CA2628121A1 - Methods and systems for wireless network survey, location and management - Google Patents

Abstract

Existing techniques for wireless client station location and wireless network engineering rely on expensive dedicated a-priori surveys. These surveys require specialized equipment and the value of the surveys fades in time. We disclose methods and systems which eliminate the need for a-priori surveys, allowing the collection of necessary data from ordinary Wireless Client Stations in the course of their normal use. Advantages include fast and inexpensive deployment in green fields, with continuous update and improvement of location accuracy. In addition, the collected data and systems provide corollary information that can assist in real time wireless network management, maintenance and engineering.

Description

BACKGROUND -With the proliferation of cheap, standards-based wireless devices, the popularity of wireless networks has experienced an explosion in growth in the recent years. These devices provide untethered access to the Internet and this convenience has helped with the spread of the technology and helped create a critical mass of consumers.

The mobility of these devices spurs a new demand for ubiquitous wireless availability and for location based services. Ubiquitous wireless availability requires the deployment of networks where the radio environment is less than perfectly controlled, for example public hot spots, campus and urban deployments. Location services depend on locating the device wherever it may be. These services include emergency services, parents that wish to keep track of their children, personnel and asset tracking services and localized advertizing, to name a few.

The most common deployed topology for end-user wireless communication consists of Wireless Client Stations (e.g. WiFi, WIMAX or cell phone user devices) which seek connectivity to global networks (e.g. the Internet or the telephony network) through a collection of fixed base stations (e.g. access points or cell towers). Thus a wireless device can access all the resources of global networks including access to other wireless devices connected to the global network.

Place Lab has demonstrated the usefulness of using the very emitters used at the fixed base stations as beacons for locating the Wireless Client Stations. Based on the signal strength of beacons received by the wireless client station, Place Lab has demonstrated position accuracies on the order of 30m in urban areas with high densities of WiFi access points. In close quarters such as within an office environment accuracies of a few meters are possible. Since the Wireless Client Stations need awareness of the surrounding base stations signals to function, they already have the essential components to enable their geographical positioning. In addition to cost savings over using additional positioning hardware, these techniques work where GPS does not, for example indoors or in urban canyons.

Two basic approaches have been developed for computing position using scan data, that is, the measure of detected beacons and their signal strengths. Skyhook computes a location for the wireless client using a database of pre-calculated base station positions.
Alizadeh-Shabdiz , et al.
(US patent 7305,245 "Location-based services that choose location algorithms based on number of detected access points within range of user device") of Skyhook explains how a variety of methods can be used to estimate the location of a wireless station based on the known position of access points it sees in a scan. Ekahau, on the other hand, uses fingerprinting techniques where the pattern of base stations seen in a scan is matched to scan patterns at known locations recorded in a database. Myllymaki et aI. (US patent 7228136 "Location estimation in wireless telecommunication networks") of Ekahau explains how the position of a Wireless Client Station can be determined by matching its fingerprint to those collected at known geographical positions.
Others such as Dressier et al. of Polaris Wireless (US Patent 7,167,714 "Location determination using RF fingerprints") show how these methods can be extended with probabilistic approaches.

In all cases, however, the location calculation requires a-priori surveys to populate a database of base station positions or to train a fingerprint matching system. These surveys require specialized equipment with accurate GPS or other form of independent positioning (for example te Ekahau surveyor allows the user to plot position manually on a map). The accuracy of the survey also depends on using calibrated or at least normalized radio sensors so that the signal strengths reported to the database are consistent.

Morgan et al. (US Patent Application 20060106850 "Location Beacon Database") goes at great length in teaching the survey methods to build an unbiased, reliable and accurate data base of access point locations. Minkyong et al. ("Risks of using AP locations discovered through war driving", Dartmouth College, http://www.cs.dartmouth.edu/-minkyong/papers/minkyong-pervasive06-v20060503.pdf) clearly points to the issues of uncontrolled surveying as a basis for location calculation using a variety of methods. This explains why Skyhook has spent considerable sums surveying urban centers of North America and elsewhere.

In wireless networks, surveys are also required for planning network deployments, monitoring the network's performance, diagnosing problem areas and engineering changes through the networks life cycle. Thus there is some overlap between the need of surveys for engineering purposes and as support for location based services.

Typically surveys are executed from motorized vehicles (in some cases bicycles and rarely by foot) and are therefore limited by access and time to public streets and paths.
Unfortunately these are precisely the areas where Wireless Client Stations are unlikely to be used;
more likely locations being, the offices, coffee shops, hotel rooms and homes where people work and play. Inherently, therefore, a-priori surveys for base station location and fingerprint training are biased to where mobile devices are unlikely to be used.

An additional problem is that even fixed base stations are not permanent. Some will be disabled, reconfigured or moved while new ones will be added. From our own measurements we have determined that the churn in WiFi access points in some urban environments is on the order of one year. Therefore, a-priori surveys can rapidly lose their value.

In summary existing techniques for wireless network engineering and wireless client station location rely on expensive dedicated a-priori surveys. These surveys require specialized equipment, the value of the surveys fades in time and the survey usually cannot cover the actual locations of network usage. In the following pages we will disclose methods and systems which eliminate the need for a-priori surveys, allowing the collection of necessary data from ordinary Wireless Client Stations in the course of their normal use. Advantages include: fast and inexpensive deployment; continuous update of network engineering and positioning data; better customer service; and improved of location services.

Location based services are also available for wired telecommunications like the Internet. These might be based on trace route analysis which identifies the address of each router and times each hop as a packet traverses the network form source to destination. Using the fact that some well known routers serve specific geographical regions, that some IP addresses are allocated regionally and that the speed of light constrains the transmission speed on each hop, it is possible to get a general indication of the source location for an IP address. This is used by Google for example for providing targeted advertizing or even tweak searches to provide results that may be more relevant to users based on their location, for example a specific state or a urban center. Such wired network location services typically cannot provide the precise positioning information available from wireless network location services.

SUMMARY OF INVENTION

The invention encompasses multiple components:

= Methods and systems for collecting survey data from Wireless Network Stations without GPS or other explicit location data;

= Methods and systems for normalizing survey data from uncalibrated sensors;

= Methods and systems for combining wireless network based position to improve IP based locations services; and = Methods and systems for monitoring and managing wireless connectivity services to autonomous Client Stations.

All of these components are closely linked as will be explained in the context of our preferred embodiments.

Traditional wireless surveys use dedicated survey equipments including positioning device(s) and accurate radio receiver(s) that can measure the absolute signal strength from Wireless Base Stations. These may be assembled from off-the-shelf components, for example a laptop computer, a GPS receiver, a commercial Wireless Interface that may have been calibrated, and software such as Kismet or Netstumbler to collect and collate information. However, the common end-user Wireless Client Station is not so equipped.

Wireless Client Stations are rarely equipped with GPS o have their positions otherwise known, however, it can be expected that the fixed location of at least a few Base Stations can be known exactly and a-priori. Some service provider grade Base Stations include GPS
receivers so their location can be obtained directly. In other cases one would know the position from the Based Stations because of their intended purpose (e.g. coffee shop hotspots) or network engineering specifications.

We will teach a methodology and system that allows surveying from and location of Wireless Stations not equipped with GPS. Instead we show how the relative proximity among Base Stations or Client Stations can be determined from scan data collected by these stations; how to build a Wireless Network Topology from scan data; how to build a Wireless Network Map from the topology an given a few reference Stations with known position; and how to determine use the Wireless Network Map to determine the position of Wireless Stations.

Our survey data consists of so called scan data which is implicitly be collected by Wireless Client Stations. Scan data identifies at a minimum surrounding Wireless Base Stations available for connection and typically a Radio Signal Strength Indication (RSSI) for each one, to enable the Client Station to select among available Base Stations the one with the best signal to connect to. For this purpose, RSSI only needs to be a relative measure of signal strength (i.e. be a monotonically increasing function of radio signal strength). Competitive commercial pressure dictates that for common end-user Wireless Interfaces, no special design or manufacturing effort is placed on making them more than just so.

