Geofence and Network Proximity

Namiot, Dmitry; Sneps-Sneppe, Manfred

doi:10.1007/978-3-642-40316-3_11

Cited by 40 publications

(16 citation statements)

References 25 publications

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“…Alternatively, we can use the information from mobile devices (from the same wireless module) -so called Wi-Fi probe request right on the server for server side processing. So, the whole processing will be performed out of the phone [17].…”

Section: Fig 2 Sensorml Environment [12]mentioning

confidence: 99%

On Open Source Mobile Sensing

Namiot

Sneps-Sneppe²

2014

Lecture Notes in Computer Science

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Abstract. The paper discusses phone as a sensor model. For many applied tasks smart phones are an ideal platform for collecting and processing context-related data. The most popular example is, probably, computational social science. Phones can collect data for conducting various social researches about people's social behavior. This paper presents an attempt to describe and categorize existing open source libraries for mobile sensing, describe architecture and design patterns as well as discover directions for the future development.

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Section: Fig 2 Sensorml Environment [12]mentioning

confidence: 99%

On Open Source Mobile Sensing

Namiot

Sneps-Sneppe²

2014

Lecture Notes in Computer Science

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“…Munson & Gupta (2002) discusses geofence-based notification as generalpurpose service from commercial promotion to tourism, traffic information, public service and safety. Namiot & Sneps-Sneppe (2013) employs network proximity rules as geo-based boundaries to effectively deploy indoor location based services and provide significant energy saving for mobile devices compared with the traditional methods.…”

Section: Occupancy Information For Coolingmentioning

confidence: 99%

A tracking cooling fan using geofence and camera-based indoor localization

Liu

Yin

et al. 2017

Building and Environment

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Highlights Human positions are tracked accurately by a camera-based indoor tracking system  Air flow is directed to the desired direction by using virtual geofences  Desired air speed is provided based on the PMV-SET thermal comfort model  Fan speed setting is adjusted by a calibrated mapping algorithm based on the estimation of occupant-fan distance  The proposed system can be used for thermal comfort improvement and energy conservation AbstractCompressor-based cooling systems have a large impact on energy consumption in modern buildings, particularly in the tropics. Elevated air movement by electric fans is a cost-effective cooling method for both energy saving and thermal comfort improvement. In this paper, a tracking cooling fan using geofence and camera-based indoor localization is proposed. Personal cooling service is provided based on the detection of the occupant in the area bounded by virtual geofences. The proposed camera-based indoor tracking system is able to accurately locate the positions of the occupant, determine the direction of air flow, and calculate the occupant-fan distance. The tracking fan is able to provide the needed air speed determined by PMV-SET thermal comfort model using a calibrated mapping algorithm. The effectiveness of the proposed system has been verified through experiments which show that the system is able to operate with low power while improving thermal comfort. The system can be used in an air-conditioned environment with higher temperature setpoint to save energy or in a naturally conditioned environment for thermal comfort enhancement.

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“…Methods for determining the location in the premises are known that are not necessarily associated with the determination of the true geographical position [2] and use, for example, data on the location of Wi Fi access points. That is, mobile operating systems can use information about the objects of the net work infrastructure for verifying (or even determining) the true position of the subscriber.…”

Section: Introductionmentioning

confidence: 99%

On the analysis of statistics of mobile visitors

Namiot

Sneps-Sneppe

2014

Aut. Control Comp. Sci.

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This paper discusses location based mobile services. The problem of counting mobile users (mobile phones) in a selected area is considered. The information available from the analysis of wire less protocols (Wi Fi, Bluetooth) is used for the calculation. The aim of the study is to construct an analog of systems of web statistics operating with real mobile subscribers (instead of data on web page visits as in web statistics). As a result, we obtain information about traffic, identification and analysis of trends in user traffic, search for the core of regular visitors, and detection of its dynamics. The paper presents algorithms for calculating network proximity and examples of use.

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Geofence and Network Proximity

Cited by 40 publications

References 25 publications

On Open Source Mobile Sensing

On Open Source Mobile Sensing

A tracking cooling fan using geofence and camera-based indoor localization

On the analysis of statistics of mobile visitors

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