  
Chronic Obstructive ◽  
Actual Usage

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

scholarly journals An Adaptive Bitrate Switching Algorithm for Speech Applications in Context of WebRTC

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
Mohannad Alahmadi ◽  
Peter Pocta ◽  
Hugh Melvin
Keyword(s):  
Real Time ◽  
Data Exchange ◽  
Quality Of Experience ◽  
End User ◽  
High Quality ◽  
Content Type ◽  
Switching Algorithm ◽  
Wide Range ◽  
The Impact

Web Real-Time Communication (WebRTC) combines a set of standards and technologies to enable high-quality audio, video, and auxiliary data exchange in web browsers and mobile applications. It enables peer-to-peer multimedia sessions over IP networks without the need for additional plugins. The Opus codec, which is deployed as the default audio codec for speech and music streaming in WebRTC, supports a wide range of bitrates. This range of bitrates covers narrowband, wideband, and super-wideband up to fullband bandwidths. Users of IP-based telephony always demand high-quality audio. In addition to users’ expectation, their emotional state, content type, and many other psychological factors; network quality of service; and distortions introduced at the end terminals could determine their quality of experience. To measure the quality experienced by the end user for voice transmission service, the E-model standardized in the ITU-T Rec. G.107 (a narrowband version), ITU-T Rec. G.107.1 (a wideband version), and the most recent ITU-T Rec. G.107.2 extension for the super-wideband E-model can be used. In this work, we present a quality of experience model built on the E-model to measure the impact of coding and packet loss to assess the quality perceived by the end user in WebRTC speech applications. Based on the computed Mean Opinion Score, a real-time adaptive codec parameter switching mechanism is used to switch to the most optimum codec bitrate under the present network conditions. We present the evaluation results to show the effectiveness of the proposed approach when compared with the default codec configuration in WebRTC.

Enhancing data-driven elderly appointment services in domestic care communities under COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
H.Y. Lam ◽  
G.T.S. Ho ◽  
Daniel Y. Mo ◽  
Valerie Tang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Care Service ◽  
The Elderly ◽  
Care Centre ◽  
Service Matching ◽  
Content Type ◽  
Care Services ◽  
Domestic Care

PurposeUnder the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services.Design/methodology/approachThe proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community.FindingsA case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS.Originality/valueFollowing the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Incorporating data quality into a multi-product procurement planning under risk

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fereshte Shabani-Naeeni ◽  
R. Ghasemy Yaghin
Keyword(s):  
Data Quality ◽  
Optimization Model ◽  
Industry 4.0 ◽  
Supplier Selection ◽  
Raw Material ◽  
Measure Data ◽  
Content Type ◽  
Multi Objective ◽  
Procurement Planning

Purpose In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties associated with raw material purchasing. This paper aims to address the issue of supplier selection and purchasing planning considering the quality of data by benefiting from suppliers’ synergistic effects. Design/methodology/approach An approach is proposed to measure data visibility’s total value using a multi-stage algorithm. A multi-objective mathematical optimization model is then developed to determine the optimal integrated purchasing plan in a multi-product setting under risk. The model contemplates three essential objective functions, i.e. maximizing total data quality and quantity level, minimizing purchasing risks and minimizing total costs. Findings With emerging competitive areas, in the presence of industry 4.0, internet of things and big data, high data quality can improve the process of supply chain decision-making. This paper supports the managers for the procurement planning of modern organizations under risk and thus provides an in-depth understanding for the enterprises having the readiness for industry 4.0 transformation. Originality/value Various data quality attributes are comprehensively subjected to deeper analysis. An applicable procedure is proposed to determine the total value of data quality and quantity required for supplier selection. Besides, a novel multi-objective optimization model is developed to determine the purchasing plan under risk.