Building a Wireless Network Topology does not absolutely require that the scans inciude an RSSI, but RSSI is nevertheless useful and can improve the accuracy of Wireless Network Maps, locations services and other analysis. It will further be evident to those familiar in the art that a measure of Absolute Radio Signal Strength can be even more advantageous.

To this effect we will further teach how to derive Absolute Radio Signal Strength from RSSI in any scan data. Basically this methodology and system uses absolute radio signal measurements from Base Stations to ground truth and calibrate the RSSI observed by Client Stations. This takes advantage of provider grade Base Stations, which are more, powerful, sophisticated and expensive than consumer grade products, and typically provide reliable absolute radio signal measurements.
Many embodiments of the previous methods and systems will required the exchange of packets over the Internet. We teach a further set of methods and systems whereby Internet addresses are correlated to the location data determined from a wireless network to improve the accuracy and detail IP based location based services.

The previously discussed components of the invention will typically require the installation of additional functionality on end-user Wireless Client Station devices. This additional functionality would operate silently and invisibly to the end user apart from enabling Location Based Services.
Respectful of privacy none of these components transact personal information or requires anyone to disclose their location.

This leads us to a fourth component of the invention that builds on this silent functionality to facilitate the management and delivery of Wireless Connectivity Services. We the address the problem of offering public connectivity service in difficult and variable environments, without what is commonly known as Customer Premise Equipment (CPE). In telecommunication the CPE is a known entity through which the Service Provider can monitor, diagnose and configure the service delivered directly to the end customer. In the case of Wireless Network Providers and specifically for WiFi services there is often no CPE as the Client Station may be a laptop or other computing device entirely controlled by the end-user. In these circumstances there is no way for the Service Provider to exactly monitor, diagnose and manage service problems experienced by his subscribers. We teach methods and systems to provide virtual CPE capability on end-user platforms, consisting of a program that performs the function of a CPE.

SUMMARY OF FIGURES

Figure 1 Definition of Wireless Network Nodes.

Figure 2 Illustration of Normalized Scan Signature and Base Station Signature assembly.

Figure 3 Graph of proximity among Scan Vectors, among Base Stations, between Scan Vectors and Base Stations, and combined Network Proximity Topology graph for the Network Nodes illustrated in Figure 1.

Figure 4 Second order proximity defined as graph path length.

Figure 5 Location determination by path length weighed centroid method.

Figure 6 Local application of the weighed centroid method forming simultaneous equations.
Figure 7 Force balance on a node.

Figure 8 Illustrative example of how RSSI reported by different Wireless Client Interfaces relate to received radio signal strength.

Figure 9 Illustrative example of two Reference Base Stations, a multiplicity of Other Base Stations and of one Wireless Client Station moving about these Base Stations.

Figure 10 System RSSI calibration with associated CS and RBS.
Figure 11 System RSSI calibration with active probing from the CS.
Figure 12 Logic for correlating an correcting SS for the RSSI calibration.

Figure 13 System for enhanced wired network location combined with wireless network survey system.

Figure 14 System for enhanced wired network location separate from a wireless network survey system.

DETAILED DESCRIPTION OF THE INVENTION

A SURVEYING WITHOUT THE BENEFIT OF GPS
A.1 DETERMINING RELATIVE NODE PROXIMITY

A first aspect of the invention consists of determining the relative proximity of Wireless Network Nodes. This aspect alone may find many applications, for example: location based games where proximity and not absolute position plays an important role; an emergency locator using a virtual range finder giving the user a indication, faster beeping for example, as he approaches a target node; smart advertisement systems that could sense the proximity of user devices and address to them specific locale based information. A key feature is that none of the devices collaborating to provide this function need to know or disclose a location in order to develop a useful topological map of the network.

There are three sub-components within the proximity determination:
1) Collecting scan information from Wireless Network Nodes;

2) Determining the relative proximity of nodes that are within radio reach of each other;

3) Inferring the second order proximity of nodes that are not within radio reach of each other, but that can be connected through a series of other nodes that do see each other.

Figure 1 illustrates the various types of Wireless Network Nodes. Two basic types of nodes are Base Stations (100) or Client Stations (200). Base Stations (100) are assumed fixed but should they be mobile this fact can be detected by a method of the invention, and appropriate steps taken to correct the situation. Client Stations (200) can be mobile (200.1) or not (200.0). A Scanning Nodes (bold in Figure 1), can be either a Base Station (100) or a Client Station (200) and is distinguished by its capability to collect information about neighbouring Base Stations (100) by scanning local radio airwaves from time to time. For each detected Base Station (100) a unique identifier (typically a MAC address) and optionally other information such as an indication of radio signal strength is recorded; this defines a Scan Vector. Scan Vector, or more exactly Scan Vector collection locations (300) are shown in Figure 1. Since Scan Vector can be collected by mobile stations, each one can be uniquely identified by the identifier of the Scanning Node (typically a MAC
address) and a time of scan collection.

By force, the Base Stations detected in a Scan Vector are known to be proximate to the collection point (300), and those that have stronger indication of radio signal strength are likely to be closer than others. A corollary is that any two Scan Vector that are similar, that is they comprise the same detected Base Stations (100), are known to have been collected in proximity to each other.
Also, Base Stations (100) detected within the same Scan Vector are known to be proximate to each other.

Thus from time to time each Scanning Node collects from the local airwaves a Scan Vector:
SUN = [ SSINb. SSINh === J;

where SSIn,;, SSIn;i and so on are a signal Strength Indication for Base Stations BS;, BS and so on, as seen in Scan Vector SVN.

Without limitations, the Signal Strength Indicator, SSI, may an actual Radio Signal Strength measured in dBm or as an energy flux, or it might be a relative un-calibrated monotonic function of Radio Signal Strength, or a ranking (denoting the node with the lowest signal, the second lowest signal, and so on to the highest), or in its simplest from it is simply 0 or 1, zero indicating that a station is not detected and 1 indicating that it is. It will be appreciated that the Scan Vector does not list the stations it does not detect, and in the later case storing SSI
explicitly is not necessary: if a station ID is listed, it's SSI is 1, and if not it is 0.

For the purpose of fingerprinting, that is matching the Scan Vectors that may have been collected from different Scanning Nodes at different times, it is advantageous to derive a Normalized SSI, NSSI, such that within the Scan Vector all values range from 0 to 1(or some other fixed maximum).
This renders all Scan Vectors comparabie to some degree. Several methods of scaling and normalization may be used for example simple linear scaling, fitting a Poisson distribution or just applying a rank. As illustrated in Figure 2, a Scan with SSis of [-84, -45, -63, -75] as might be obtained from a 802.11 Wireless Interface could be normalized to [0.25, 1.0, 0.46, 0.34] with:

NSSI 1Ji 1 (SSIN,~ - maxJ(SSIN)) N'` + ax(jSSI-miS1N)) where the indices N, i denotes the ith Base Station detected within N`h Scan Vector I is the number of detected Base Stations in the Nth Scan Vector; and max(SS1N) and min(SSIN) are the maximum and minimum SSI in the Nth Scan Vector.

In this example, scaling by 1/(I+1) ensures that the smallest SSI does not have a value of 0, which is implicitly reserved for undetected nodes.

Thus each Scan Vector can be transformed into a Normalized Scan Signature:
NSSN = [ NSStn,,~ , NSSIn ;, ... J;

where NSStnr,;, NSSIn,f and so on, are the Normalized Signal Strength Indicators for Base Station node BS;, BS=and so on, as seen in scan vector SVN.

