Refining WiFi Indoor Positioning Renders Pertinent Deploying Location-Based Multimedia Guide

Lassabe, Frédéric; Canalda, Philippe; Chatonnay, Pascal; Spies, François; Charlet, Damien

doi:10.1109/aina.2006.284

Cited by 21 publications

(6 citation statements)

References 7 publications

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“…In our proposed algorithm we use the environmental data and do not rely on MDI data. In [22], a WiFi positioning method to locate mobile terminals is presented. A hybrid model based on the RADAR [23] model and Friis-Based Calibrated Model (FBCM) is suggested.…”

Section: Related Workmentioning

confidence: 99%

Advanced Real-Time Indoor Tracking Based on the Viterbi Algorithm and Semantic Data

Trogh¹,

David²,

Martens³

et al. 2015

International Journal of Distributed Sensor Networks

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A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance.

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Section: Related Workmentioning

confidence: 99%

Advanced Real-Time Indoor Tracking Based on the Viterbi Algorithm and Semantic Data

Trogh¹,

David²,

Martens³

et al. 2015

International Journal of Distributed Sensor Networks

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“…A relatively new option is the Wi-Fi solution, which offers a more efficient, affordable, and less complex option for indoor tracking (Lassabe et al 2006). The system is especially suitable for urban areas because of the frequency of signal overlapping, which creates a natural reference system (Zandbergen 2009).…”

Section: Building Occupancy Detectionmentioning

confidence: 99%

Assessing Occupants' Energy-load Variation in Commercial and Educational Buildings: Occupancy Detecting Approach Based on Existing Wireless Network Infrastructure

Chen¹

2014

Construction Research Congress 2014

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Providing energy-consumption feedback has the potential to change people's behavior, a reality that has led to significant energy-usage reductions in residential buildings. However, it is challenging to provide feedback in commercial and educational buildings because it is difficult to track occupants' behaviors and their corresponding energy usage-especially for temporary occupants. In order to make providing such feedback possible in commercial and educational buildings, this paper presents a coupled system that monitors the energy load for occupants who have WiFi-enabled devices. The system benchmarks energy loads using an energy monitoring system that simultaneously detects occupancy and roughly estimates the residents' location through Wi-Fi access points. A preliminary experiment was conducted in an educational building to illustrate the data processing procedure and to test the validity of the system. The experiment results suggest the event of wireless connection is a valid indication of energy load variation. The proposed system is the prototype of a coupled system that, in the future, will be able to estimate individual's energy load through an indoor positioning system and, in turn, provide corresponding energyconsumption feedback. INTRODUCTIONBuildings account for 40% of all energy consumed every year, making them the largest consumer of energy in the U.S. (U.S. Department of Energy 2011). Residents in commercial and educational buildings significantly contribute to and exercise a great control over a significant amount of the energy end-uses, such as lighting, space heating, space cooling, electronics and appliances (U.S. Department of Energy 2011). Conventional approaches toward enhancing building energy efficiency focus on building envelope retrofitting and system update. However, such approaches require large capital investments and sometimes are infeasible.Recently, researchers propose that changing people's behavior by providing high resolution feedback has a great potential to contribute to the reduction of energy consumption. Feedback, in the context of energy-related research, means all levels of information related to energy consumption that may be provided to energy end-users. By providing real-time energy feedback to occupants, researchers observed up to a 46% reduction in consumption in residential buildings (Abrahamse et al. 2007;

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“…OwlPS implements several positioning techniques and algorithms (RADAR [3], Interlink Networks [25], FBCM [26] and Basic FRBHM [27]), allowing to combine and compare them, even in a real-life experiment way [23]. The configuration where infrastructure executes all the processing needs several elements: mobile terminals equipped with Wi-Fi cards, access points or any capture device (listening for any positioning request transmitted by the mobiles), the aggregation server (which the APs forward the received positioning requests to) and the computation server (which computes the position of each mobile from information forwarded by the aggregation server) [24].…”

Section: Owl Positioning System (Owlps)mentioning

confidence: 99%

Indoor positioning using CoLDE: An IEEE 802.11 connectionless extension

Oun

Bloch

Spies

2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Acquiring current position information from the GNSS is a straightforward procedure outdoors. Any mobile device, equipped with a GNSS receiver, having a line of sight with enough numbers of satellites can calculate its location. The mobile device does not need a full connection with any of these satellites. In the indoor positioning using the Wireless Ethernet IEEE 802.11 (Wi-Fi) standard, some accurate methods require the mobile device to send some initial data from its environment to a server, the server utilizes the data to calculate the exact location. The mobile device should be able to connect to this server, either by having an association with a Wi-Fi access point or using 3G/4G if the server is accessible from the Internet. In some cases, having a connection to the Internet is not possible, either because of the absence of the 3G/4G, or the Wi-Fi access points and the server are not for the public. This study aims to make the Wi-Fi indoor positioning systems available to all the mobile devices, exactly as the GPS, without even the need to be connected, either to the Internet, or to any of the Wi-Fi access points. In order to achieve this goal, we developed an extension, CoLDE (Connectionless Data Exchange), for the protocol IEEE 802.11. CoLDE utilizes the management frames to allow Wi-Fi devices and access points to exchange small amounts of data without having an association. Using CoLDE, the access points can relay the initial data (which they) received from the mobile device to the server, and send the exact position back to the Wi-Fi device. CoLDE can be utilized regardless of the used Wi-Fi indoor positioning. This paper gives the description and the implementation of the protocol used and the proposed method, in addition to the experimental results gathered in a feasibility study to validate some of the basic concepts of this approach.

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Refining WiFi Indoor Positioning Renders Pertinent Deploying Location-Based Multimedia Guide

Cited by 21 publications

References 7 publications

Advanced Real-Time Indoor Tracking Based on the Viterbi Algorithm and Semantic Data

Advanced Real-Time Indoor Tracking Based on the Viterbi Algorithm and Semantic Data

Assessing Occupants' Energy-load Variation in Commercial and Educational Buildings: Occupancy Detecting Approach Based on Existing Wireless Network Infrastructure

Indoor positioning using CoLDE: An IEEE 802.11 connectionless extension

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