scholarly journals Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1372 ◽  
Author(s):  
Manuel Garcia Alvarez ◽  
Javier Morales ◽  
Menno-Jan Kraak
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Spatial Information ◽  
Smart Cities ◽  
Spatial Data Analysis ◽  
Sensor Data ◽  
Reference Architecture ◽  
Real Time Processing ◽  
Time Processing ◽  
Event Driven

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

scholarly journals A Set of Integral Grid-Coding Algebraic Operations Based on GeoSOT-3D

2021 ◽  
Vol 10 (7) ◽  
pp. 489
Author(s):  
Kaihua Hou ◽  
Chengqi Cheng ◽  
Bo Chen ◽  
Chi Zhang ◽  
Liesong He ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Information ◽  
Data Retrieval ◽  
Geospatial Data ◽  
Algebraic Operation ◽  
Real Time Processing ◽  
C Language ◽  
Time Processing ◽  
Binary Operations ◽  
Set Operations

As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to each grid has become an effective way to accelerate real-time processing. Researchers have proposed optimization algorithms for spatial calculations in specific scenarios. However, a complete set of algorithms for real-time processing using grid coding is still lacking. To address this issue, a carefully designed, integral grid-coding algebraic operation framework for GeoSOT-3D (a multilayer latitude and longitude grid model) is proposed. By converting traditional floating-point calculations based on latitude and longitude into binary operations, the complexity of the algorithm is greatly reduced. We then present the detailed algorithms that were designed, including basic operations, vector operations, code conversion operations, spatial operations, metric operations, topological relation operations, and set operations. To verify the feasibility and efficiency of the above algorithms, we developed an experimental platform using C++ language (including major algorithms, and more algorithms may be expanded in the future). Then, we generated random data and conducted experiments. The experimental results show that the computing framework is feasible and can significantly improve the efficiency of spatial processing. The algebraic operation framework is expected to support large geospatial data retrieval and analysis, and experience a revival, on top of parallel and distributed computing, in an era of large geospatial data.

scholarly journals Real-time stream processing for active fire monitoring on Landsat 8 direct reception data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 765-770
Author(s):  
C. Böhme ◽  
P. Bouwer ◽  
M. J. Prinsloo
Keyword(s):  
Real Time ◽  
Processing System ◽  
Sensor Data ◽  
Distinct Advantage ◽  
Landsat 8 ◽  
Real Time Processing ◽  
Time Processing ◽  
Fire Monitoring ◽  
Active Fire ◽  
Sensing Applications

Some remote sensing applications are relatively time insensitive, for others, near-real-time processing (results 30-180 minutes after data reception) offer a viable solution. There are, however, a few applications, such as active wildfire monitoring or ship and airplane detection, where real-time processing and image interpretation offers a distinct advantage. The objective of real-time processing is to provide notifications before the complete satellite pass has been received. This paper presents an automated system for real-time, stream–based processing of data acquired from direct broadcast push-broom sensors for applications that require a high degree of timeliness. Based on this system, a processing chain for active fire monitoring using Landsat 8 live data streams was implemented and evaluated. The real-time processing system, called the FarEarth Observer, is connected to a ground station’s demodulator and uses its live data stream as input. Processing is done on variable size image segments assembled from detector lines of the push broom sensor as they are streamed from the satellite, enabling detection of active fires and sending of notifications within seconds of the satellite passing over the affected area, long before the actual acquisition completes. This approach requires performance optimized techniques for radiometric and geometric correction of the sensor data. Throughput of the processing system is kept well above the 400Mbit/s downlink speed of Landsat 8. A latency of below 10 seconds from sensor line acquisition to anomaly detection and notification is achieved. Analyses of geometric and radiometric accuracy and comparisons in latency to traditional near-real-time systems are also presented.

scholarly journals SMART CITIES INTELLIGENCE SYSTEM (SMACiSYS) INTEGRATING SENSOR WEB WITH SPATIAL DATA INFRASTRUCTURES (SENSDI)

2017 ◽  
Vol IV-4/W3 ◽  
pp. 21-28 ◽  
Author(s):  
D. Bhattacharya ◽  
M. Painho
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Smart Cities ◽  
Spatial Data Infrastructure ◽  
Sensor Data ◽  
Data Infrastructure ◽  
Intelligence System ◽  
Sensor Web ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures

The paper endeavours to enhance the Sensor Web with crucial geospatial analysis capabilities through integration with Spatial Data Infrastructure. The objective is development of automated smart cities intelligence system (SMACiSYS) with sensor-web access (SENSDI) utilizing geomatics for sustainable societies. There has been a need to develop automated integrated system to categorize events and issue information that reaches users directly. At present, no web-enabled information system exists which can disseminate messages after events evaluation in real time. Research work formalizes a notion of an integrated, independent, generalized, and automated geo-event analysing system making use of geo-spatial data under popular usage platform. Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. The other benefit, conversely, is the expansion of spatial data infrastructure to utilize sensor web, dynamically and in real time for smart applications that smarter cities demand nowadays. Hence, SENSDI augments existing smart cities platforms utilizing sensor web and spatial information achieved by coupling pairs of otherwise disjoint interfaces and APIs formulated by Open Geospatial Consortium (OGC) keeping entire platform open access and open source. SENSDI is based on Geonode, QGIS and Java, that bind most of the functionalities of Internet, sensor web and nowadays Internet of Things superseding Internet of Sensors as well. In a nutshell, the project delivers a generalized real-time accessible and analysable platform for sensing the environment and mapping the captured information for optimal decision-making and societal benefit.

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