Two Scan Vectors with a very similar scan signature, NSSN and NSSM, are likely to have been collected close to each other. Also, the location where the Scan Vector, SVN, was collected will be close to where Base Stations node BS; , BSj and so on, are located, and likely closer to those that have a larger NSSI. If the location of at least some of the Base Station nodes BS;, BSj and so on, are known, then the position where the Scan Vector was observed can be estimated by various means. For example, the following expresses the weighed centroid method of position estimation in 2D:

f~ Ei NSSIN.I XBSi s~," = Ei NSSIN,i = Ei NSSIN,i I'BS, ~SV" Ei NSSINj where XSV" and YSV" are the estimated X and Y coordinates of the Scan Vector SVN, and XBS, and YBS, are the known X and Y coordinates of Base Station BSi.

The accuracy of such position estimates depends partly on the spatial density of Base Stations and is typically 3m indoors and 15 m outdoors. Here and later the person familiar in the art will appreciate that other forms of weighing and methods of position estimates may be used, and that the formulation is trivially extended to 3 dimensions.

As illustrated in Figure 2, we can also derive Base Station Signatures from a collection of NSS!
BSS, = f NSSIn,1, , NSSIm 1, ... J;

where NSSI,,, NSSIml and so on, are the Normalized Strength Indication for Base Station BSI. as seen in scan vector SVn, SVm and so on.

Two Base Stations BS1 and BSj with similar signatures will be close to each other and are expected to be locatable with comparable accuracy as Scan Vectors. Also, the Base Station X will be close to where the various Scan Vectors, SVn, SVm and so on, were collected, and likely closer to those that have a larger signal, NSSI. If the location of at least some of the Scan Vector, SVn, SVõ, and so on, are known, then the position of the Base Station can be estimated by various means. For example, the following expresses the weighed centroid method of position estimation in 2D:

Ei NSSIN,i Xes;
Xsv" Ei NSSINi Yi NSSIN,i ~'es, Ysv" _ 2:NSSI
i N,i where 9sv" and Ysv" are the estimated X and Y coordinates of the Scan Vector SVN, and XBS, and YBsi are the known X and Y coordinates of Base Station BSi.

Note that the indices N, M and so on, identify Scan Vectors and not Scanning Nodes since a-priori the Scanning Nodes can be mobile so any two Scan Vectors (300) collects by the same node could be completely unrelated.

Various methods are admitted by the invention for quantifying the similarity among Scan Signatures NSS or BSS. Some use probabilistic and maximum likelyhood methods, however, our preferred embodiment defines a metric based on a normalized squared distance between two Scan Vectors can be determined by their signatures NSSN, NSSm as:

SSD = Zz(NSSIN NSSIM,i Ei NSSIN,i NSSIM,i where NSSIN; and NSS&,;are from the signatures NSSNand NSSMfor station BS;, and the signal NSSIx; from each Base Station BS;, is taken as a dimension in an abstract space.

It is readily verified that as long as NSSI are all positive, SSD2NM = 0 implies an exact match of NSSN and NSSM , while SSD2Nm = 1 results when none of the same bases stations are present in NSSNand NSSM An SSD2NMapproaching 1 is indicative of a separation distance on the order of a cell diameter (coverage area of a Base Station). Figure 3A illustrates how the SSD2,,j can define a graph relationship among Scan Vectors from the network of Figure 1.

Similarly the squared distance between Base Station can be determines by their Signatures as:
E'(NSSIn NSSInI
BSD -~,i NSSln1 NSSInj where NSSI,,,i and NSSljare the NSSI from the signatures BSS~and BSSI , and the signal NSSI,,,= from each Scan Vector NSS,, , is taken as a dimension in an abstract space.

BSD211 = 0 indicates a perfect match between the Base Station BSSI and BSSI, perhaps because they are collocated, while BSD21j = 1 means they are out of range of each other with no single scan containing both Base Stations BSSi and BSS1 and is indicative of a separation distance on the order of a cell diameter. Figure 3B illustrates how the BSD2,1 can define a graph relationship among Base Stations from the network of Figure 1.

The set of SSD2nrnr and BSD21f relate pairs of Scan Vectors amongst themselves and pairs Base Stations amongst themselves. Furthermore, one can link Scan Vectors to Base Stations by attributing to each a proximity metric given by:

SVD = I NSSI 2 , N,1 ) where NSSIn;I is the Normalized Signal Strength Indicator for Scan Vector, SVN, observing Base Station BS,.

The factor of 0.5 account for the fact that a near 0 NSSInri (marginal detection) is indicative of a distance equal to the cell radius, rather than the cell diameter. As for SSD2 and BSD2, a value of 0 for SVD2 is indicative of close proximity. Figure 3C illustrates how the SVD2]j can define a graph relationship between Scan Vectors and Base Stations from the network of Figure 1.

SSD ,, BSD , and SVD2n;1 are comparable proximity metrics for Scan Vectors amongst themselves, for Base Stations amongst themselves and for Scan Vectors with Base Stations. These proximity metrics can be assigned as the attribute of edges defining graphs linking Scan Vectors to themselves, for Base Stations to themselves and Scan Vectors to Base Stations.
Thus, with no a priori knowledge of any Wireless Node position we have used a collection of Scan Vectors to build three graph topologies. As illustrated in Figure 3 D, these three graphs can further be combined into one to form one Wireless Proximity Topology graph. In this graph edges with values of one (1) are indicative of a separation roughly representative of a cell diameter while an edge value near zero (0) is indicative of collocation. In the Wireless Proximity Topology graph the value of an edge between nodes Kand L, is simply referred to as SD2xL, irrespective of the node type.

It will be appreciated by persons familiar with the art that any one, or any combinations of these graphs can be used to represent a Wireless Proximity Topology graph, and that other similar graphs can be derived from Scan Vectors.

In applications where relative proximity rather than absolute position is sufficient, the Wireless Proximity Topology can be used directly to obtain an indication eparation distance between any two nodes. As illustrated in Figure 4 the path length, ZK,LSD rough the graph is obtained by adding the proximity attribute of each edge as they are traversed. The shortest path between two nodes is indicative of the distance between these two nodes and is representative of how many cells diameters separate the two end points.

If a path does not exist between two nodes then either they are each part of two non contiguous wireless service areas, or there are too few Scan Vectors accumulated yet to characterize their proximity. In a green field deployment it will initially be impossible to determine proximity as no connectivity graph yet exists a-priori, however, if all or a large number of Client Station Nodes were to be made Scanning Nodes with the addition of the appropriate program, the graph would progressively be build up as Scan Vectors were accumulated. In general Wireless Networks are designed to service many more Client Stations than there are Base Stations, and one could expect complete graph coverage within days.

The description of the invention assumes all Base Stations have similar ranges or cell sizes, however, this might not be the case, as for example one might be using FM
radio stations for long range and WiFi for shorter range positioning. These can be accommodated by attributing further weights to the graph edges that reflect the relative range of the Base Stations used. Similarly, if the Scanning Nodes are calibrated to measure absolute radio signal strength, the above procedure can be adapted to use these rather than a Normalized Signal Strength Indicator.

A.2 DETERMINING THE POSITION OF NODES

A second aspect of the invention consists of using the Wireless Proximity Topology to infer the absolute location of nodes from the known location of a few nodes. Several techniques can be applied for this purpose, including but not limited to:

= Weighed centroid technique, where the weights are determined by the length of the shorted path to each of the known locations;

= Weighed centroid technique, applied to each node and its neighbours, thus forming a set of simultaneous linear equations; and = Formal optimization with cost function minimization.

The first is the simplest and may be sufficient in some applications or it might provide an appropriate starting point for the other solutions. As illustrated in Figure 5, it is a direct application of the weighed centroid method, using the inverse path length to nodes with known positions as weights:

El t ~K,l S
9K Et K,l SD

~-3 El IK,l SD2 rK El K,l SD

where XK and YK are the estimated X and Y coordinates for a graph node K,-Xt and Yt are the known X and Y coordinates of known graph nodes t, and ZK,t SD2notes the cumulative length of the shortest graph path between nodes Kand L
This path might be along SSD2, BSD2 and SVD2 edges or any combination of these.
The usual techniques can be used to gua ainst division by zero in this formula. The number of nodes I or the maximum range ZK,l SD~be used in the calculation may be speci ' o limit computations in this and other techniques. Also, various other functions of x, SDL~i ht be E
t g used for weighing.

