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Locating in Crowdsourcing-Based DataSpace: Wireless Indoor Localization without Special Devices

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Abstract

Locating a target in an indoor social environment with a Mobile Network is important and difficult for location-based applications and services such as targeted advertisements, geosocial networking and emergency services. A number of radio-based solutions have been proposed. However, these solutions, more or less, require a special infrastructure or extensive pre-training of a site survey. Since people habitually carry their mobile devices with them, the opportunity using a large amount of crowd-sourced data on human behavior to design an indoor localization system is rapidly advancing. In this study, we first confirm the existence of crowd behavior and the fact that it can be recognized using location-based wireless mobility information. On this basis, we design “Locating in Crowdsourcing-based DataSpace” (LiCS) algorithm, which is based on sensing and analyzing wireless information. The process of LiCS is crowdsourcing-based. We implement the prototype system of LiCS. Experimental results show that LiCS achieves comparable location accuracy to previous approaches even without any special hardware.

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Notes

  1. A “special infrastructure” means that the infrastructure consists of customized equipment. LiCS is based on Received Signal Strength (RSS) that exists in any wireless equipment, so LiCS can be directly supported by existing wireless infrastructures around us.

  2. For a location-aware online social network, if B is in the friend list of A, we consider that there is friendship between A and B, and the relationship is directed.

  3. An Expectation-Maximization (EM) clustering algorithm [7] is used in this article. The EM assigns a probability distribution for each trace record (instance), which indicates the probability of each instance belonging to each of the clusters. The EM can automatically decide how many clusters to create.

  4. The collected data with anonymous mobile devices from Brightkite is used to correlate, model, evaluate and analyze the relationships between the check-in time, locations, friendship and crowd behavior of users in 772,966 distinct places. The data consists of 58,228 nodes (users) and 214,078 friend edges (friendship is directed between any two nodes).

  5. Even if the dataset is incomplete, it still can be used to show that “the impact of attributes (friendship and check-in locations) is existent on crowd behavior”.

  6. A triple can be denoted as [R S S,M A C T ,M A C R ]. For the specific RSS of a location, M A C T is the MAC address of corresponding signal transmitter and M A C R is the MAC address of corresponding signal receiver.

  7. In machine learning, using the hidden layer enables greater processing power and system flexibility. The nodes of hidden layer are named as hidden nodes. Hidden nodes are the nodes that are neither in the input layer nor the output layer. These nodes are essentially hidden from view, and their number and organization can typically be treated as a black box to people who are interfacing with the system.

  8. Cumulative Distribution Function describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. It can be formulated as F X (x)=P(X≤x).

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Acknowledgment

Lei Shu’s work is supported by the Guangdong University of Petrochemical Technology Internal Project (2012RC0106).

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Authors and Affiliations

  1. Institut Mines-Télécom, Télécom SudParis, Évry, France

    Yuanfang Chen, Antonio M. Ortiz & Noel Crespi

  2. Guangdong University of Petrochemical Technology, Maoming, Guangdong, China

    Lei Shu

  3. Dalian University of Technology, Dalian, China

    Lin Lv

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  1. Yuanfang Chen
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  2. Lei Shu
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  3. Antonio M. Ortiz
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  4. Noel Crespi
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  5. Lin Lv
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Correspondence to Yuanfang Chen.

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Chen, Y., Shu, L., Ortiz, A.M. et al. Locating in Crowdsourcing-Based DataSpace: Wireless Indoor Localization without Special Devices. Mobile Netw Appl 19, 534–542 (2014). https://doi.org/10.1007/s11036-014-0517-8

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  • DOI: https://doi.org/10.1007/s11036-014-0517-8

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