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Smart Innovations in Communication and Computational Sciences pp 289–298Cite as

Fingerprint-Based Support Vector Machine for Indoor Positioning System

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 670))

Abstract

The position of a movable object is required in an indoor environment for providing various business interest services and for emergency services. The techniques implemented on WLAN (802.11b Wireless LANs) endow with more ubiquitous (Feng et al. in IEEE Trans Mob Comput 12(12), 2012, [1]) within the environment and the requirement for additional hardware is not necessary, thereby reducing infrastructure cost and enhancing the value of wireless data network. The received signal strength (RSS) from various reference points (RP) were recorded by a tool and fingerprint radio map is constructed. The signal property of a fingerprint will differ in each point. The location can be found by comparing the current signal strength with already collected radio maps. Almost all indoor environments are equipped with Wi-Fi devices. No additional hardware is required for the setup. In this paper, we introduce SVM classifier (Roos et al. in IEEE Trans Mob Comput 1(1), 59–69, 2002 [2]) as a methodology with minimum cost and without scarifying accuracy. The obtained results show minimal location error and accurate location of the object.

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Author information

Authors and Affiliations

  1. Sri Krishna College of Technology, Coimbatore, India

    A. Christy Jeba Malar

  2. Coimbatore Institute of Technology, Coimbatore, India

    Govardhan Kousalya

Authors
  1. A. Christy Jeba Malar
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  2. Govardhan Kousalya
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Corresponding author

Correspondence to A. Christy Jeba Malar .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

  3. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  4. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  5. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India

    Pradeep Kumar Singh

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Christy Jeba Malar, A., Kousalya, G. (2019). Fingerprint-Based Support Vector Machine for Indoor Positioning System. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-8971-8_26

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