Another method applies the centroid method locally determining the location of each node as a function of its neighbours as illustrated in Figure 6 and expressed as follows:

Et JCK, S
~'K=E t IISD
El YK,t SD
YK ZK,t D

where (X, ?)K are the estimated X and Y coordinates for a graph node K, (X, Y)K,l are known (X, Y)K,t or estimated (X, Y)K,l coordinates for graph node I directly linked to Kby an edge, and SD , denotes the edge length, whether SSD2, BSD2 or SVD2, linking node I
directly to node K.

Since the equation for estimating (X, ?)K includes other estimated coordinates (JC, Y)K,1 , the problem consists of a set of simultaneous equations that is linear in (X, Y).
A variety of well known solution methodologies exist to solve this problem.

On limitation of any technique based on the centroid method, is that the estimated position will always be within the largest polygon containing the known position nodes. This is despite the fact that information from the Wireless Proximity Topology may suggest otherwise.

This is alleviated in a third technique where the graph edges are modeled as a mesh of tension/compression element, the force along these elements being determined by the proximity metric, SD2, of the edge and the estimated distance between its two end points. As an example, the force along an edge linking nodes i and j nay be given by:

Fi j= x Dij - SD
where Kis a constant;

Djj =
i(Xi - Xj)+(Y - Y) the distance between nodes i and j; and SD 's the proximity metric for the edge between nodes i and j.

A negative force is indicative of compression and a positive force of tension.
The constant, K, defines an equivalence between distance and the proximity metric, SD2. the 2ace of other constraints an edge would naturally relax (F=0) to a length of DtJ = SWith the particular formulation we have used for SD2 earlier, setting K to the inverse of the expected cell diameter or to an empirically determining value is advantageous. Other formulation for F and SD2 are admitted within the scope of the invention, the most important factors is that smaller SD2 and larger distance lead to greater tension and that the two cancel at some equivalent value.

It is also advantageous to include additional compressive member between a node K and surrounding nodes that are not linked to it but are within a certain distance, as:

F~~ =~=KD~ ~ - ~ for D= ~ < , =and ~~ ~~

FI.1 = 0 for DL-,I = > .

This introduces a repulsive force among nodes that are not linked by an edge forcing them to spread out as would be expected because nodes that are not connected by an edge are also not close to each other. The total of all the forces acting on a node K are illustrated in Figure 7, and its equilibrium position for node K is:

JCYt 9K,1FK,tIDK,I + Gm XK,mFK,m/DK,m K =
2:1 FK,I/ DK,I + 2:m FK,m/ DK,m _El YK,I FK,I / DK,I + Em YK,m FK,m ! DK,m ~K Gl FK1I/ DK,I + Em FK,m/ DK,m where the summation El is over nodes linked to K by an edge, the summation Y,,, is over nodes not linked to K but within a distance of 1/K.

(X, Y)K,l are known (X, Y)K,l or estimated (X, Y)K,l coordinates for graph nodes surrounding K, DK,I denotes the distance between node Idirectly to node K.

Since the above equations for estimating (9, f')K includes other estimated coordinates (X, ?)K,t the problem consists of a set of simultaneous equations. In this instance, however, the equations are not linear in (X, Y).

Solution to the above force balance problem can be obtained by a variety of finite differences or finite element techniques which seek local minimization of force imbalances or global minimization of potential energy stored as tension and compression in the mesh.

The invention admits any technique by which a Network Proximity Topology is combined with the knowledge of the position of some nodes, to determine a plausible position for all the other nodes.
In addition to the above, such techniques include various probabilistic, optimization and simulation methods. It is also possible to add other constraints to the problem, for example, ruling out Base Station positions over water bodies or in the middle of thoroughfares, or applying probabilistic constraints on the location of Scan Vectors based on displacement models.

In 2 dimensions at least 3 non-collinear nodes of known position must be specified to unambiguously solve the problem. In 3 dimensions 4 non-coplanar nodes are needed. In practice the specification of many more position representing a small fraction of all nodes, say 10%, evenly distributed over the network domain will provide the best solution. The results can easily be plotted in geographical coordinates or local coordinates such as on building plans to provide a Wireless Network Map.

A.3 DETERMINING A SIGNAL ATTENUATION MAP

In another aspect of the invention this map is further analyzed to infer regions of signal attenuation. This can be achieved by examining the ratio between the actual length of edges in the Network Map and their corresponding proximity metric in the Network Topology. A small length to proximity metric ratio is indicative of greater signal attenuation.
In the case where the solution of a force balance equation is used in obtaining the Network Map, the force values along edges can be considered directly, compression being an indication of attenuation.

In its simplest form a map of relative attenuation can be obtained by plotting the above ratio or force at the center point of each edge and suitably interpolating techniques to produce a continuous attenuation field over the domain of the Wireless Network Map.

Should the proximity metric be quantifiably related to absolute radio signal strength, this signal can be compared to what ideal radio propagation would predict for the distance separating the nodes at either end of the edge. The radio propagation model might be for simple isotropic propagation, might include ground effect and might also include antenna patterns.
Differences between modeled and observed signal strength will provide an estimate of attenuation in db along the edge.

This attenuation along each edge of the graph represents an integral of the attenuation over that distance. This attenuation might be contributed uniformly over the edge length or by discrete obstacles separated by low attenuation. As illustrated in Figure 3 the complete Network Topology Graph is far from planar, that is multiple edges intersect each other. In principle therefore it is possible to determine attenuation on a shorter special scale than the edge lengths. The problem of finding a attenuation density distribution is an inverse problem not dissimilar from determining 2D or 3D medical images from scanning devices such as ultrasound, CAT scan.

Many factors can contribute to attenuation including time varying environmental factors (precipitation and vegetation), moving interferer such a vehicles, and antenna orientation of portable devices; however, with sufficient sampling these can be filtered out to obtain a map of fixed interfering bodies such as topography and building. Constraints might be placed on the attenuation solution for example ruling out attenuation over open bodies of water.

An Attenuation Map can in turn be used to help derive the Wireless Network Map in the previous aspect of the invention. Recall that in one embodiment the range of wireless nodes is parameterized as 1/x. In regions of attenuation this range and thus 1/x should decrease.
Therefore the Attenuation Map can be used to define a spatially variable value of ic and to calculate a more reliable Wireless Network Map. Combining these processes maps can be generated that optimize both the distribution of network nodes and of attenuation.

A.4 IDENTIFYING ANOMALOUS NODES

Yet another aspect of the invention is the examination of the Wireless Network Topology or Wireless Network Map to identify inconsistent nodes, for example Base Stations that were assumed fixed, which may be mobile, or have been moved. These will be recognized by graph edges whose range are beyond reason, for example WiFi Base Stations that appear to be detected at distances of more than 1 kilometer (i.e. edges in extreme tension), or nodes in the graph that appear to be linked to so many other nodes that it would imply an implausible density of wireless nodes. Such suspect nodes can readily be identified and removed from the graph and a new mapping solution derived. In the case where a Base Station has moved but otherwise is not mobile, it may be split into two virtual Base Stations with position depending on the time frame considered.

B SURVEYING WITHOUT CALIBRATED SENSORS

B.1 OVERVIEW

In the following we will teach a methodology to normalize data collected from commercial un-calibrated receivers such as the Wireless Interfaces embedded or installable on common end-user Wireless Client Station. This allows the direct comparison and analysis of data collected from multiple end-user platforms each of which may use a different and a-priori unknown scale of Radio Signal Strength Indication.

