AU2011232813B2 - Location beacon database and server, method of building location beacon database, and location based service using same - Google Patents

Abstract

C \RPonbADCC\DER\1913_ I DOC-7/111/2I111 A system comprising: computer-implemented logic to derive position information for each of a plurality 5 of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi access point, and location data characterizing a location at which the one or more 10 characteristics of the signal received from the Wi-Fi access point was observed; and computer-implemented logic to store the derived position information in a non-transitory computer-readable medium.

Description

Regulation 3.2 AUSTRALIA Patents Act 1990 COMPLETE SPECIFICATION FOR A STANDARD PATENT (ORIGINAL) Name of Applicant: Skyhook Wireless, Inc., of 34 Farnsworth Street, 5th Floor, Boston, Massachusetts 02210, United States of America Actual Inventors: ALIZADEH-SHABDIZ, Farshid SHEAN, Michael George MORGAN, Edward James JONES, Russel Kipp Address for Service: DAVIES COLLISON CAVE, Patent Attorneys, of I Nicholson Street, Melbourne 3000, Victoria, Australia Invention Title: Location beacon database and server, method of building location beacon database, and location based service using same The following statement is a full description of this invention, including the best method of performing it known to us: C \NRPonbl\DCC\DER\3920137_ I DOC - 7/10/11 C NRPonbl\DCC\DER3909'61 DOC-7/10/2011 -1 LOCATION BEACON DATABASE AND SERVER, METHOD OF BUILDING LOCATION BEACON DATABASE, AND LOCATION BASED SERVICE USING SAME 5 Cross-Reference To Related Applications 100011 This application claims the benefit under 35 U.S.C. § 1I9(e) of U.S. Provisional Patent Application No. 60/623,108, filed on October 29, 2004, entitled Wireless Data Scanning Network for Building Location Beacon Database, which is herein incorporated by reference in its entirety. 10 100021 This application is related to the following U.S. Patent Applications (Nos. TBA), filed on an even date herewith, entitled as follows: Location Beacon Database; Server for Updating Location Beacon Database; Location-Based Services that Choose Location Algorithms Based on Number of 15 Detected Access Points Within Range of User Device; and Method and System for Building a Location Beacon Database. Background I. Field of the Invention 20 10003] The invention generally related to location-base services and, more specifically, to methods and systems of determining locations of Wi-Fi access points and using such information to locate a Wi-Fi-enabled device. 2. Discussion of Related Art 25 100041 In recent years the number of mobile computing devices has increased dramatically creating the need for more advanced mobile and wireless services. Mobile email, walkie-talkie services, multi-player gaming and call following are examples of how new applications are emerging on mobile devices. In addition, users are beginning to demand/seek applications that not only utilize their current location but also share that 30 location information with others. Parents wish to keep track of their children, supervisors need to track the location of the company's delivery vehicles, and a business traveller looks C \NRPonbPDCC\DERW919 I.DOC-7110/2mi1 -2 to find the nearest pharmacy to pick up a prescription. All of these examples require the individual to know their own current location or that of someone else. To date, we all rely on asking for directions, calling someone to ask their whereabouts or having workers check-in from time to time with their position. 5 [00051 Location-based services are an emerging area of mobile applications that leverages the ability of new devices to calculate their current geographic position and report that to a user or to a service. Some examples of these services include local weather, traffic updates, driving directions, child trackers, buddy finders and urban concierge services. These new location sensitive devices rely on a variety of technologies that all use 10 the same general concept. Using radio signals coming from known reference points, these devices can mathematically calculate the user's position relative to these reference points. Each of these approaches has its strengths and weaknesses based on the radio technology and the positioning algorithms they employ. [00061 The Global Positioning System (GPS) operated by the US Government 15 leverages dozens of orbiting satellites as reference points. These satellites broadcast radio signals that are picked up by GPS receivers. The receivers measure the time it took for that signal to reach to the receiver. After receiving signals from three or more GPS satellites the receiver can triangulate its position on the globe. For the system to work effectively, the radio signals must reach the received with little or no interference. Weather, buildings or 20 structures and foliage can cause interference because the receivers require a clear line-of sight to three or more satellites. Interference can also be caused by a phenomenon known as multi-path. The radio signals from the satellites bounce off physical structures causing multiple signals from the same satellite to reach a receiver at different times. Since the receiver's calculation is based on the time the signal took to reach the receiver, multi-path 25 signals confuse the receiver and cause substantial errors. 100071 Cell tower triangulation is another method used by wireless and cellular carriers to determine a user or device's location. The wireless network and the handheld device communicate with each other to share signal information that the network can use to calculate the location of the device. This approach was originally seen as a superior 30 model to GPS since these signals do not require direct line of site and can penetrate buildings better. Unfortunately these approaches have proven to be suboptimal due to the C:\NRPortbKlCODER\399%3_. DOC-7/1N201l -3 heterogeneous nature of the cellular tower hardware along with the issues of multi-path signals and the lack of uniformity in the positioning of cellular towers. 100081 Assisted GPS is a newer model that combines both GPS and cellular tower techniques to produce a more accurate and reliable location calculation for mobile users. In 5 this model, the wireless network attempts to help GPS improve its signal reception by transmitting information about the clock offsets of the GPS satellites and the general location of the user based on the location of the connected cell tower. These techniques can help GPS receivers deal with weaker signals that one experiences indoors and helps the receiver obtain a 'fix' on the closest satellites quicker providing a faster "first reading". 