Sensor Field Resource Management for Sensor Network Data Mining

2010 ◽  
pp. 280-304
Author(s):  
David J. Yates ◽  
Jennifer Xu
Keyword(s):  
Data Mining ◽  
Response Time ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Resource Consumption ◽  
Data Accuracy ◽  
Network Data ◽  
Sensor Field ◽  
User Queries

This research is motivated by data mining for wireless sensor network applications. The authors consider applications where data is acquired in real-time, and thus data mining is performed on live streams of data rather than on stored databases. One challenge in supporting such applications is that sensor node power is a precious resource that needs to be managed as such. To conserve energy in the sensor field, the authors propose and evaluate several approaches to acquiring, and then caching data in a sensor field data server. The authors show that for true real-time applications, for which response time dictates data quality, policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This “win-win” is because when data acquisition response time is sufficiently important, the decrease in resource consumption and increase in data quality achieved by using approximate values outweighs the negative impact on data accuracy due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between resource consumption and data accuracy emerges. The authors then identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, the authors discuss the challenges facing sensor network data mining applications in terms of data collection, warehousing, and mining techniques.

Developing an integrated indoor built environment–quality of life model for the elderly in public and subsidized housing

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mei-yung Leung ◽  
Ibukun Oluwadara Famakin ◽  
Chendi Wang
Keyword(s):  
Quality Of Life ◽  
Built Environment ◽  
Social Relationships ◽  
The Elderly ◽  
Walking Distance ◽  
Equation Modeling ◽  
Content Type ◽  
Environment Quality ◽  
Life Model

Purpose The growth rate of the aging population raises the demand for and challenges of public and subsidized (P&S) housing for the elderly. The decline in elderly ability increases their dependence on the quality of facilities provided by their residential apartment. Hence, the purpose of this paper is to develop an integrated indoor built environment–quality of life model for the elderly in P&S housing estates. Design/methodology/approach A questionnaire survey, including scales for 4 quality of life (QoL) domains (physical health, psychological health, social relationships and overall QoL), and 13 indoor built environment (BE) components were identified. In total, 365 survey data were collected from the elderly in 18 political districts of Hong Kong, while reliability analysis, multiple regression analysis and structural equation modeling were adopted in the data analysis. Findings Based on the congruence of results of these statistical analyses, it was revealed that: furniture and fixtures predict all the four QoL domains of the elderly; lighting and color induce social relationships; and overall QoL is predicted by distance and handrails. Originality/value Several recommendations were made in accordance with the research results, such as review minimum spacing requirements to provide walking distance for elderly physical activity, investigate the micro-climate for appropriate building orientation, consider the changing body size of the elderly for supply of furniture, use warm colors with high levels of illumination, and so on.

Impact of the built environment and care services within rural nursing homes in China on quality of life for elderly residents

2017 ◽  
Vol 24 (6) ◽  
pp. 1170-1183 ◽  
Author(s):  
Jingyu Yu ◽  
Guixia Ma ◽  
Xiaoyan Jiang
Keyword(s):  
Quality Of Life ◽  
Nursing Homes ◽  
Built Environment ◽  
Indoor Environment ◽  
Healthcare Services ◽  
Content Type ◽  
Rural Nursing ◽  
Daily Care ◽  
Care Services

Purpose The ageing of rural Chinese populations is challenging health and social policy, driving growth in rural nursing homes. Living environment plays a role in enhancing elderly quality of life (QoL), however, the impact of the built environment and care services are under-studied. The purpose of this paper is to investigate the influence of the built environment and care services on the QoL of elderly people within rural nursing homes in China. Design/methodology/approach A total of 242 residents of nursing home were surveyed, of which 76 percent were male and 24 percent were female. In total, 25.6 percent were aged between 60 and 69, 40.1 percent between 70 and 79, 31 percent between 80 and 89, and 3.3 percent were 90 or above. Quantitative data were analyzed through factor analysis, reliability test and multiple regression modeling. Findings The authors identified six built environment factors (room distance, space, barrier-free design, indoor environment, fire safety, and support facilities) and three services factors (i.e. daily care services, cleaning services, and healthcare services). QoL was measured over four dimensions: QoL, physical health, psychological health, and social relationships. Elderly QoL could be accurately predicted from room distance, space, barrier-free design, indoor environment, daily care services, and cleaning services. Practical implications Interventions in design of the built environment and the provision of care services are proposed, including dimensions of living space, heating, and provisions for qualified care providers. Originality/value This paper provides a clear picture about elderly special requirements on their built environment and healthcare services, helping architects, engineers and facilities managers understand elderly needs and improve built environment during design and operation stages.

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