In the case of WiFi (wireless networks based on the 802.11 series of protocols) Radio Signal Strength Indication (RSSI) is often represented as negative number between -100 and 0 in which case it is also often assumed to have units of dBm or the standard unit of radio signal intensity.
However this assumption is erroneous and comparison of RSSI reported by different Wireless Interfaces as a function of radio signal strength will show large discrepancies, as illustrated in Figure 8. The largest differences will occur between different brands and models of Wireless Interfaces typically using different chipsets, but these devices are generally not calibrated at the time of manufacture so even items of the same make and model will differ.

A component of our invention consists of methods, mechanisms and systems for automatically deriving the functional mapping Absolute Radio Signal Strength = FX (RSSIX) ;

where: F. is the functional mapping for Wireless Interface X, and RSSIXis the RSSI value reported by Wireless Interface X.

This mapping then allows reliable and reproducible measurement of radio signal strength in a Wireless Network, using common Wireless Interfaces on end-user Wireless Client Stations.

This component of our invention requires that some Wireless Base Stations detectable by the Wireless Client Stations, be able to accurately measure received signal strength and that this measure be accessible for the purpose of the invention. These Wireless Base Stations will be referred to as Reference Base Stations (RBS); other Wireless Base Stations will be referred to as Other Base Stations (OBS). Wireless Clients Stations may be equivalently referred to as Clients Stations. In the WiFi (802.11 protocol) context, Base Stations would be known as Access Points or APs.

Figure 9 illustrates two Reference Base Stations (110), several Other Base Stations (100) and one Client Station (200).

Reference Base Stations are likely to be service provider devices, that is, Wireless Base Stations destined for the commercial offering of connectivity services, such as Hot Spots or Wireless LAN
(WLAN). These devices require a level of robustness and manageability far beyond end-user APs for residential or SOHO use. Service-provider-grade Wireless Base Stations are more expensive and normally capable of accurately measuring the radio signal strength of devices around them (Wireless Base Stations and Wireless Client Stations) in order to improve communication diagnostics and manageability. Service provider Wireless Base Stations are also typically accessible through SNMP or other mechanism so that information they gather can be accessed externally.
This method of the invention in its simplest form consists of:

1. Contemporaneously a. measuring the absolute signal strength, SS110-200 , with which a Reference Base Station(110) detects a Client Station (200), and b. recording the RSSI200-110 with which the Client Station (200) detects the Reference Base Station(110).

2. Building a correspondence table between the absolute signal strength (SS110-200) and RSStZO0-ii0from one or more instances of the above measurement.

3. Using the above correspondence table to recast any measurement of RSSI by the Client Station (200) into an absolute signal strength measurement for any Wireless Base Station (100 or 110).

Extensions of the method include correcting the signal strength (SS110-200) by the difference in radiative power between the Reference Base Station(110) and the Client Station (200) so that correspondence is established between RSSI and a Corrected Signal Strength (CSS110-200)=

Further extensions of the method include in step 3 using the correspondence table to estimate a continuous functional relationship between Signal Strength (SS110-200 or CSS110-200 ) and RSSlzoo-11o through fitting, regression and/or interpolation techniques, which themselves might include without being limited to:
1. choosing a method or parameters of the functional relationship depending on number of Signal Strength and RSSI pairs in the table;

2. assigning a weight to each pair of (SS110-200 or CSS110-200 ) and RSSlZ0o-zoo based on some factor such as the time difference between the Signal Strength and RSSI
measurements;
3. determining the expected reliability and accuracy of the functional relationship;
4. estimating the Signal Strength for a given RSSI from the functional relationship; and 5. estimating the accuracy of the Signal Strength estimate for a given RSSI .

Further extensions of the method include:

1. comparing the correspondence table or functional relationship between Signal Strength (SS110-200 or CSS110-200 ) and RSSi2oo-iio for different Client Stations (200) ;
2. determining their similarity based on some property of the Client Stations, for example the make and model of their Wireless Interface; and 3. deriving a correspondence table or functional relationship between Signal Strength (SS110-200 or CSS110-200) and RSS1200-110 for a class of Client Station (200) sharing that same property.

A further extension of the method includes removing redundant, unreliable, outlier or outdated pairs of Signal Strength (SS110-200 or CSSllo-2oo ) and RSSI200-110 for a Client Station (200).

Yet a further extension of the method consists of using a correspondence table or functional relationship between Signal Strength (SS110-200 or CSS110-200) and RSSlzoo-11o for a class of Client Station (200), either derived as above or obtained by other means, to recast any measurement of RSSI by a Client Station (200) into an absolute Signal Strength measurement for any Wireless Base Station (100 or 110) based on the membership to that class.

B.2 ASSOCIATED CLIENT STATION

We shall now describe the details of a system where the invention is put to practice for networks built upon the 802.11 (WiFi) standard. In this embodiment the Client Station is required to be Associated with a Wireless Base Station. This is a likely scenario for an operator of an extended Wireless Network such as on a campus, an urban area, or an office building, wishing to better monitor their network coverage area. Such an operator can require that Client Stations associating to his network include the program(s) needed to implement the invention.

According to Figure 10, Wireless Base Stations that are part of the operator's Wireless Network are referred to as Reference Base Stations or RBSs (110) and Wireless Client Stations simply as Client Stations or CS (200). There may also be Other Base Stations (100) within the network coverage area. Access to the operator's Wireless Network requires that that a CS (200) be associated to a RBS (110). Association is a negotiated relationship between the CS (200) and the RBS (110) and in the case of 802.11 systems is usually automatic. While associated the CS (200) and the RBS (110) continuously monitor RSSI and Signal Strength respectively to ensure a good connection and prepare to associate with an alternate Base Station (100 or 110) should the signal drop below acceptable levels. Signal Strength, or SS, measurements by RBSs (110) are made in a logarithmic scale of dBm.

A first program (225) logs the RSSI recorded by the CS (200) for its associated RBS (110). This log (226) might consist of recorded RSSI at some fixed time interval, or of records of the time and magnitude of changes in RSSI, or of a combination of the two, or based on any other criteria.

A second program (205) logs the SS measured by the RBA (110) for the possibly multiple CS (200) associated to it. This log (206) consists of recorded SS at some fixed time interval, or of records of the time and magnitude of changes in RSSI, or of a combination of the two, or based on any other criteria.

A third program (245) collates the logs (206 and 226) to compute a joint probability distribution function, JPDF (246), of SS and RSSI for each CS (200). This JPDF counts the number of times, or the proportion of time, that a given pair of SS and RSSI occur together. With SS
and RSSI represented as integers between -100 and 0 this discrete JPDF can in principle require up to 10,000 elements, but in practice less than 1,000 occur. It is also possible to group SS and RSSI in bins of 2, 3 or 5, for example, reducing storage by a factor of 4, 9 or 25 with little loss of accuracy. Therefore, it is quite feasible to store the discrete JPDF (246) for each CS (200) and this is more scalable than preserving all of the raw logs (206 and 226).

The previous step assumes that the transmission power of the Reference Base Station(110) is the same as that of the Client Station (200). Not illustrated is an optional function to accommodate differences in transmit power. This requires knowledge of the transmit power of Reference Base Station(110) and of the Client Station (200). These can be obtained from the radio settings of the RBS (110) and CS (200), or by referring to the manufacturer specification for these devices, or by a combination of the two, or by any other mean. The enhanced version of the third program computes a Corrected Signal Strength, or CSS, as follows:

CSS = SS + TXP110 - TXP200 where TXP110 is the transmit power of the Reference Base Station 110, and TXP200 is the transmit power of the Client Station 200.

Hence, the discrete JPDF is computed for the CSS-RSSI pairs.

It will be evident to those familiar in the art that other corrections can be made to the Signal Strength, for example, in the case of a telecommunications system using separate antenna for transmitting and receiving, the above may be further enhanced to:

CSS = SS + TXP110 - TXP200 + TXAG110 - RXAG110 - TXAG200 + RXAG200 where TXAG and RXAG are the transmit and receive antenna gains for the Reference Base Station (110) and the Client Station (200).