10 These systems have been plagued by slow response times and poor accuracy - greater than 100 meters in downtown areas. 10009] There have been some more recent alternative models developed to try and address the known issues with GPS, A-GPS and cell tower positioning. One of them, known as TV-GPS, utilizes signals from television broadcast towers. (See, e.g., 15 Muthukrishnan, Maria Lijding, Paul Havinga, Towards Smart Surroundings: Enabling Techniques and Technologies for Localization, Lecture Notes in Computer Science, Volume 3479, Jan 2 Hazas, M., Scott, J., Krumm, J.: Location-Aware Computing Comes of Age. IEEE Computer, 37(2):95-97, Feb 2004 005, Pa005, Pages 350-362.) The concept relies on the fact that most metropolitan areas have 3 or more TV broadcast towers. A 20 proprietary hardware chip receives TV signals from these various towers and uses the known positions of these towers as reference points. The challenges facing this model are the cost of the new hardware receiver and the limitations of using such a small set of reference points. For example, if a user is outside the perimeter of towers, the system has a difficult time providing reasonable accuracy. The classic example is a user along the 25 shoreline. Since there are no TV towers out in the ocean, there is no way to provide reference symmetry among the reference points resulting in a calculated positioning well inland of the user. 100101 Microsoft Corporation and Intel Corporation (via a research group known as PlaceLab) have deployed a Wi-Fi Location system using the access point locations 30 acquired from amateur scanners (known as "wardrivers") who submit their Wi-Fi scan data to public community web sites. (See, e.g., LaMarca, A., et. al., Place Lab: Device C \NRortbhDCC\DER091)9%1 l D0C-7/111/21 1 -4 Positioning Using Radio Beacons in the Wild.) Examples include WiGLE, Wi FiMaps.com, Netstumbler.com and NodeDB. Both Microsoft and Intel have developed their own client software that utilizes this public wardriving data as reference locations. Because individuals voluntarily supply the data the systems suffer a number of 5 performance and reliability problems. First, the data across the databases are not contemporaneous; some of the data is new while other portions are 3-4 years old. The age of the access point location is important since over time access points can be moved or taken offline. Second, the data is acquired using a variety of hardware and software configurations. Every 802.11 radio and antenna has different signal reception 10 characteristics affecting the representation of the strength of the signal. Each scanning software implementation scans for Wi-Fi signals in different ways during different time intervals. Third, the user-supplied data suffers from arterial bias. Because the data is self reported by individuals who are not following designed scanning routes, the data tends to aggregate around heavily traffic areas. Arterial bias causes a resulting location pull towards 15 main arteries regardless of where the user is currently located causing substantial accuracy errors. Fourth, these databases include the calculated position of scanned access.points rather than the raw scanning data obtained by the 802.11 hardware. Each of these databases calculates the access point location differently and each with a rudimentary weighted average formula. The result is that many access points are indicated as being 20 located far from their actual locations including some access points being incorrectly indicated as if they were located in bodies of water. 100111 There have been a number of commercial offerings of Wi-Fi location systems targeted at indoor positioning. (See, e.g., Kavitha Muthukrishnan, Maria Lijding, Paul Havinga, Towards Smart Surroundings: Enabling Techniques and Technologies for 25 Localization, Lecture Notes in Computer Science, Volume 3479, Jan 2Hazas, M., Scott, J., Krumm, J.: Location-Aware Computing Comes of Age. IEEE Computer, 37(2):95-97, Feb 2004005, Pa005, Pages 350-362.) These systems are designed to address asset and people tracking within a controlled environment like a corporate campus, a hospital facility or a shipping yard. The classic example is having a system that can monitor the exact location 30 of the crash cart within the hospital so that when there is a cardiac arrest the hospital staff doesn't waste time locating the device. The accuracy requirements for these use cases are C .NRPonbl\DCC\DERU919% I DOC-7/11012011 -5 very demanding typically calling for 1-3 meter accuracy. These systems use a variety of techniques to fine tune their accuracy including conducting detailed site surveys of every square foot of the campus to measure radio signal propagation. They also require a constant network connection so that the access point and the client radio can exchange 5 synchronization information similar to how A-GPS works. While these systems are becoming more reliable for these indoor use cases, they are ineffective in any wide-area deployment. It is impossible to conduct the kind of detailed site survey required across an entire city and there is no way to rely on a constant communication channel with 802. 11 access points across an entire metropolitan area to the extent required by these systems. 10 Most importantly outdoor radio propagation is fundamentally different than indoor radio propagation rendering these indoor positioning algorithms almost useless in a wide-area scenario. 100121 There are numerous 802.11 location scanning clients available that record the presence of 802.11 signals along with a GPS location reading. These software 15 applications are operated manually and produce a log file of the readings. Examples of these applications are Netstumber, Kismet and Wi-FiFoFum. Some hobbyists use these applications to mark the locations of 802.1 1 access point signals they detect and share them with each other. The management of this data and the sharing of the information is all done manually. These application do not perform any calculation as to the physical 20 location of the access point, they merely mark the location from which the access point was detected. 100131 Performance and reliability of the underlying positioning system are the key drivers to the successful deployment of any location based service. Performance refers to the accuracy levels that the system achieves for that given use case. Reliability refers to the 25 percentage of time that the desired performance levels are achieved.

