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Localization in Wireless LANs
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Outline Wireless LAN fundamentals Wi-Fi Scanner WLAN Localization Simple Point Matching Area Based Probability
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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
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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
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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
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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)
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Wi-Fi Scanner Overall Architecture User InterfcaeCore Operations Embedded VC++ DLL (dynamic link library ) Visual.NET 2003
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Wi-Fi Scanner Wi-Fi Application MAC Address, Signal Strength Network Driver Interface Specification (NDIS) Application Presentation Session Transport Network Data Link Physical
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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
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NDIS Architecture Miniport driver -communicate directly with network interface card (NIC)
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NDIS User-mode I/O(NDISUIO) Supports sending and receiving Ethernet frames Retrieve MediaSense indications Retrieve signal power indications
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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
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NDIS User-mode I/O fRetVal = DeviceIoControl( hNdisUio, hNdisUio,IOCTL_NDISUIO_QUERY_OID_VALUE, (LPVOID) pQueryOid, dwQueryBufferSize, dwQueryBufferSize,&dwBytesReturned,NULL); Retrieves NDIS object
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NDIS User-mode I/O struct _NDIS_WLAN_BSSID { ULONG Length; NDIS_802_11_MAC_ADDRESS MacAddress; Uchar Reserved[2]; NDIS_802_11_SSID Ssid; ULONG Privacy; NDIS_802_11_RSSI Rssi; NDIS_802_11_NETWORK_TYPE NetworkTypeInUse; NDIS_802_11_CONFIGURATION Configuration; NDIS_802_11_NETWORK_INFRASTRUCTURE InfrastructureMode; NDIS_802_11_RATES SupportedRates; } NDIS_WLAN_BSSID, *PNDIS_WLAN_BSSID;
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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
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Area-based Approach
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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
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Some Terms and Definitions n Access Points AP 1, AP 2, …, AP n Training set T 0 the offline measured signal strengths and locations an algorithm uses Consists of a set of fingerprints (S i ) at m different areas A i T 0 = ( A i, S i ), i = 1 … m
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Some Terms and Definitions Fingerprints S i Set of n signal strengths at A i, one per each access point The are totally n access points S i = (s i1, …, s in ), where s ij is the expected average signal strength from AP j
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Generating Training Set In one particular A i, we read a series of signal strengths (s ijk ) for a particular AP j with a constant time between samples k = 1… o ij,where o ij is the number of samples from AP j at A i We estimate s ij by averaging the series, {s ij1, s ij2 …, s ijo }
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Generating Training Set We do the same for all n APs, so we have the fingerprints at A i, S i = (s i1, …, s in ) We do the same for all m areas, so we have the training set T 0 = ( A i, S i ), i = 1… m
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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(S t ) is created similar to the fingerprints in the training set It is a set of average signal strengths from n APs, S t = (s t1, …, s tn )
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Area-based Approach Algorithms How to use the training set and testing set? Simple Point Matching Area Based Probability
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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 AP j, j =1…n The RSS with threshold for AP j at position i = s ij ±q Return the areas formed by intersecting all matched areas from different AP area sets
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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 …
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SPM algorithm
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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 (S t ) that matches the fingerprint for each area (S i )
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Area Based Probability Assumptions: Signal received from different APs are independent For each AP j, j = 1…n, the sequence of RSS s ijk, k = 1… o ij, at each A i in T o is modeled as a Gaussian distribution
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Bayes’ rule We compute the probability of being at different areas A i, on given the testing set S t P(A i |S t ) = P(S t |A i )* P(A i )/ P(S t ) (1) P(S t ) is a constant Assume the object is equally likely to be at any location. P(A i ) is a constant P(A i |S t ) = c*P(S t |A i )(2)
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Area Based Probability We compute P(S t |A i ) for every area A i,i=1…m,using the Gaussian assumption Max{P(A i |S t ) } = Max{c*P(S t |A i ) } = Max{P(S t |A i ) } = Max{P(S t |A i ) } Return the area A i with top probability
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Area Based Probability
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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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