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Localization in Wireless LANs

Localization in Wireless LANs. Outline. Wireless LAN fundamentals Wi-Fi Scanner WLAN Localization Simple Point Matching Area Based Probability. Wireless LAN. Standard IEEE 802.11a IEEE 802.11b Also call “Wi-Fi” operating at 2.4 GHz 11 Mbps IEEE 802.11g operating at 2.4 GHz

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Localization in Wireless LANs

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  1. Localization in Wireless LANs

  2. Outline • Wireless LAN fundamentals • Wi-Fi Scanner • WLAN Localization • Simple Point Matching • Area Based Probability

  3. Wireless LAN • Standard • IEEE 802.11a • IEEE 802.11b • Also call “Wi-Fi” • operating at 2.4 GHz • 11 Mbps • IEEE 802.11g • operating at 2.4 GHz • 54 Mbps • Future Standard

  4. Wireless LAN • Information • MAC Address • Identifier of the Wireless LAN Access Point (AP) • Provided by the Ethernet LAN in the AP • RSSI • Signal Strength • SSID • Name of AP

  5. Wi-Fi Scanner • Platform • Pocket PC 2003(Windows CE 4.0) • Wi-Fi Network • IEEE 802.11b • API • Windows CE .NET 4.2 • Tools • Embedded Visual C++ 4.0 • Visual Studio .NET 2003

  6. Wi-Fi Scanner • Target • Get two unique information • MAC Address • Signal Strength • Future Application • Develop the 2D Location Algorithm • Provide the Multimedia Services (e.g. streaming service)

  7. Wi-Fi Scanner • Overall Architecture Core Operations User Interfcae Embedded VC++ Visual .NET 2003 DLL (dynamic link library )

  8. Wi-Fi Scanner Wi-Fi Application Network Driver Interface Specification (NDIS) MAC Address, Signal Strength

  9. NDIS • Develop the Network Driver • Support varieties of Network Technology (e.g. Ethernet (IEEE 802.3), Token Ring (IEEE 802.5), and IrDA media) • Portability of drivers between platforms that support NDIS • Network adapter miniport driver

  10. NDIS Architecture Miniport driver -communicate directly with network interface card (NIC)

  11. NDIS User-mode I/O(NDISUIO) • Supports sending and receiving Ethernet frames • Retrieve MediaSense indications • Retrieve signal power indications

  12. NDIS User-mode I/O • Steps to bind the NIC Card • Get Ethernet LAN Adapter (NIC) Name • Create the File Handle to bind NDISUIO • Using the “DeviceIoControl” interface to achieve the required packet

  13. NDIS User-mode I/O • fRetVal = DeviceIoControl( hNdisUio, IOCTL_NDISUIO_QUERY_OID_VALUE, (LPVOID) pQueryOid, dwQueryBufferSize, (LPVOID) pQueryOid, dwQueryBufferSize, &dwBytesReturned, NULL); Retrieves NDIS object

  14. NDIS User-mode I/O • struct _NDIS_WLAN_BSSID{ ULONGLength; NDIS_802_11_MAC_ADDRESSMacAddress; UcharReserved[2]; NDIS_802_11_SSIDSsid; ULONG Privacy; NDIS_802_11_RSSIRssi; NDIS_802_11_NETWORK_TYPENetworkTypeInUse; NDIS_802_11_CONFIGURATIONConfiguration;NDIS_802_11_NETWORK_INFRASTRUCTURE InfrastructureMode; NDIS_802_11_RATESSupportedRates; } NDIS_WLAN_BSSID, *PNDIS_WLAN_BSSID;

  15. WLAN Localization • Point-based approach • Localization goal is to return a single point for the mobile object • Area-based approach • Localization goal is to return the possible locations of the mobile object as an area rather than a single point

  16. Area-based Approach

  17. Advantage of Area-based Approach • Direct the user in the search for an object in a more systematic manner • Presents the user an understanding of the system in a more natural and intuitive manner

  18. Some Terms and Definitions • n Access Points • AP1, AP2, …, APn • Training set T0 • the offline measured signal strengths and locations an algorithm uses • Consists of a set of fingerprints (Si) at m different areas Ai • T0 = ( Ai, Si ), i = 1 … m

  19. Some Terms and Definitions • Fingerprints Si • Set of n signal strengths at Ai, one per each access point • The are totally n access points • Si = (si1, …, sin), where sij is the expected average signal strength from APj

  20. Generating Training Set • In one particular Ai, we read a series of signal strengths (sijk ) for a particular APj with a constant time between samples • k = 1… oij ,where oij is the number of samples from APj at Ai • We estimate sij by averaging the series, {sij1, sij2…, sijo }

  21. Generating Training Set • We do the same for all n APs, so we have the fingerprints at Ai, • Si = (si1, …, sin) • We do the same for all m areas, so we have the training set • T0 = ( Ai, Si ), i = 1… m

  22. Getting Testing Set • The object to be localized collects a set of received signal strengths (RSS) when it is at certain location • A testing set(St) is created similar to the fingerprints in the training set • It is a set of average signal strengths from n APs, St = (st1, …, stn)

  23. Area-based Approach Algorithms • How to use the training set and testing set? • Simple Point Matching • Area Based Probability

  24. Simple Point Matching • Compare the received signal strength (RRS) in the training set and the testing set • Find n set of areas that fall within a threshold of the RSS for each APj , j =1…n • The RSS with threshold for APj at position i = sij ±q • Return the areas formed by intersecting all matched areas from different AP area sets

  25. Simple Point Matching • How to choose the threshold? • q is the standard deviation of signal received from the corresponding AP • The algorithm starts with a very small q • Area sets for some AP may be empty • q is additively increases eg. q, 2q, 3q …

  26. SPM algorithm

  27. Area Based Probability • Goal is to return the area with the highest probability that the object is in • Approach is to compute the likelihood of the testing set (St) that matches the fingerprint for each area (Si)

  28. Area Based Probability Assumptions: • Signal received from different APs are independent • For each APj, j = 1…n, the sequence of RSS sijk, k = 1… oij, at each Ai in To is modeled as a Gaussian distribution

  29. Bayes’ rule • We compute the probability of being at different areas Ai, on given the testing set St • P(Ai |St) = P(St |Ai)* P(Ai)/ P(St) (1) • P(St) is a constant • Assume the object is equally likely to be at any location. P(Ai) is a constant • P(Ai |St) = c*P(St |Ai) (2)

  30. Area Based Probability • We compute P(St |Ai) for every area Ai ,i=1…m,using the Gaussian assumption • Max{P(Ai |St) } = Max{c*P(St |Ai) } = Max{P(St |Ai) } • Return the area Ai with top probability

  31. Area Based Probability

  32. Reference • Eiman Elnahrawy, Xiaoyan Li, Richard P. Martin ,Using Area-based Presentations and Metrics for Localization Systems in Wireless LANs, Department of Computer Science, Rutgers University • Andreas Haeberlen, Eliot Flannery, Andrew M. Ladd, Algis Rudys, Dan S. Wallach and Lydia E. Kavraki, Practical Robust Localization over Large-Scale 802.11 Wireless Networks,Rice University

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