C\NRPortlbDCC\DERU'9)9%61_1 DOC-7/lW1201l -6 Performance Reliability Local Search/Advertising <100 meters 85% of the time E91 I <150 meters 95% of the time Turn-by-turn driving directions 10-20 meters 95% of the time Gaming <50 meters 90% of the time Friend finders <500 meters 80% of the time Fleet management <10 meters 95% of the time Indoor asset tracking <3 meters 95% of the time Summary 100141 According to the present invention there is provided a system comprising: 5 computer-implemented logic to derive position information for each of a plurality of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi 10 access point, and location data characterizing a location at which the one or more characteristics of the signal received from the Wi-Fi access point was observed; and computer-implemented logic to store the derived position information in a non-transitory computer-readable medium. [0014A] The invention also provides a computer-implemented method comprising: 15 deriving position information for each of a plurality of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi access point, and location data 20 characterizing a location at which the one or more characteristics of the signal received from the Wi-Fi access point was observed; and C-NRPonbI\DCC\DER\3919%1_I DOC-7/1I1)2011 - 6A storing the derived position information in a non-transitory computer-readable medium. [0014B] The invention also provides software, embodied in a non-transitory computer-readable medium, comprising instructions executable by a processor to: 5 derive position information for each of a plurality of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi access point, and location data 10 characterizing a location at which the one or more characteristics of the signal received from the Wi-Fi access point was observed; and store the derived position information in a non-transitory computer-readable medium. 10014C] Features and aspects of embodiments of the invention are summarised 15 below. [00151 Under one aspect of the invention, Wi-Fi access points are located in a target geographical area to build a reference database of locations of Wi-Fi access points. At least one vehicle is deployed including at least one scanning device having a GPS device and a Wi-Fi radio device and including a Wi-Fi antenna system. The target area is 20 traversed in a programmatic route to avoid arterial bias. The programmatic route includes substantially all drivable streets in the target geographical area and solves an Eulerian cycle problem of a graph represented by said drivable streets. While traversing the target area, periodically receive the GPS coordinates of the GPS device. While traversing the target area, detecting Wi-Fi signals from Wi-Fi access points in range of the Wi-Fi device 25 and recording identity information of the detected Wi-Fi access point in conjunction with GPS location information of the vehicle when the detection of the Wi-Fi access point was made. The location information is used to reverse triangulate the position of the detected Wi-Fi access point; and the position of the detected access point is recorded in a reference database. 30 [00161 Under another aspect of the invention, the target geographic area has a radius on the order of tens of miles.