Yet a fourth program (255) accepts as input a Client Station Identification (likely its MAC address) and a RSSI and uses the JPDF (246) to return a corresponding SS estimate.
Common methods of using the JPDF (246) for this purpose include, but are not limited to, regressing a line or curve through the various SS-RSSI pairs weighed by their probability, or picking directly from the JPDF
either the average, the most likely or the median SS given the RSSI.
Statistical methods for evaluation the accuracy of the resulting SS estimate are commonplace, and such accuracy estimate can also be outputted.

It will be evident to those familiar in the art that the programs (205) and (225) do not necessarily run on the RBS (110) and CS (200), as they can be executed remotely on the same or on different computer platforms and access the data they require from the RBA (110) and CS
(200) through a variety of means such as, but not limited to, SNMP or WMI (Windows Management Instrumentation). Similarly the third and fourth programs (245, and 255) could be run on the same or on different computer platforms than the first two (201 and 221). Also, the functions of all the programs discussed above may be combined among themselves or with other programs to form a single computational unit fulfilling several functions. It will also be evident that various synchronization and optimization options are possible between and within these programs, for example in the case of program (255) regression coefficients might by pre-computed rather than calculated on-the-fly.

An operation might use the invention to obtain an exact measure of the Signal Strength with which CS (200) detects Other Base Stations (100) which may be rogue devices, that is Base Stations that do not belong to an operator. A common scanning or survey program could determine the RSSI
with which CS (200) sees these Other Base Stations (100), and then translate these into a normalized Signal Strength as per the invention. Combining several of such observations from multiple Client Stations would allow more precise location of these rogue devices.

B.3 ACTIVE SCANNING

We shall now describe another system whereby the invention can be put to practice for networks built upon the 802.11 (WiFi) standard. This one does not require association between the Clients Station and any Base Station. It uses active scanning (probing) and would be a more likely implementation in the context of a survey. Persons familiar with the art will appreciate that the invention is applicable to other wireless communication network with or without active scanning.
According to Figure 11, in this implementation, Base Stations are divided among Reference Base Stations (110) or RBSs and Other Base Stations (100) or OBSs , and Wireless Client Stations are simply Client Stations or CS (200). All network nodes whether RBS, OBS or CS
are uniquely identified by a MAC address an all messages exchanged between these nodes carry a Source Address, SA, and a Destination Address, DA, the latter possibly including a wildcard or being a broadcast address. Furthermore, all Base Stations (100 and 110) have one or more Service Set IDentifier (SSID) that identify the logical network they belong too. Signal Strength, SS, measurements by RBSs are made in a logarithmic scale of dBm.

The CS (200) include a program (221) that at some interval will cause it for perform an active scan, that is, it will send a Probe request (250) addressed to each SSID it knows to comprise RBSs (110).
The Probe message has the SA of the CS (200) MAC, the broadcast DA. Hence the program (221) listen for Probe Responses (260), and for each of these compiles a Probe Report (222) consisting of the RSSI, the source MAC address of the corresponding RBS (110) and possibly other parameters from each of the received Probes (260), together with a time stamp, the MAC
address of the CS
(200) and potentially other information characterizing the CS (200).

The RBSs (110) receive the Probe Request (250) and if they recognize their SSID must reply with a Probe Response (260) according to the 802.11 protocol. The Probe Response (260) has the SA of the responding RBS (110) and the DA of the CS (220) that had originated the Probe. Reference Base Stations (110) include a second program (201) causing it to compile Probe Report (202) for each Probe Response (260) it sends, and consisting of the Signal Strength, the source MAC and possibly other parameters from each of the qualifying Probes Request (250) or the CS (200) that has originated the Probe Request (250), together with a time stamp, the MAC
address of the RBS
(110) and potentially other information characterizing the RBS (110).

In general the time difference between a Probe Request (250) and a Response (260) is less than lOms and can be neglected.

Not illustrated is the aspect that the program (221) of a CS (200) may address Probe Requests (250) to several SSIDs, and that zero, one or more RBSs (110) might respond to each Probe Request (250).

Figure 12 illustrates the function of a third program (241) collating the Probe Reports (202 and 222). Within these reports, the program (241) searches for entries with the same or proximate time stamps and where:

~ the SA of an RBS Probe report (202) matches the CS_MAC in a CS Probe Report (222); AND

= the SA of the CS Probe Report (222) matches the AP_MAC for that same AP
Probe Report (222).

From the matched records, the program (241) constructs an SS-RSSI
correspondence record (242) for that particular Client Station using the measured Signal Strength from AP
Probe Report and the RSSI from the Client Station Probe Report.

The previous step assumes that the transmission power of the Reference Base Station (110) is the same as that of the Client Station (200). Figure 12 also illustrates an optional function (243) to accommodate differences in transmit power. This requires knowledge of the transmit power of Reference Base Station (110) and of the Client Station (200). These can be contained in the optional Other Data fields of the Probe Request Record, and Probe Records.
This is formation may be obtained by programs 201 and 221 by accessing the radio settings of the RBS
(110) and CS
(200), or by referring to the manufacturer specification for these devices, or by a combination of the two, or by any other means. The optional function of program 241 hence computes a Corrected Signal Strength, CSS, as follows:

CSS = SS + TXPZ00 - TXPZZo where TXP200 is the transmit power of the Reference Base Station200, and TXP220 is the transmit power of the Client Station 220.

It will be evident to those familiar in the art that other corrections can be made to the Signal Strength, for example, in the case of a telecommunications system using separate antenna for transmitting and receiving, the above may be further enhanced to:

CSS = SS + TXP200 - TXP220 + TXAG200 - RXAG200 - TXAG220 + RXAGZZO

where TXAG and RXAG are the transmit and receive antenna gains for the Reference Base Station(200) and the Client Station (200).

Yet a fourth program, not illustrated, accepts as input a Client Station MAC
address (CS_MAC) and an RSSI, and using the information from the SS-RSSI Correspondence Records (242) for that Client Station determine the Signal Strength, SS, equivalent to the RSSI.

It will be evident to those familiar in the art that the third program (241) or fourth program might be run on the Reference Base Station (110), on the Client Stations (200), or on yet a third computing device, and that the Probe Reports will need to be transferred from one device to another as required. Also, the functions of all the programs discussed above may be combined among themselves or with other programs to form a single computational unit fulfilling several functions. For example these programs can be commingled with functions that actively or passively scan for the RSSI of Other Base Stations (100), and thus provide survey data to be transformed to absolute Signal Strength data using the invention. It will be further evident that the illustrated content of the Probe Reports (202 and 222) are logical rather than literal representations, for example these may be implemented with relational tables arranged in different ways. As a further example Probe Reports for the same Client Station (200) bearing the same scan time stamp may be grouped together to avoid duplication of Time, CS_MAC and other_data fields. It will also be evident that the SS-RSSI Correspondence records (242) might be processed at their time of creation, at the time where translation is required or at any other time into some form that allows functional mapping between RSSI and SS.

A possible use of the invention in would be in a WiFi survey, where the Client Station (200) is mobile and scans the airwaves for the presence of all Base Stations (100 and 110). These scans would record the RSSI with which each Base Stations (100 or 110) is detected at various time.
Using the invention these RSSIs could be translated to Signal Strengths for a much more reliable mapping of coverage and location of WiFi devices. Furthermore surveys from multiple CS (200) would be consistent amongst each other and could be meaningfully combined.

C ENHANCED WIRED NETWORK LOCATION BASED SERVICES
C.1 OVERVIEW

We now describe a component of the invention that can serve to extend the precision of IP or other wired network location based services to the precision of a Wireless Network location based service as has been described previously or that is obtained by other means.

This component of the invention is particularly relevant to locating devices within a residential or SOHO private network, where a single public IP address is allocated to a gateway router that performs NAT or other distribution function so that all devices within the private network can access the Internet using the single public IP address.

