scholarly journals Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment

2022 ◽  
Vol 3 (4) ◽  
pp. 241-252
Author(s):  
Akey Sungheetha
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Environmental Data ◽  
Sensor Data ◽  
Graphical Display ◽  
Time Data ◽  
Campus Environment ◽  
Digital World ◽  
Digital Representations ◽  
Smart Campus

Recently, various indoor based sensors that were formerly separated from the digital world, are now intertwined with it. The data visualization may aid in the comprehension of large amounts of information. Building on current server-based models, this study intends to display real environmental data acquired by IoT agents in the interior environment. Sensors attached to Arduino microcontrollers are used to collect environmental data for the smart campus environment, including air temperature, light intensity, and humidity. This proposed framework uses the system's server and stores sensor readings, which are subsequently shown in real time on the server platform and in the environment application. However, most current IoT installations do not make use of the enhanced digital representations of the server and its graphical display capabilities in order to improve interior safety and comfort conditions. The storage of such real-time data in a standard and organized way is still being examined even though sensor data integration with storing capacity server-based models has been studied in academics.

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

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

<p>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.</p><p> </p><p>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.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>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.</p><p> </p><p>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 ‘De Lieve’ 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.  </p>

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020 ◽  
Vol 12 (23) ◽  
pp. 10175
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data ◽  
Sensor Data ◽  
Simple Type ◽  
Data Sampling ◽  
Time Data ◽  
Latency Reduction ◽  
Network Delays ◽  
Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

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.

Design and Implementation of Large Computer Room Environment Intelligent Monitoring System Based on Zigbee

2013 ◽  
Vol 397-400 ◽  
pp. 1673-1676 ◽  
Author(s):  
Xing Chen Zhao ◽  
Jin Qiang Li ◽  
Jian Liu ◽  
Quan Shi ◽  
Ling Sun
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Environmental Data ◽  
Large Network ◽  
Time Data ◽  
Large Computer ◽  
Switch Control ◽  
Street Lamp ◽  
Intelligent Irrigation

For large network communication equipment are in need of real time computer maintenance and management, the traditional manual management can't meet this need, so this paper designs a set of real-time data acquisition, intelligent alarm and remote control, large computer room environment monitoring system based on Zigbee. The system is composed of terminal monitoring subsystem, control subsystem and management terminal. The terminal monitoring system consists of ZigBee node through the network, and its data acquisition include temperature, humidity and smoke environmental data; MSP430F1611 and Ethernet module monitoring subsystem are responsible for packet analysis, Ethernet connection, and Web intelligent remote switch control when monitoring data reaches a threshold. The system also can be used in street lamp remote intelligent control, intelligent irrigation control system of greenhouse vegetables and so on. Therefore, it has good application value.

Automatic Weather Stations: Tools for Managing and Monitoring Potential Impacts to Coral Reefs

2002 ◽  
Vol 36 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Ray Berkelmans ◽  
Jim C. Hendee ◽  
Paul A. Marshall ◽  
Peter V. Ridd ◽  
Alan R. Orpin ◽  
...  
Keyword(s):  
Environmental Management ◽  
Real Time ◽  
Coastal Development ◽  
Environmental Data ◽  
Limiting Factors ◽  
Data Types ◽  
Time Data ◽  
Effective Manner ◽  
Wide Range ◽  
Weather Stations

With recent technological advances and a reduction in the cost of automatic weather stations and data buoys, the potential exists for significant advancement in science and environmental management using near real-time, high-resolution data to predict biological and/or physical events. However, real-world examples of how this potential wealth of data has been used in environmental management are few and far between. We describe in detail two examples where near real-time data are being used for the benefit of science and management. These include a prediction of coral bleaching events using temperature, light and wind as primary predictor variables, and the management of a coastal development where dynamic discharge quality limits are maintained with the aid of wind data as a proxy for turbidity in receiving waters. We argue that the limiting factors for the use of near real-time environmental data in management is frequently not the availability of the data, but the lack of knowledge of the quantitative relationships between biological/physical processes or events and environmental variables. We advocate renewed research into this area and an integrated approach to the use of a wide range of data types to deal with management issues in an innovative, cost-effective manner.

Augmented Reality Based Real-Time Data Visualization Concept for Ultra-Efficient Car Prototype

2017 ◽  
Author(s):  
Peter Wozniak ◽  
Oliver Vauderwange ◽  
Nicolas Javahiraly ◽  
Dan Curticapean
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Data Visualization ◽  
Time Data ◽  
Real Time Data

scholarly journals A Framework for Real Time Processing of Sensor Data in the Cloud

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Supun Kamburugamuve ◽  
Leif Christiansen ◽  
Geoffrey Fox
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Cloud Services ◽  
Sensor Data ◽  
Smart Devices ◽  
Time Data ◽  
Real Time Processing ◽  
Real Time Data ◽  
Autonomous Robot Navigation ◽  
Real Time Data Processing

We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.

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