C \NRPobl\DCC ERV I91%3 DOC7/1 W/2 I I -7 100171 Under another aspect of the invention, the programmatic route is determined by using the Chinese postman routing algorithm. [00181 Under another aspect of the invention, when detecting Wi-Fi signals, several readings of Wi-Fi signals are made for a given Wi-Fi access point so that a set of 5 readings is used when determining the position of the detected Wi-Fi access point. 100191 Under another aspect of the invention, when detecting a Wi-Fi access point signal strength information about the signal from the Wi-Fi access point is calculated and recorded by the scanning device. 100201 Under another aspect of the invention, a given Wi-Fi access point is 10 detected from as many different angles as possible given the organization of the drivable streets. 100211 Under another aspect of the invention, a given Wi-Fi access point is detected from as many different angles as possible given the organization of the drivable streets, and wherein a power profile of signal strengths for a given Wi-Fi access point is 15 determined and recorded. 100221 Under another aspect of the invention, the Wi-Fi antenna system includes directional antennas and wherein the scanning device records a vector of origin of a Wi-Fi access point based on different signal strengths received by the Wi-Fi access point on the directional antennas. 20 100231 Under another aspect of the invention, access points are located for a plurality of target areas. 10024] Under another aspect of the invention, a user-device having a Wi-Fi radio may be located. A reference database of calculated locations of Wi-Fi access points in a target area is provided. In response to a user application request to determine a location of 25 a user-device having a Wi-Fi radio, the Wi-Fi device is triggered to transmit a request to all Wi-Fi access points within range of the Wi-Fi device. Messages are received from the Wi Fi access points within range of the Wi-Fi device, each message identifying the Wi-Fi access point sending the message. The signal strength of the messages received by the Wi Fi access points is calculated. The reference database is accessed to obtain the calculated 30 locations for the identified Wi-Fi access points. Based on the number of Wi-Fi access points identified via received messages, choosing a corresponding location-determination algorithm from a plurality of location-determination algorithms, said chosen algorithm being suited for the number of identified Wi-Fi access points. The calculated locations for the identified Wi-Fi access points and the signal strengths of said received messages and the chosen location-detennination algorithm are used to determine the location of the user-device. (0025] Under another aspect of the invention, the calculated locations for the identified Wi-Fi access points are filtered to determine if the corresponding Wi-M access points have moved since the time the information about the Wi-F access points was included in the reference database. [0026] Under another aspect of the invention, the phrality of location detenminaton algorithms includes a simple signal strength weighted average model. [0027] Under another aspect of the invention, the plurality of location detunnination algorithms includes a nearest neighbor modeL [0028] Under another aspect of the invention, the plurality of location determination algorithms includes a triangulation technique. [0029] Under another aspect of the invention, the plurality of location determinatin algorithms includes an adaptive smoothing technique based on the device velocity. [0030] Under another aspect of the invention, the choice of location-determination algorithm is further based on the user application nasking the location request [0031] Under another aspect of the Invention, a database of Wi-H access points for at least one target area having a radius on the order of tens of miles is recorded in a camputer-readable medium. It Includes database records for substantially all Wi-Fl access points in the target areach record including Idenifcation information for a conesponding Wi-Fi access point and calculated position information for the corresponding WI-F access point, wherein said calculated position information is obtained from recording multiple readings of the WI-Fi access point to provide reference symmetry when calculating the position of the Wi-FH access point and to avoid arterial bias in the calculated position information. [0032] Under another aspect of the invention, a Wi-Fi location sewer includes a database of WI-F access points for at least one target area having a radius on the order of ts of miles, said database being recorded in a computer-readable medium and including database records for substantially all Wi-Fl access points in the target area, each record including identification information for a corresponding WI- access point and calculated position information for the corresponding Wi-Fr access point, 8 wherein said calculated position information is obtained from recording multiple readings of the WI-Fi access point to provide reference symmetry when calculating the position of the Wi-F access point and to avoid arterial bias in the calculated position information. The server also includes computer-Implemented logic to add records to the database for newly-discovered Wi-Fi access points said computer logic including logic to recalculate position information for Wi-Fl access points previously stored in the database to utilize the position information for the newly-discovered WI P1 access points. [00333 Under another aspect of the invention, the server includes computer implemented clustering logio to Identify position information based on error prone GPS information. [0034) Under another aspect of the invention, the clustering logic includes logic to determine a weighted centroid position ftr all position information reported far an access point and logic to Identify position information that exceeds a statistically based deviation threshold amount away from the centrold position and excludes such deviating position information from the database and from influencing the calculated positions of the WI-fl access points. Brief Description Of Drawings (0035] In the drawing, Figure 1 depicts certain embodiments of aWi-Fi positioning system; Figure 2 depicts scanning vehicles including scanning devices according to certain embodiments of the invention; Figure 3 depicts an example of a scanning scenario to illustrate the problem of arterial bias in data collection; Figure 4 depicts an example using a programmatic route for a scanning vehicle according to certain embodiments of the invention; Figure 5 depicts an example scenario to illustrate the problem of lack of reference symmetry of WI-F access points in locating a user device; 9 Figure 6 depicts an example scenario to illustrate reference symmetry of WI-Fi access points in locating a user device; Figure 7 depicts scanning vehicles including scanning devices according to certain embodiments of the invention; Figure 8 depicts a central network server including a central database of Wi-Fi access points according to certain embodiments of the invention; Figure 9 depicts an exemplary architecture of positioning software according to certain embodiments of the invention; Figure 10 depicts an exemplary architecture of a scanning client according to certain embodniments of the invention; and Figure 11 compares and contrasts the effects of a random scanning model with one using a model from the Chinese postman routing algorithm. Detailed Description [0036] Preferred embodiments of the present Invention provide a system and a methodology for gathering reference location data to enable a commercial positioning system using public and private 802.11 access points. Preferably, the data is gathered in a programmatic way to fully explore and cover the streets of a target region. The programmatin approach'identifies as many Wi-Fi access points as possible. By gathering location information about more access points, preferred embodiments not only provide a larger collection of location information about access points, but the location information for each access point may be calculated with more precision. Subsequently this larger set of more precise data may be used by location services to more precisely locate a user device utilizing preferred embodiments of the invention. Certain embodiments use techniques to avoid erroneous data in dAenining the Wi-fi positions and use newly-discovered position information to improve the quality of previously gathered and determined position information. Certain embodiments use location-deternlnation algorithms based on the context of the user device at the time 10 the user requests a location. For example, the location-determination algorithm will be based on the number of Wi-Fl access points identified or detected when a location request is made, or based on the application making the request [00371 Figure 1 depicts aportion of a preferred embodiment of a Wi-Fi positioning system (WPS). The positioning system includes positioning software [103] that resides on a computing device [101]. Throughout a particular coverage area there are fixed wireless access points (102] that broadcast information using control/common channel broadcast signals. The client device monitors the broadcast signal or requests its transmission via a probe request Each access point contains a unique hardware identifer known as a MAC address. The client positioning software receives signal beacons from the 802.11 access points in range and calculates the geographic location of the computing