Residential and SOHO private networks are physically small, typically less than 30m. Wireless Network Base Station (Access Points in 802.11 parlance) destined to residential or SOHO
application are typically collocated or integrated with the gateway router, and have a useful wireless range of about 30m also. This is to say that locating a Wireless Client Node connected to such a Base Station or the Base Station itself is tantamount to locating any device connected to the same private network and using the same public IP address, within 30m. These devices might include IP-TV sets, VOIP phone sets, gaming consoles, computers and a variety of other devices.
Location of home network equipment is vital to such services as e911 and various other Location Based Services for example targeted advertizing or location based gaming.
Although Internet connectivity providers, for example cable or phone operators, keep or can generate lists of street addresses and IP addresses of the gateway and other equipment they provide to subscribers.
Although Internet Connectivity Provider have access to the correspondence between IP address and street address of where they provide services, commercial and regulatory constraints make this information is either not accessible of very expensive to acquire.

As an alternative, users can input their location (e.g. street address, postal code...) in a web-page, and the web page can record IP address and MAC address of the routing equipment where the request originated. This is prone to a variety of errors, including misdirection, and requires the active participation of the end consumer. For example, in the event that an IP
address or MAC
address is re-assigned, the consumer must be involved to re-enter the information. Some consumers are suspicious and intentionally enter incorrect postal information.

The intent of this component of the invention is to legitimately acquire position information for these end-user devices, but without the a-priori consent of network service providers, without embedding location sensing technology in these devices, and without requiring any overt action on the part of consumers, and without requiring that end consumers compute or disclose their locations. A key aspect of this system is the devices collaborating to provide this function need to know or disclose a location in order to develop a useful map of the network.

C.2 METHOD FOR ENHANCED WIRED NETWORK LOCATION BASED SERVICES
The method of this aspect of the invention consists of:

a. Correlating the IP address used by a wireless network node with the location estimated for that device on the basis of proximity or detection of other wireless nodes;

b. Grouping all location estimates corresponding to the same IP address to determine the range of location associated to that address;

c. Providing a function whereby given an IP address, either a single estimated likely location, a mean for example, or a range of likely location is provided, in accordance to the previous grouping.

The method is further extended by automatically detecting when an IP address might be reallocated, and therefore old location data becomes irrelevant, when a new location within a group is drastically different from previous values. Several techniques can be used for this determination, including using a fixed threshold distance, for example if a new location is more than 100m away from previous ones, and using statistical methods, for example if a new value is more than 4 standard deviations away from the average of previous ones.

Furthermore, in the case where it can be determined that several Base Station nodes are associated with the same IP address, it is likely that this IP address is a proxy for an extended private subnet, for example a campus network rather than residential or SOHO
network. In such cases the IP location will be known to be less precise.

It will be evident to any familiar in the art that the above methodology can by directly extended to considering IP subnets and their geographical coverage. It will also be evident that the methodology can be adapted to provide relative proximity information rather than location information.

C.3 SYSTEM FOR ENHANCED WIRED NETWORK LOCATION BASED SERVICE

An easily implemented system for this component of the invention is combined with the surveying methods disclosed previously. An illustrative example is provided in Figure 13 showing several Base Stations (100) and Client Stations (200). The positions of a Particular Client Station (135) and a Particular Base Stations (135) are available or determinable by the methods and systems previously disclosed, or by some other means. In addition the Particular Client Station (135) contributes Scan Data (150) that may be part of the source of position data.
The particular Base Station (130) is connected to an Internet Gateway/Router (140). User devices Dl to D4 are also connected to the same Gateway Router (140) so that when these devices communicate over the internet they all use the same IP address as does the Particular Client Station (135).

The Particular Client Station (135) communicates a Scan Data (150) to an IP
Location Server (145) over the Internet. The IP Location Server (145) may among other things also be running the various programs necessary to implement the Wireless Network Topology, Mapping and Location functions disclosed earlier. When the Location Server (145) received the data packet containing Scan Data (150) it can examine it's Internet Protocol header and determine the source IP address.
This will be the IP address assigned to the Gateway/Router (140) and used by devices Dl to D4 as well as Particular Client Station (135). The Scan Data (150) contains among other things the MAC
address identifying the Particular Client Station (135). The IP Location Server (145) obtains a location for the Particular Client Station (135) using this MAC address as identifier by some means which could be the systems disclosed earlier to build Wireless Network Maps from the scan data provided by various wireless network nodes (200, 100, 130, 135). Hence, the IP
Location Server (145) can correlate this location with the IP address of the Gateway Router (140). Multiple location estimates for Scan Data (150) originating from the Particular Client Station (135) can be used to compute an average position, or other statistical parameters of position, corresponding to the IP
address assigned to the Gateway/Router (140).

A better system can be implemented in the case where the Scan Data (150) includes identification of the MAC address of the Base Station it is associated with. This extra information was not mentioned previously (not essential for Wireless Node Positioning) but is routinely available from the regular scanning processing of Client Stations. From this information, the IP Location Server (145) can deduce that the Scan Data (150) was actually transmitted through a Particular Base Station with a specific MAC address. Using this MAC address as an identifier the Location Server can obtain a location that is likely more relevant to the Gateway/Router (140) and its assigned IP
address. Hence, the Location Server can use the location of the Particular Base Station (130) instead of the Particular Client Station (135) for physically locating an IP.

The IP Location Server (145) can accomplish further tasks such as checking that only the Particular Base Station MAC address is associated with the Gateway/Router IP address, confirming the likelihood of a small localized network. Should the same Particular Base Station MAC suddenly be associated to a new IP address, then it is likely sign that the Gateway/Router was assigned a new IP
address lease. In such a case, location data for the old IP address can be transferred to the new one.

Not illustrated is the mechanism by which and external entity might query the IP Location Server (145) with an IP address to have returned a location. Also the case where multiple Client Stations are associated to the Particular Base Station (135) is not illustrated but is a trivial extension to the invention.

System embodying the same functionality can be implemented differently from above. A key point being that any of the system described earlier for surveying Wireless Networks without GPS or calibrated sensors, require the exchange of information among network entities. If any such exchange occurs over the Internet or similar wired network these exchanges will carry wired network addressing data and the location data collected from the Wireless Network can be used to enhance positioning onto the Wired Network.

Figure 14 illustrate yet another system, where the enhanced wired network location service is implemented separately from the Wireless Network surveying activity. The only information that needs to be provided to the Locations Server (145) to enable enhance wired network location, is a packet (155) containing the MAC address of the Particular Base Station (130).
Figure 14 also shows a separate Wireless Location Server (160) which given the MAC address for the Particular Base Station (130) returns the position (X, Y) of the Particular Base Station (130). Hence, the IP Location Server (145) correlates this position to the source IP address (or other wired network address) of the packet (155). In this scenario, the Particular Client Station (135) does not need to send any information about itself and thus can remains completely anonymous.
Furthermore, packet (155) could be originated by the Particular Base Station itself and never involve the Particular Client Station (135).

Although illustrated as two independent elements, the IP Location Server (145) and the Wireless Location Server (160) may actually be components running on the same computer.

D VIRTUAL CPE
D.1 OVERVIEW

We now describe a component of the invention that serves to improve manageability of wireless networks by extending the intelligence of a wireless network to each user devices connected to it.
Traditionally, managed wired networks are delivered to customers through a physical port located at the client premise and connected to a device managed by the network operator; this managed device is either located at the customer premise and often called "Customer Premise Equipment", or is located in a remote facility and extended to the customer premise by a cable extension. This enables network administrator to fully manage and re-configure each network port in use by their customers. By nature of wireless networks, these devices cannot be used to manage wireless medium since most customers will be connecting to wireless network using portable devices (such as laptops or PDAs) providing embedded wireless equipment that is not manageable by network operators.

This aspect of the invention is particularly relevant for managed wireless networks comprising many Base Stations (APs) but may also apply to single Base Stations, especially is it is surrounded by one or a plurality of other wireless Base Stations.

