Semantic Traffic Sensor Data: The TRAFAIR Experience

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

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An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Algorithms ◽

10.3390/a13100254 ◽

2020 ◽

Vol 13 (10) ◽

pp. 254

Author(s):

Rosario Fedele ◽

Massimo Merenda

Keyword(s):

Emergency Management ◽

Real Time ◽

Smart Cities ◽

Near Field ◽

Shortest Paths ◽

Sensor Data ◽

Time Data ◽

Social Distancing ◽

Web Platform

Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown.

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A Sustainable Method for Publishing Interoperable Open Data on the Web

Data ◽

10.3390/data6080093 ◽

2021 ◽

Vol 6 (8) ◽

pp. 93

Author(s):

Raf Buyle ◽

Brecht Van de Vyvere ◽

Julián Rojas Meléndez ◽

Dwight Van Lancker ◽

Eveline Vlassenroot ◽

...

Keyword(s):

Air Quality ◽

Smart Cities ◽

Research Question ◽

Open Data ◽

Cost Effective ◽

Linked Open Data ◽

Sensor Data ◽

Quality Data ◽

Data Publishing ◽

Railway Infrastructure

Smart cities need (sensor) data for better decision-making. However, while there are vast amounts of data available about and from cities, an intermediary is needed that connects and interprets (sensor) data on a Web-scale. Today, governments in Europe are struggling to publish open data in a sustainable, predictable and cost-effective way. Our research question considers what methods for publishing Linked Open Data time series, in particular air quality data, are suitable in a sustainable and cost-effective way. Furthermore, we demonstrate the cross-domain applicability of our data publishing approach through a different use case on railway infrastructure—Linked Open Data. Based on scenarios co-created with various governmental stakeholders, we researched methods to promote data interoperability, scalability and flexibility. The results show that applying a Linked Data Fragments-based approach on public endpoints for air quality and railway infrastructure data, lowers the cost of publishing and increases availability due to better Web caching strategies.

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Multisensor IoT Platform for Optimising IAQ Levels in Buildings through a Smart Ventilation System

Sustainability ◽

10.3390/su11205777 ◽

2019 ◽

Vol 11 (20) ◽

pp. 5777 ◽

Cited By ~ 7

Author(s):

Giacomo Chiesa ◽

Silvia Cesari ◽

Miguel Garcia ◽

Mohammad Issa ◽

Shuyang Li

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Real Time ◽

Indoor Air ◽

Low Cost ◽

Ventilation System ◽

Mobile App ◽

Sensor Data ◽

Pollutant Concentrations ◽

Indoor And Outdoor

Indoor Air Quality (IAQ) issues have a direct impact on the health and comfort of building occupants. In this paper, an experimental low-cost system has been developed to address IAQ issues by using a distributed internet of things platform to control and monitor the indoor environment in building spaces while adopting a data-driven approach. The system is based on several real-time sensor data to model the indoor air quality and accurately control the ventilation system through algorithms to maintain a comfortable level of IAQ by balancing indoor and outdoor pollutant concentrations using the Indoor Air Quality Index approach. This paper describes hardware and software details of the system as well as the algorithms, models, and control strategies of the proposed solution which can be integrated in detached ventilation systems. Furthermore, a mobile app has been developed to inform, in real time, different-expertise-user profiles showing indoor and outdoor IAQ conditions. The system is implemented in a small prototype box and early-validated with different test cases considering various pollutant concentrations, reaching a Technology Readiness Level (TRL) of 3–4.

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DESIGN AND IMPLEMENT AN IoT CLOUD FOR FIELD SURVEY BASED ON COAP PROTOCOL

Journal of Engineering and Sustainable Development ◽

10.31272/jeasd.conf.2.1.21 ◽

2021 ◽

Vol 25 (Special) ◽

pp. 1-181-1-188

Author(s):

Hadeel H. Azeez ◽

◽

Mahmood Z. Abdullah ◽

Keyword(s):

Real Time ◽

Smart Cities ◽

Low Cost ◽

Raspberry Pi ◽

New Schools ◽

Time Data ◽

Elapsed Time ◽

Secure Storage ◽

Cloud Server ◽

Secure Place

Urban planning for smart cities requires collecting big real-time data, specially geolocation data from GPS sensors to use in many services like finding the best location for new schools so this data must be stored in a secure place with low cost and because the storage services offered from different cloud providers like Google, Amazon Web Service, Azure, etc., is not free. For these reasons, this study proposed Internet of Things (IoT) cloud architecture using Raspberry Pi model B+ as a cloud server with MySQL database services to provide free and secure storage at a low cost. The main contributions of this study lie in the Constrained Application Protocol (CoAP) server hosted in raspberry Pi to offer services in the proposed architecture of the IoT cloud with different scenarios to know the proposed architecture's ability for handling many user requests per second in terms of standard division, average elapsed time, error rate, throughput, and a number of real stored data in the database. AS a result, the proposed architecture handled 150 requests per second in real-time with an elapsed time of 1186 milliseconds without any error or data loss.