device using characteristics from the signal beacons. Those characteristics include the unique identifier of the 802.11 access point known as the MAC address, and the strengths of the signal reaching the client device. The client software compams the observed 802.11 access points with those in its reference database [104] of access points, which may or may not reside on the device as well. The reference database contains the calculated geographic locations or power profile of all the access points the gathering system has collected. The power profile is a collection of readings that represent the power of the signal from various locations. Using these known locations, the client software calculates the relative position of the user device [101] and determines its geographic coordinate in the form of latitude and longitnde readings. Those reading are then fed to location- based applications such as friend finders, local search web sites, fleet management systems and E911 services. (0038] The positioning software is described in greater detail with reference to Figure 9, which depict exemplary components of positioning software 103. Typically there is an application or service [901J that utilizes location readings to provide some value to an end user (example, driving directions). This location application maes a request of the positioning software for the location of the device at that particular moinent. That request Initiates the scanner [902], which makes a "scan request" to the 802.11 radio [903] on the device. The 802.11 radio sends out a probe request to all 802.11 access points [904] within range. According to the 802.11 protocol, those 11' access points in receipt of a probe request will transmit a broadcast beacon containing information about the access point That beacon includes the MAC address of the device, the network name, the precise version of the protocol that it supports and its security configuration along with intonation about how to connect to the device. The 802.11 radio collects this infonnation from each access point that responds, calculates the signal strength of each access point and sends that back to the scanner. [0039] The scanner passes this array of access points to the Locator [906] which checks the MAC addresses of each observed access point against the Access Point Reference Database (905]. This database can either be located on the device or remotely over a network conection. The Access Point Reference Database returns the location data for each of the observed access points that are known to the system. The Locator passes this collection of location information along with the signal characteristics returned from each access point to the Bad Data Filter [907]. This filter applies a number of comparison tests against each access point to determine If any of the access points have moved since they were added to the access point database. After removing bad data records, the Filter sends the remaining access points to the Location Calculation component [908]. Using the reference data from the access point database and the signal strength readings from the Scanner, the Location Calculation component computes the location of the device at that moment. Before that location data is sent back to the Locator, it is ptocessed by the Smoothing engine [9093 which averages a past sees of location readings to remove any enmtic readings from the previous calculation. The adjusted location data is then sent back to the Locator. [0040] The calculated location readings produced by the Locator are communated to these location-based applications [9011 through the Application Interface [910] which includes an application programming interface (API) or via a virtual OPS capability [911]. MPS receivers communicate their location readings using proprietary massages or using the location standard like the one developed by the National Marine Electronics Associatiou (NMA). Connecting into the device using a standard interface such as a COM port on the machine retrieves the messages. Certain embodiments of the invention Include a virtual GPS capability that allows any 12 GPS compatible application to cmmnncate with this new positioning system without have to alter the communication model or messages, [0041] The location calculations are produced using a series of positoning algorithms intended to tum noisy data flows into reliable and steady location readings. The client software compares the list of obserTed access points along with their calculated signal strengths to weight the location of user to determine precise location of the device user. A variety of techniques am employed including simple signal stregth weighted average models, nearest neighbor models combined with triangulation techniques and adaptive smoothing based on device velocity. Different algorithms perform better under different scenarios and tend to be used together in hybrid deployments to product the most accurate final readings. Preferred embodiments of the invention can use a number of positioning algorithms. The decision of which algorithm to use is driven by the number of access points observed and the user case application using it The filtering models differ from traditional positioning systems since traditional systems rely on known reference points that never move. In the model of preferred embodiments, this assumption of fixed locations of access points is not made; the access points are not owned by the positioning system so they may move or be taken offline. The filtering techniques assume that some acess points may no longer be located In the same place and could cause a bad location calculation. So the filtering algorithms attempt to isolate the access points that have moved since their position was recorded. The filters are dynamic and change based on the number of access points observed at that moment The smoothing algorithms include simple position averaging as well as advanced bayesian logic Including Klnman filters. The velocity algorithms calculate device speed by estimating the Doppler effect from the signal strength observations of each access point Gathering of scan data to build rfdrence database [0042] Figure 2 depicts the components used to gather location information for the various access points. A large fleet of vehicles [201] is deployed to build the reference database (104 of figure 1) for the positinng system. These vehicles 201 follow a programmatic route through target scan areas to gather data in the most 13 optimal fashion producing the highest quality data. The target scan areas typically represent a large metropolitan area including every single drivable street in 15-20 mile radius. These vehicles are equipped with scanning devices [202] designed to record the locations and characteristics of 802.11 signals while tmaveaing the coverage area. The scanning devices track the location of the scanning vehicle every second using signal from GPS satellites [204]. The scanning device also tracks the presence of any 802.11 access point within range and records the radio characteristics of that access point signal along with the GPS location of the scanning vehicle. TIe quality of the data collected is greatly affected by the scanning methodology employed by the scanning vehicles. Each model has its own benefits and limitations. One approach, known as the Random Model, places scanning devices in vehicles as they are conducting daily activities for business or personal use. These vehicles could be delivery trucks, taxi cabs, traveling salesman or just hobbyists. The concept is that over time these vehicles will cover enough streets in their own random fasbion'In order to build a reliable reference database. The model does in fact provide a simple mes to collect data but the quality of the resulting datais g ively affected ue to issues of "artedal bias". Figure 3 describes the challenge of the random model. When scanning vehicles traverse routes designed to solve other problems than gathering data (e.& delivering packages, people commuting to and fkom work) they tend to follow destination routes. A destination route Is when a driver needs to get from A to B and seeks the fastest route to get there, So the driver looks for the shortest route to the nearest main artery whether it be a highway or a main thoroughfare. As a result, over time the random driving covers more and more ground by the cumulative coverage shows a bias to the main roads, or arteries at the expense of the smaller and surrounding roads, In figure 3, arteries [304] and [305] aie heavily traversed by the scanning vehicles resulting in a healthy amount of scanning data for those streets. But streets [306] and [307 are rarely, if ever, covered because there is no frequent destination on those streets and the arteries are more optimal travel roads. The result is that access points [3081 and [309] are not scanned at all by the scanning vehicles so the positioning system will struggle to identify a user who is traveling on streets [306] and [307]. The result is that when the system attempts to calculate the location of the access point from the scan data it is limited to a biased collection of input data. Figure 14 11 shows the difference in resulting data quality. As the scanning vehicle drives near the Access Point [1101], it records a reading and its location continuously. ITe positioning system must then calculate the location of the Access Point [1102] using the entire set of observed data [1103). In the Random Scanning model the set of data is limited to one main road passing by the access point. That forces the system to calculate the access point's