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Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities.

Sagl G, Resch B, Blaschke T - Sensors (Basel) (2015)

Bottom Line: These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing).In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city.This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

View Article: PubMed Central - PubMed

Affiliation: Department of Geoinformation and Environmental Technologies, Carinthia University of Applied Sciences, Europastrasse 4, A-9524 Villach, Austria. g.sagl@cuas.at.

ABSTRACT
In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of today's technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

No MeSH data available.


Related in: MedlinePlus

Dimensions involved in sensing (data generation, geographic phenomena, type of sensing), and some exemplary blocks (a–f) representing the amount of sensor data assigned to each dimension [140]. (a) VGI and mobile network traffic: associated with in situ sensing, social phenomena, and user-generated data; (b) VGI in the context of environmental status updates: associated with in situ sensing, physical phenomena, and user-generated data; (c) Satellite imagery: associated with remote sensing, physical phenomena, and machine-generated data; (d) Measurements from sensors and sensor networks: associated with in situ sensing, physical phenomena, and machine-generated data; (e) Human settlements extracted from satellite imagery: associated with remote sensing, social phenomena, and machine-generated data; (f) Numerical data at entrances to, and exits from shopping malls, public transport, etc.: associated with in situ sensing, social phenomena (e.g., mobility), and machine-generated data.
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sensors-15-17013-f002: Dimensions involved in sensing (data generation, geographic phenomena, type of sensing), and some exemplary blocks (a–f) representing the amount of sensor data assigned to each dimension [140]. (a) VGI and mobile network traffic: associated with in situ sensing, social phenomena, and user-generated data; (b) VGI in the context of environmental status updates: associated with in situ sensing, physical phenomena, and user-generated data; (c) Satellite imagery: associated with remote sensing, physical phenomena, and machine-generated data; (d) Measurements from sensors and sensor networks: associated with in situ sensing, physical phenomena, and machine-generated data; (e) Human settlements extracted from satellite imagery: associated with remote sensing, social phenomena, and machine-generated data; (f) Numerical data at entrances to, and exits from shopping malls, public transport, etc.: associated with in situ sensing, social phenomena (e.g., mobility), and machine-generated data.

Mentions: In this way urban geo-analysis approaches are able to gain a certain degree of context awareness, but it is increasingly important that they also comply with the paradigm of “socially aware computing” [64,65]. Based on the above deliberations, sensor data for context-aware analysis can be described in terms of its mode of generation, the geographic phenomena that it relates to, and the type of sensing. Figure 2 illustrates six different types of sensor data represented by six different blocks (labelled from “a” to “f”) and places them in a three-dimensional space according to the above-mentioned dimensions. Note that the sizes of the blocks shown in Figure 2 represent only a rough estimate of the proportional volume of data, for comparison purposes only.


Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities.

Sagl G, Resch B, Blaschke T - Sensors (Basel) (2015)

Dimensions involved in sensing (data generation, geographic phenomena, type of sensing), and some exemplary blocks (a–f) representing the amount of sensor data assigned to each dimension [140]. (a) VGI and mobile network traffic: associated with in situ sensing, social phenomena, and user-generated data; (b) VGI in the context of environmental status updates: associated with in situ sensing, physical phenomena, and user-generated data; (c) Satellite imagery: associated with remote sensing, physical phenomena, and machine-generated data; (d) Measurements from sensors and sensor networks: associated with in situ sensing, physical phenomena, and machine-generated data; (e) Human settlements extracted from satellite imagery: associated with remote sensing, social phenomena, and machine-generated data; (f) Numerical data at entrances to, and exits from shopping malls, public transport, etc.: associated with in situ sensing, social phenomena (e.g., mobility), and machine-generated data.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4541919&req=5

sensors-15-17013-f002: Dimensions involved in sensing (data generation, geographic phenomena, type of sensing), and some exemplary blocks (a–f) representing the amount of sensor data assigned to each dimension [140]. (a) VGI and mobile network traffic: associated with in situ sensing, social phenomena, and user-generated data; (b) VGI in the context of environmental status updates: associated with in situ sensing, physical phenomena, and user-generated data; (c) Satellite imagery: associated with remote sensing, physical phenomena, and machine-generated data; (d) Measurements from sensors and sensor networks: associated with in situ sensing, physical phenomena, and machine-generated data; (e) Human settlements extracted from satellite imagery: associated with remote sensing, social phenomena, and machine-generated data; (f) Numerical data at entrances to, and exits from shopping malls, public transport, etc.: associated with in situ sensing, social phenomena (e.g., mobility), and machine-generated data.
Mentions: In this way urban geo-analysis approaches are able to gain a certain degree of context awareness, but it is increasingly important that they also comply with the paradigm of “socially aware computing” [64,65]. Based on the above deliberations, sensor data for context-aware analysis can be described in terms of its mode of generation, the geographic phenomena that it relates to, and the type of sensing. Figure 2 illustrates six different types of sensor data represented by six different blocks (labelled from “a” to “f”) and places them in a three-dimensional space according to the above-mentioned dimensions. Note that the sizes of the blocks shown in Figure 2 represent only a rough estimate of the proportional volume of data, for comparison purposes only.

Bottom Line: These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing).In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city.This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

View Article: PubMed Central - PubMed

Affiliation: Department of Geoinformation and Environmental Technologies, Carinthia University of Applied Sciences, Europastrasse 4, A-9524 Villach, Austria. g.sagl@cuas.at.

ABSTRACT
In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of today's technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

No MeSH data available.


Related in: MedlinePlus