scholarly journals Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation

2018 ◽  
Vol 7 (8) ◽  
pp. 310 ◽  
Author(s):  
Thomas Gilbert ◽  
Stuart Barr ◽  
Philip James ◽  
Jeremy Morley ◽  
Qingyuan Ji
Keyword(s):  
Real Time ◽  
Systems Approach ◽  
Spatiotemporal Dynamics ◽  
Service Planning ◽  
Streaming Data ◽  
Sensor Data ◽  
Integrated Modelling ◽  
Software Systems ◽  
Resource Flows ◽  
Infrastructure Network

There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requires the ability to represent a city as a complex system of connected and interdependent components in which the topology of the electricity, water, gas, and heat demand-supply networks are modelled in an integrated manner. However, integrated modelling of these networks is hampered by the disparity between the predominant data formats and modelling processes used in the Geospatial Information Science (GIS) and Building Information Modelling (BIM) domains. This paper presents a software systems approach to scale-free, multi-format, integrated modelling of evolving cross-domain utility infrastructure network topologies, and the analysis of the spatiotemporal dynamics of their resource flows. The system uses a graph database to integrate the topology of utility network components represented in the CityGML UtilityNetwork Application Domain Extension (ADE), Industry Foundation Classes (IFC) and JavaScript Object Notation (JSON) real-time streaming messages. A message broker is used to disseminate the changing state of the integrated topology and the dynamic resource flows derived from the streaming data. The capability of the developed system is demonstrated via a case study in which internal building and local electricity distribution feeder networks are integrated, and a real-time building management sensor data stream is used to simulate and visualise the spatiotemporal dynamics of electricity flows using a dynamic web-based visualisation.

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.

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

2021 ◽  
pp. 036119812110133
Author(s):  
Yuandong Liu ◽  
Zhihua Zhang ◽  
Lee D. Han ◽  
Candace Brakewood
Keyword(s):  
Real Time ◽  
High Speed ◽  
Recurrent Events ◽  
Clustering Algorithm ◽  
Selection Procedure ◽  
The United States ◽  
Streaming Data ◽  
Data Sources ◽  
Sensor Data ◽  
Temporal Extent

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

scholarly journals A Stream Processing System for Multisource Heterogeneous Sensor Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Hu ◽  
Rui Sun ◽  
Feng Wang ◽  
Xiuhong Fei ◽  
Kuo Zhao
Keyword(s):  
Data Integration ◽  
Data Processing ◽  
Real Time ◽  
Rapid Development ◽  
Streaming Data ◽  
Sensor Data ◽  
Low Latency ◽  
Time System ◽  
Real Time System ◽  
Set Up

With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.

scholarly journals DSSM: Distributed Streaming Data Sharing Manager

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1344
Author(s):  
Hiroaki Fukuda ◽  
Ryota Gunji ◽  
Tadahiro Hasegawa ◽  
Paul Leger ◽  
Ismael Figueroa
Keyword(s):  
Control System ◽  
Data Sharing ◽  
Robot Control ◽  
Streaming Data ◽  
Sensor Data ◽  
Software Systems ◽  
Robot Operating System ◽  
Multiple Sensors ◽  
Advantages And Disadvantages ◽  
Robot Control System

Developing robot control software systems is difficult because of a wide variety of requirements, including hardware systems and sensors, even though robots are demanding nowadays. Middleware systems, such as Robot Operating System (ROS), are being developed and widely used to tackle this difficulty. Streaming data Sharing Manager (SSM) is one of such middleware systems that allow developers to write and read sensor data with timestamps using a Personal Computer (PC). The timestamp feature is essential for the robot control system because it usually uses multiple sensors with their own measurement cycles, meaning that measured sensor values with different timestamps become useless for the robot control. Using SSM allows developers to use measured sensor values with the same timestamps; however, SSM assumes that only one PC is used. Thereby, if one process consumes CPU resources intensively, other processes cannot finish their assumed deadlines, leading to the unexpected behavior of a robot. This paper proposes an SSM middleware, named Distributed Streaming data Sharing Manager (DSSM), that enables distributing processes on SSM to different PCs. We have developed a prototype of DSSM and confirmed its behavior so far. In addition, we apply DSSM to an existing real SSM based robot control system that autonomously controls an unmanned vehicle robot. We then reveal its advantages and disadvantages via several experiments by measuring resource usages.

Opinion Mining with Real Time Ontology Streaming Data

2019 ◽  
Vol 23 (1) ◽  
pp. 346-357
Author(s):  
Vithya G ◽  
Naren J ◽  
Varun V
Keyword(s):  
Real Time ◽  
Opinion Mining ◽  
Streaming Data

Optimisation and control of the inflow to a wastewater treatment plant using integrated modelling tools

1998 ◽  
Vol 37 (1) ◽  
pp. 347-354 ◽  
Author(s):  
Ole Mark ◽  
Claes Hernebring ◽  
Peter Magnusson
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Pilot Project ◽  
Integrated Modelling ◽  
Real Time Control ◽  
Sewer System ◽  
Time Control ◽  
The Impact

The present paper describes the Helsingborg Pilot Project, a part of the Technology Validation Project: “Integrated Wastewater” (TVP) under the EU Innovation Programme. The objective of the Helsingborg Pilot Project is to demonstrate implementation of integrated tools for the simulation of the sewer system and the wastewater treatment plant (WWTP), both in the analyses and the operational phases. The paper deals with the programme for investigating the impact of real time control (RTC) on the performance of the sewer system and wastewater treatment plant. As the project still is in a very early phase, this paper focuses on the modelling of the transport of pollutants and the evaluation of the effect on the sediment deposition pattern from the implementation of real time control in the sewer system.

Prediction of aircraft engine emissions using ADS-B flight data

2021 ◽  
pp. 1-25
Author(s):  
A. Filippone ◽  
B. Parkes ◽  
N. Bojdo ◽  
T. Kelly
Keyword(s):  
Real Time ◽  
Water Vapour ◽  
Long Range ◽  
Aircraft Engine ◽  
Software Systems ◽  
Engine Emissions ◽  
Flight Data ◽  
Seasonal Basis ◽  
Emissions Inventories ◽  
Aviation Emissions

ABSTRACT Real-time flight data from the Automatic Dependent Surveillance–Broadcast (ADS-B) has been integrated, through a data interface, with a flight performance computer program to predict aviation emissions at altitude. The ADS-B, along with data from Mode-S, are then used to ‘fly’ selected long-range aircraft models (Airbus A380-841, A330-343 and A350-900) and one turboprop (ATR72). Over 2,500 flight trajectories have been processed to demonstrate the integration between databases and software systems. Emissions are calculated for altitudes greater than 3,000 feet (609m) and exclude landing and take-off cycles. This proof of concept fills a gap in the aviation emissions inventories, since it uses real-time flights and produces estimates at a very granular level. It can be used to analyse emissions of gases such as carbon dioxide ( $\mathrm{CO}_2$ ), carbon monoxide (CO), nitrogen oxides ( $\mathrm{NO}_x$ ) and water vapour on a specific route (city pair), for a specific aircraft, for an entire fleet, or on a seasonal basis. It is shown how $\mathrm{NO}_x$ and water vapour emissions concentrate around tropospheric altitudes only for long-range flights, and that the cruise range is the biggest discriminator in the absolute value of these and other exhaust emissions.

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