location near that road rather than close to the access point Itself. [0043] Another approach is develop routing algorithms that include every single street in the target area so as to avoid arterial bias in the resulting collection of data thus producing a more reliable positioning system for the end users. FIgure 4 describes an optimized routing algorithm known as the Chinese Postman to calculate the most efficient driving route for covering every single street in a target area. The Chinese Postman routing algorithm Is a known technique used by postal agencies, utilities and census agencies and is a variant of the Enlerian cycle problem. The Eulerian cycle is a problem sting for the shortest tour of a graph which visits each edge at least once. (See, e.g., Kwan, M. K. "Graphic Programming Using Odd or Even Points." Chinese Madh 1, 273-277, 1962,.) Preferred embodiments of the Invention include a methodology for identifying a target region for coverage and then using the Chine Postman routing algorithm for planning the vehicle route. The scanning vehicle (401] follows the optimal route according to the algorithm showing no bias to any street ensuring that all observable access points are detected and mapped by the system. So, by way of example, access points [4081 and [409] are added to the access point database using the Chinese Postman unodel but would have been missed using the Random model. Referring back to Figure 11, with the Chinese Postman Scanning model, the vehicle travels every single road getting as complete a set of scning records [1106] fr the Access Point [1104. The system can then calculate the location [11052 of the access point with less error since it has a mom uniform distribution of scan data for access point 1104 than for access 1102. So the Chinese Postman Scanning model not only gathers morm access points unifounly across a target area but the resulting data produces more accurate calculations of access point locations. 15 Higher quality AP Locations [0044] Once collected (or partially collected), the scanning data is uploaded back to a central access point database (described later in this application) where It is processed. The raw observation points for each access point are used to reverse triagulate the actual physical location of the access points or create a power profie representing the radio propagation of that access point In order to produce the most accurate calculated location for a particular access points or to create the most accurate power profile, the scanning vehicle must observe the access point from as many different angles as possible. In the random model [figure 33, many access points are observed from only one street forcing the system to calculate their location directly on the street [303]. These locations exhibit a directional bias and are significandy different than the actual locations of these access points [302]. rirors are introduced into a positioning system when its reference point locations ar inaccurate. So in this positioning system, the accuracy of the access point locations play a large role in the accuracy of the end user positioning accuracy. Using the Chinese Postman model (figure 4] the scning vehicles detect a particular access point from as many sides as possible of the building housing the access point This additional data greatly improves the results of the reverse triangulation formula used to calculate the location of the access points [403]. More details on the access point location quality is described in connection with FigOur 11. [0045] The scanning data collected from this system represents a reliable proxy for the signal propagation patten for each access point in its specific environment Every radio device and associated swrounding environment produces a uniqe signal igepit showing how far the signal reaches and how strong the sn is n various locations within the signal fingerprint This fingerprint data is used in conjunction with the calculated access point location to drive high accuracy for the positioning system. This fingerprint is also )own as a "power profile" since the signal strengths at each position is measured as signal power in watts. The positioning system can intnrprt the fingerprint data to indicate that a particular signal strength of an 802.11 access point radio is associated with a particular distance from that access point Signal fingerprinting techniques are used in indoor Wi-Fi positioning but have proved 16 difficult to replicate in the wider area outdoor environments because the difficulty associated with collecting the fingerprint data. When the fingerprints or power profiles of multiple access points are overlayed, the positioning system can deterrnin a device location merely by finding the one position where the observed signal strengths match the combined fingerprints. Preferred embodiments of this invention provide a reliable system for obtaining this fingerprint data across a massive coverage area with million of access points in order to utilize fingerprint-based positioning algorithms. Reference Symmetry [0046] Positioning systems typically work by having tnee or more reference points around the device being tracked. These positioning systems use the radio signals from these reference points in various ways to calculate the device's current location. Significant errors occur when there are an insuffcient number of reference points or when the reference points lack balance or symmetry around the user. As illustrated in figure 5, the arterial bias that emerges from the random model introduces many scenarios where the end user [501] moves into physical areas in which there are only recorded access point locations [502] on one side of them. This lack of symmetry in the distribution of reference points around the end user causes the positioning algorhams to calculate the device location (503] with a great deal of error. With Chinese Postman model of scanning for access points, the user typically encounters a physical location [figure 6] in which there are numerous access point locations [602] on all sides of the user (601] within the range [604] of the device's 802.11 radio, The resulting position calculation [603] has reduced location bias and is more accurate as a result Figure 11 is another example showing the impact of quality location calculations. Scanning Device [0047] Figure 7 depicts the details of a preferred embodiment of a scanning device 702 used to detect and identify the various Wi-Fi access points. A scanning vehicle [701] contains a scanning device [702] that continuously scans the airways for radio signals from GPS satellites (708] and 802.11 access points [707]. The scanning 17 device runs the scanning client software [7041 that controls the entire process. The scanning client activates both the GPS receiver [705J and the 802.11 radio [706]. The GPS receiver is set into a continuous reception mode calculating the geographic location of the device every second. That calculation is read by the scanning client and stored in the local data storage [703]. The scanning client initiates the 802.11 radio and begins sending out 802.11 probe requests using directional antennas [709]. Any 802.11 access point [707) within range of that probe request responds with a signal beacon as per the 802.11 protocol. The responding signal beacons contains the network name of the access point (known as an SSID), the MAC address of the access point device as well as other meta information about the access point The responding signals reach each of the directional antennas with a different strength of signal based on the vector of origin and the proximity of the access point. That vector is recorded along with the identifier of that particular antenna and the meta information about the access point This probe-teceive-record process occurs continuously every tenth of a second. The scanning device deployed is a combination of the iPAQ 4155 Pocket PC and Powered GPS PDA Mount Cradle with integrated SMiR type GPS receiver with XTrac v. 2.0 firmware. [0048] The Scanning Client 704 of certain embodiments is described in connection with Hgure 10. The client consist of three main components, the Data Manager (1001], the System Manager (10021 and the Upload Manager [1003]. The Data Manager [1001] controls the operations of both the GPS radio (1006] and the 802.11 radio [1007]. The Data Manager controls when and how often these radios scan for signals and process those signals. The GPS radio once activated receives signals ftom GPS satellites [1004] and calculates its geographic location. The GPS recorder [10081 logs all of those readings every second and sends them to the File Manager [1010]. The Wi-Fl Recorder [1009] activates the 802.11 Radio to scan every tenth of a second, and associates those 802.11 readings with the GPS readings coming from the GPS radio and sends the resulting data to the File Manager. The File Manager receives scan data from both the OPS Recorder and Wi-FI Recorder and creates storage files on the device. This process continues the entire time the device is operational and both radios are functioning 18 [0049] In the Upload Manager [1003] there is a Hotspot Detector (10171 that monitors the 802.11 scanning results to look for the configured network of public hotshots [1024] (e.g, T-mobile) that the device is authorized to access. Once it detects a valid Hotspot it notifies the user of its presence. The user can select to connect to the hotspot by activating the Create Connection component [1018]. This component associates with the hotspot's access point and creates an 802.11 connection. Then the Hotspot Authetication