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Evaluating the applicability of a low-cost sensor for measuring PM2.5 concentration in Ho Chi Minh city, Viet Nam

Science and Technology Development Journal ◽

10.32508/stdj.v22i3.1688 ◽

2019 ◽

Vol 22 (3) ◽

pp. 343-347

Author(s):

Chi Doan Thien Nguyen ◽

Hien Thi To

Keyword(s):

Real Time ◽

Strong Correlation ◽

Low Cost ◽

Gravimetric Method ◽

Reference Method ◽

Sensor Data ◽

Ho Chi Minh City ◽

Time Data ◽

New Approach ◽

Pm2.5 Concentration

Introduction: Continuous monitoring provides real-time data which is helpful for measuring air quality; however, these systems are often very expensive, especially for developing countries such as Vietnam. The use of low-cost sensors for monitoring air pollution is a new approach in Vietnam and this study assesses the utility of low-cost, light-scattering-based, particulate sensors for measuring PM2.5 concentrations in Ho Chi Minh City. Methods: The low-cost sensors were compared with both a Beta attenuation monitor (BAM) reference method and a gravimetric method during the rainy season period of October to December 2018. Results: The results showed that there was a very strong correlation between two low-cost sensors (R = 0.97, slope = 1.0), and that the sensor precision varied from 0 to 21.4% with a mean of 3.1%. Both one-minute averaged data and one-hour averaged data showed similar correlations between sensors and BAM (R2 = 0.62 and 0.69, respectively), while 24-hour averaged data showed excellent agreement (R2 = 0.95, slope = 1.05). In addition, we also found a strong correlation between those instruments and a gravimetric method using 24-hour averaged data. A linear regression was used to calibrate the 24-hour averaged sensor data and, once calibrated, the bias dropped to zero. Conclusion: These results show that low-cost sensors can be used for daily measurements of PM2.5 concentrations in Ho Chi Minh City. The effect of air conditions, such as temperature and humidity, should be conducted. Moreover, technical methods to improve time resolution of lowcost sensors need to be developed and applied in order to provide real-time measurements at an inexpensive cost.

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Open or Closed? Open Licensing of Real-time Public Sector Transit Data

JeDEM - eJournal of eDemocracy and Open Government ◽

10.29379/jedem.v8i2.414 ◽

2016 ◽

Vol 8 (2) ◽

pp. 1-20

Author(s):

Teresa Scassa ◽

Alexandra Diebel

Keyword(s):

Real Time ◽

Smart Cities ◽

Open Data ◽

Gps Data ◽

Time Data ◽

Arrival Times ◽

Static Data ◽

Real Time Data ◽

Data Files ◽

Data Context

This paper explores how real-time data are made available as “open data” using municipal transit data as a case study. Many transit authorities in North America and elsewhere have installed technology to gather GPS data in real-time from transit vehicles. These data are in high demand in app developer communities because of their use in communicating predicted, rather than scheduled, transit vehicle arrival times. While many municipalities have chosen to treat real-time GPS data as “open data”, the particular nature of real-time GPS data requires a different mode of access for developers than what is needed for static data files. This, in turn, has created a conflict between the “openness” of the underlying data and the sometimes restrictive terms of use which govern access to the real-time data through transit authority Application Program Interfaces (APIs). This paper explores the implications of these terms of use and considers whether real-time data require a separate standard for openness. While the focus is on the transit data context, the lessons from this area will have broader implications, particularly for open real-time data in the emerging ‘smart cities’ environment.

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Spatial calibration and PM2.5 mapping of low-cost air quality sensors

Scientific Reports ◽

10.1038/s41598-020-79064-w ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hone-Jay Chu ◽

Muhammad Zeeshan Ali ◽

Yu-Chen He

Keyword(s):

Air Quality ◽

Real Time ◽

Low Cost ◽

Spatial Regression ◽

Sensor Data ◽

Calibration Model ◽

Mapping Algorithm ◽

Governmental Agencies ◽

Mapping Approach ◽

Spatial Calibration

AbstractThe data quality of low-cost sensors has received considerable attention and has also led to PM2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.

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An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

Sensors ◽

10.3390/s21010050 ◽

2020 ◽

Vol 21 (1) ◽

pp. 50

Author(s):

Steve H. L. Liang ◽

Sara Saeedi ◽

Soroush Ojagh ◽

Sepehr Honarparvar ◽

Sina Kiaei ◽

...

Keyword(s):

Real Time ◽

Situational Awareness ◽

Low Cost ◽

Heterogeneous Systems ◽

Geospatial Data ◽

Sensor Data ◽

Contact Tracing ◽

Time Data ◽

Sensor Web

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

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BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings

Sustainability ◽

10.3390/su13084496 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4496

Author(s):

Giuseppe Desogus ◽

Emanuela Quaquero ◽

Giulia Rubiu ◽

Gianluca Gatto ◽

Cristian Perra

Keyword(s):

Energy Consumption ◽

Low Cost ◽

Application Programming Interface ◽

Visual Programming ◽

Information Modeling ◽

Sensor Data ◽

Time Data ◽

Existing Building ◽

Building Maintenance ◽

Indoor Conditions

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

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Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

10.5194/egusphere-egu21-13338 ◽

2021 ◽

Author(s):

Goedele Verreydt ◽

Niels Van Putte ◽

Timothy De Kleyn ◽

Joris Cool ◽

Bino Maiheu

Keyword(s):

Surface Water ◽

Groundwater Flow ◽

Real Time ◽

Flow Direction ◽

Flow Dynamics ◽

Sensor Data ◽

Time Data ◽

Mass Fluxes ◽

First Case

Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.&#160;This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.&#160;We will present the measurement principles and technical aspects of the sensor, together with two case studies.&#160;The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.&#160;A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal &#8216;De Lieve&#8217; in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.&#160;&#160;

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