module [1019] supplies valid authentication information for the device, The hotspot validates the account and then provides network access to the device. The Upload Manager then initiates the Upload Server Authentication process [1020] to connect to the Central Network Server [1025] and provides valid authenticaion information. Once authenticated, the Upload & Data Veiclation module [1021] is initiated. This module retrieves the scan data fom the Scanning Data store [1011] and uploads the data to the Central Network Server using FI?. The CentralNetwork Server initiate a process to store all the data in the Central Access Point Database. After the upload is complete the upload process moves the scan data from the Scanning Data store [1011] to the Backup Data store [1012] on th device. Once the upload is completed and vedfied, the New Version module [1022] checks the Central Network Server to determine if there is a new version of the client software available for the device. If there Is a new version, the software is downloaded and the Now Version Installatin [1023] process begins to upgrade the client software. Once the installation process Is completed the connection with the Central Network Server is terminated, the connection with the hotspot is terminated and the device returns to normal scanning operation. [0050] Incded in the Scanning Client 704 are a set of utilities that help to manage the device and reduce system errors. The Radio Manager [10131 monitors the operation of the GPS Radio and the Wi-Fl Radio to make sure they are functioning properly. If the Radio Manager encounters a problem with one of the radios, it will restart the radio. The User Interface Controller (1014] presents the tools and updates to the user so they can operate the device effectively. The Error Handling and Logging [1015] records all system issues to the device and alerts the user so they can address. Ie System Restart module [1016] is called when issues cannot be resolved. 19 'Ibs module sbuts down the device and restarts the hardwam, operating system and scanning client to ensure proper operation. [0051] The 1/10 of a second 802.11 scanning interval was chosen since it provides the optimal scanning period for 802.11 under these conditions using off the shelf hardware. 802.11bIg/n operates using 14 channels of the unlicensed spectrum. An individual access point broadcasts its signal beacon over one of those channels at any given time. The scanning device needs to survey each channel in order to observe as many access points as possible. The scanning interval is corlated with the average speed of the scanning vehicle to optimize how the scanning client covers the frequency real estate of a particular region. Central Network Server [00521 With reference to figure 8, the fleet of vehicles perfona their scanning routines while driving their pre-designed routes. Periodically each vehicle [ 801] will connect to an available 802.11 access point and authenticate with the Data Communications Module [807] of the Central Network Server. Typically the access points used for communicating with the Central Network Server are public botspots like those operated by T-Mobile ensuring reliable and metered access, The provisioning of this connection could be done via any available public access point. The scanning vehicle stops at a nearby hotspot location and begins the process of connecting to the access point. Once authenticated, the scanning client [704] identifies all the recently collected scan data from the local storage [703] and uploads that data to the Central Network Databasb [802]. [0053] Once the data has been uploaded to the database, the Parser and Filter process [803] begins. The Parser and Filter process reads all of the upload scanning data and loads it up into the appropriate tables of the database. During this exercise the data is evaluated for quality issues. In some cases the GPS receiver may record erroneous or error records for some period of time, which could negatively affect the final access point location calculation. The parser and filter process identifies these bad records and either conects them or removes them from the system. The filtering process users clustering techniques to weed out error prone GPS readings. For exarnple, if 90% of the readings are within 200 meters of each other but the remaining 20 10% of the readings are 5 kilometers away then those outiers ar removed by the filter and stored in a corrupted table of the database for further analysis. In particular, the system first calculates the weighted centroid for the access point using all reported data. R then determines the standard deviation based on the distribution of the reported locations. The systemuses a definable threshold based on the sigma of this distribution to filter out access points that are in error. Once these error records are marked, the centraid is recalculated with the remaining location records to detenrine the final centrold using the Reverse Triangulation method described below. [0054] Note that the error records may be the result of an access point that has moed. In this instance, the centroid for the access points will quickly "snap" to the new location based on the preponderance of records. An additional enhancement to the algorithm would include a weighting value based on the age of the records such that new records represent a more significant indication of the present location for a given access point. [0055] Once the parsing process has been completed the central network system initiate the Reverse Tiangulation model [804] begins processing the new data. During this process 1) new access points are added to the database and their physical location is calculated and 2) existing access points are repositioned based on any new data recorded by the scanners. The reverse triangulation algorithm factors in the number of records and their associated signal strengths to weight stronger signal readings more than weaker signals with a quasi weighted average model. [0056] During data gathering, a WPS user Is equipped with a Wi-F receiver device which measures Received Signal Strength (RSS) from all the available Wi-F access points, and then extracts location information of corresponding access points. RSS value of access points are shown as follows: (RSS, RSS, ... RSSIJ [0057] If the corresponding recorded GPS location of access point i is denoted by (Lab Longr}, and the calculated access point location is denoted by (Las Longg), the triangulated position is found by applying the algorithm as follows: 21 10= 0 Long,= [0058] The quad root of power is selected to ease the implementation of the algorithm, since quad root is synonymous to taking two square roots. [0059] The second point is referring to adjusting the dynamic range of coefficients. If the dynamic range of coefficients is a conceM, the coefficient of the algorithm can be divided by a constant nniber, e.g., " 0 # 10g Lat = M " " 10/i o C V 410m"d 1o C C [0060] The Parameter C can be any number and it does not impact the results, theoretically. Since, the weighted average is based on the ratio of the coefficients and not the absolute value, theoretically, dividing a11 the coefficients by a constant value, C, does not impact the results, but it changes the dynamic range of the coefficient values. [0061] This Bnal [Lat, Longdl Is then used as the final centroid value for the location of that access point. The latitude and longitude will the be stored in the database including a timestamp to indicate the freshness of the triangulation calcnlation 22 C-\NRPonbl\DCC\DER\319%93_l.DOC-7/101211 - 23 [00621 After the Central Network Database has been updated and each access point has been repositioned, the Data Pack Builder [805] creates subsets of the database based on regions of the country or world. The pack builder facilitates distribution of the database for a variety of use cases in which only region certain geographies are of interest. The pack 5 builder is configured with region coordinates representing countries, time zones and metropolitan areas. Utilizing this technique a user can download just the location data for the west coast of the United States. The pack builder segments the data records and then compresses them. 100631 The Fleet Management Module [806] helps operations personnel manage 10 the scanning vehicles and ensure they are adhering the routing procedures. This module processes all the scan data and builds the location track for each vehicle in the system. The operations manager can create maps of the vehicle track using the Map Builder [808] to visually inspect the coverage for a particular region. The GPS tracking data from each device is reviewed with route mapping software to verify completion of coverage and to 15 identify missed areas. This ability to audit and verify uniform coverage ensures that the system is getting the best data possible. The module also calculates the driving time of the vehicle to determine average speed and to subtract any idle time. These outputs are used to monitor efficiency of the overall system and in planning of future coverage. 100641 It will be appreciated that the scope of the present invention is not limited to 20 the above described embodiments, but rather is defined by the appended claims; and that these claims will encompass modifications of and improvements to what has been described. 100651 The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an 25 acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates. 100661 Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and 30 "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

Claims (32)

1. A system comprising: computer-implemented logic to derive position information for each of a plurality of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi access point, and location data characterizing a location at which the one or more characteristics of the signal received from the Wi-Fi access point was observed; and computer-implemented logic to store the derived position information in a non-transitory computer-readable medium.

2. A computer-implemented method comprising: deriving position information for each of a plurality of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi access point, and location data characterizing a location at which the one or more characteristics of the signal received from the Wi-Fi access point was observed; and storing the derived position information in a non-transitory computer-readable medium.

3. Software, embodied in a non-transitory computer-readable medium, comprising instructions executable by a processor to: derive position information for each of a plurality of Wi-Fi access points in a geographic area having a radius on the order of miles, wherein the position information is derived at least in part from sets of data collected during a comprehensive traversal of the geographic area, each set of data including signal data characterizing one or more observed characteristics of a signal received from the Wi-Fi access point, and location data H WcfJnenvovnW4RPonbr\DCCODER\4978226_I de-19104/2013 -25 characterizing a location at which the one or more characteristics of the signal received from the Wi-Fi access point was observed; and store the derived position information in a non-transitory computer-readable medium.

4. The system of claim 1, method of claim 2 or software of claim 3, wherein the one or more observed characteristics include one or more radio characteristics of the signal received from the Wi-Fi access point.

5. The system of claim 1, method of claim 2 or software of claim 3, wherein the geographical area has a radius on the order of at least tens of miles.

6. The system of claim 1, wherein the computer-implemented logic to store the derived position information includes logic to store the derived position information for a particular Wi-Fi access point in association with data representative of an identifier of that Wi-Fi access point.

7. The system of claim 1, method of claim 2 or software of claim 3, wherein the comprehensive traversal of the geographic area comprises an exhaustive traversal of accessible thoroughfares of the geographic area.

8. The system of claim 1, method of claim 2 or software of claim 3, wherein the comprehensive traversal of the geographic area comprises a systematic traversal of accessible thoroughfares of the geographic area.

9. The system of claim 1, wherein the computer-implemented logic to derive the position information includes logic to derive position information from a combination of newly-collected sets of data and previously-collected sets of data.

10. The system of claim 1, further comprising: computer-implemented logic to identify sets of data with potential error. H:\de nteron\NRPonbiDCOER\4978226_ldoc-19-I2I3 -26

11. The system of claim 10, wherein the computer-implemented logic to derive the position information includes logic to exclude the identified sets of data with potential error from the sets of data from which the position information is derived.

12. The system of claim 10, further comprising: computer-implemented logic to identify a portion of the geographic area in which the identified sets of data with potential error was collected.

13. The system of claim 1, further comprising computer-implemented logic to: identify, as a group, sets of data that share a common identifier of a Wi-Fi access point; and perform clustering analysis of the sets of data of the identified group, and based on results of the analysis, designate one or more sets of data of the identified group as sets of data with potential error.

14. The system of claim 13, wherein the logic to perform clustering analysis includes logic to: determine a first centroid for the Wi-Fi access point based at least in part on the sets of data of the identified group; and designate a first set of data as a set of data with potential error if a comparison of a distance between the first centroid and the location data of the first set of data exceeds a threshold.

15. The system of claim 13, wherein the logic to perform clustering analysis includes logic to: determine a weighted centroid for the Wi-Fi access point based at least in part on the age of the location data of the sets of data of the identified group; and designate a first set of data as a set of data with potential error if a comparison of a distance between the weighted centroid and the location data of the first set of data exceeds a threshold. H:\de mnoven\NR P \DCCDER\497B226_1.doc.-I 42l3 - 27

16. The system of claim 1, further comprising: a collection of information in the non-transitory computer-readable medium, the collection of information describing the plurality of Wi-Fi access points in the geographic area.

17. The system of claim 16, wherein the plurality of Wi-Fi access points in the geographic area includes substantially all of the Wi-Fi access points in the geographic area.

18. The system of claim 1 or method of claim 2, wherein the sets of data that share a common identifier of a Wi-Fi access point provide reference symmetry when the position information for the Wi-Fi access point is derived.

19. The system of claim 1 or method of claim 2, wherein the sets of data that share a common identifier of a Wi-Fi access point reduce arterial bias when the position information for the Wi-Fi access point is derived.

20. The system of claim 1, further comprising: computer-implemented logic to identify at least one portion of the geographic area in which no position information for a Wi-Fi access point is derived.

21. The method of claim 2, wherein storing the derived position information includes storing the derived position information for a particular Wi-Fi access point in association with data representative of an identifier of that Wi-Fi access point.

22. The method of claim 2, wherein deriving the position information includes deriving position information from a combination of newly-collected sets of data and previously-collected sets of data.

23. The method of claim 2, further comprising: identifying sets of data with potential error. H :derlnterwvn\NRfonb\DCC\DERM975226_ Idoc-19/D4/2013 -28

24. The method of claim 2, wherein deriving the position information includes excluding the identified sets of data with potential error from the sets of data from which the position information is derived.

25. The method of claim 23, further comprising: identifying a portion of the geographic area in which the identified sets of data with potential error was collected.

26. The method of claim 2, further comprising: identifying, as a group, sets of data that share a common identifier of a Wi-Fi access point; and performing clustering analysis of the sets of data of the identified group, and based on results of the analysis, designating one or more sets of data of the identified group as sets of data with potential error.

27. The method of claim 26, wherein performing clustering analysis includes: determining a first centroid for the Wi-Fi access point based at least in part on the sets of data of the identified group; and designating a first set of data as a set of data with potential error if a comparison of a distance between the first centroid and the location data of the first set of data exceeds a threshold.

28. The method of claim 26, wherein performing clustering analysis includes: determining a weighted centroid for the Wi-Fi access point based at least in part on the age of the location data of the sets of data of the identified group; and designating a first set of data as a set of data with potential error if a comparison of a distance between the weighted centroid and the location data of the first set of data exceeds a threshold.

29. The method of claim 2, further comprising: identifying at least one portion of the geographic area in which no position H dces ntnwon rblOCC\DER\4978226_1.doc-19/i4/2013 - 29 information for a Wi-Fi access point is derived.

30. Software of claim 3, wherein the instructions executable by the data processor to derive the position information include instructions to derive position information from a combination of newly-collected sets of data and previously-collected sets of data.

31. Software of claim 3, wherein the instructions executable by the data processor to derive the position information include instructions to excludes sets of data with potential error from the sets of data from which the position information is derived.

32. A system; a computer-implemented method; or software to derive position information substantially as hereinbefore described with reference to the accompanying drawings.