With the proliferation of wireless networks and equipment readily available on most user devices, connecting to a wireless network has become a ubiquitous task. Under these circumstances, it is expected that most locations, where users may desire to connect to a wireless network, will likely have a plurality of Wireless Network Nodes available for users to connect.
This is happening because Wireless Internet Service Providers may compete among themselves to get users connecting to their own network, but also because corporations or homeowners may have their own Wireless Network Nodes deployed as well.

With this aspect of the invention, which is referred to as "Virtual Customer Premise Equipment" or "Virtual CPE", users and network operators will be able to better manage their networks and improve end user network performance. It is a natural extension and is optimally used together with the previously disclosed components of the invention.

In order to better understand the application scope of this invention, we will consider as an example a typical small town (Figure 15) which comprises a downtown area (100) surrounded by residential areas (200) and further surrounded by rural areas (300). Downtown area comprises of a high density of large wireless networks managed by Internet Wireless Services Providers operators (101), and a high density of managed corporate wireless networks (102), all overlapping or not. Residential Areas comprise a medium to low density of large wireless networks managed by Internet Wireless Services Providers operators (201), and a high density of unmanaged SOHO
wireless networks (202), all overlapping or not. Rural Areas comprise a very low density (or none) of large wireless networks managed by Internet Wireless Services Providers operators (301), and a low density of unmanaged SOHO wireless networks (302), mostly non-overlapping.

In this context, users of wireless networks may require connection to one or a plurality of networks, managed or not. Typical operating systems available on wireless devices provide basic connectivity support enabling users to properly configure a wireless connection, secure or not, to wireless networks. To some extent, operating systems also provide basic mechanisms to allow the hardware platform to automatically change its connection point based on specific local conditions typically limited to link rate or signal level. What is lacking and is the scope of the Virtual CPE
aspect of our invention is the function of a managed customer premise equipment similar to those used in wired networks, with sufficient intelligence to control the client device to dynamically adapt its connection point, based on advanced information consolidated with neighboring client devices similar information when available, as described below.

D2. BASIC COMPONENTS OF THE VIRTUAL CPE

The first component is the Virtual CPE itself, which comprises of a program (firmware or software) on an end user device which has the capability of:

= Monitoring status and performance counters for various parameters relevant to communication over a particular network interface;

= Issuing or receiving test traffic for the purpose of characterizing the communication performance and diagnosing communication problems over the particular network interface;

= Controlling some aspects of the configuration of the particular network interface to optimize communication performance, enforce communication service level, or improve network performance; and = From time to time, communicating or receiving communication from a remote control server.

A second component is a program (the Central Application) running on one or distributed on a plurality of server or any computational platforms, connected amongst themselves or not, remote from the end user devices, said Central Application having the capability of:

= From time to time, communicating or receiving communication from the aforementioned program on end user devices;

= Analyzing status, performance counters and test traffic reports form the end user devices;

= From time to time, communicating with other devices running Central Applications to further analyze status, performance counters and test traffic reports form the end user devices; and = Reporting on the status of communication for end user devices.

The Central Application may also be distributed on Wireless Network Nodes themselves and therefore not require to run on a server. For example, many of today's Wireless Base Stations are computational platforms that can execute advanced tasks and therefore are perfectly capable of performing basic tasks required by the Central Application as described above, and also perform advanced functions as described below.

This basic architecture enables the Client Application to provide network-centric information to the network operator that would not otherwise be available.

D3. ADVANCED FUNCTIONS OF THE VIRTUAL CPE INVENTION

Both components of the Virtual CPE aspect of this invention will be using the gathered information to perform advanced tasks requiring intelligent information acquired or analyzed by other elements running a Virtual CPE or Central Application components of this invention.

The Virtual CPE component, residing on user devices, will be able to perform enhanced functions including but not limited to:

= make decisions about the configuration of the particular network interface;

= activate or deactivate itself depending on which network the particular network interface is connected to;

= obtain new control parameters or updates for its own logic, from some remote server; and = control operating parameters of a particular network interface, including but not limited to link rate, RTS/CTS, Fragmentation Threshold , or connection to available Wireless Network Nodes, enabling the end user device to always be connected to the most efficient Wireless Network Node available at this location and at this time.

It will be evident to those skilled in the art that through usage of a Virtual CPE, many other parameters of end user device can be controlled and further optimized.

The Central Application component will be able to perform enhanced functions including but not limited to:

= make decisions about the configuration of a particular network interface of a particular end user device and communicating this decision to the Virtual CPE program on the end user device; and = modify the control parameters or updating the logic of the Virtual CPE
program running on end user devices.

It will be evident to those skilled in the art that through usage of a Virtual CPE, many other parameters of end user device can be controlled and further optimized.

Claims (46)

1. The method for determining the relative proximity of wireless network nodes consisting of the following steps:
a. Collecting scan information from at least some of the Wireless Network Nodes, this information including at least of the identification of other Wireless Network Nodes detectable from the at least some Wireless Network nodes and optionally of an indication of received radio signal strength from the other Wireless Network Nodes;
b. Combining the scan information to build a wireless proximity topology which is a graph where proximate nodes are connected by edges on the basis of having been observed in the same scans.

c. Using the wireless proximity topology to determine the relative proximity of two wireless network nodes on the basis of the minimum number of edges required to form a path from one Wireless Network Node to the other.

2. Method 1. extended so that step b. includes qualifying each edge by a numerical value representing a local proximity metric between two connected nodes, on the basis of the signal strength with which one sees the other, similarity in scan signature of the two nodes or some other metric indicative of node proximity; and step c. further cumulating the local proximity metric along the shortest path between the two wireless network nodes to obtain a relative proximity measure between these two nodes.

3. The method for determining the absolute position of wireless network nodes consisting of the following steps:

a. Specifying at the absolute position of at least three (3) non-collinear wireless network Nodes in 2D, or at least four (4) non-coplanar wireless network nodes in 3D;
b. Determining a position for each wireless network node that is consistent with both the specified absolute position of some wireless network nodes and a wireless proximity topology.

4. Method 3. where step b. uses weighed centroid calculation to compute the position of wireless network nodes.

5. Method 3. where step b. uses a minimization technique to determine the position of wireless network nodes.

6. Method 3. where step b. further includes constraints on the position of wireless network nodes.

7. Method 3. Including the further step of plotting the position of wireless network nodes in geographical coordinates or some other Cartesian coordinates to obtain a wireless network map.

8. The method for determining the attenuation of wireless network radio signals comprising:
a. Comparing the absolute distance separating wireless network nodes connected by a wireless proximity topology graph edge, to the local proximity metric along the graph edge; and b. Assigning greater attenuation where this proximity is smaller than what would be expected from the distance.

9. The method 8. where step b includes the specification of constraints as to the location of attenuating bodies.

10. The methods 8. including the further step of computing the attenuation for multiple graph edges and plotting these in geographical coordinates or some other Cartesian coordinates to obtain an attenuation map.

11. The method of determining and eliminating anomalous nodes in a wireless proximity topology, based on the observation of an anomalous number of edges connected to a node.

12. The method of determining and eliminating anomalous nodes in a wireless network map, based on the observation of an anomalous distance between connected to a node.

13. A system for determining the relative proximity of Wireless Network Nodes comprising:
a. A function for collecting scan information from Wireless Network Nodes;
b. A function for collating collected scan information and building a logical structure representing the wireless proximity topology as a graph of connected wireless network nodes based on being observed in the same scan; and c. A function for computing the relative proximity of two wireless network nodes based on the length of the path through the graph of wireless proximity topology.

14. System 13 extended so that function b. attributes to each edge a numerical value representing a local proximity metric of two connected nodes, on the basis of the signal strength with which one sees the other, the similarity in scan signature of the two nodes or some other metric indicative of node proximity; and function c. further cumulating this proximity metric along the shortest path between the two wireless network nodes to obtain a relative proximity measure between these two nodes.

15. A system for determining the absolute position of wireless network nodes comprising:
a. A function for accepting the absolute position of at least three (3) non-collinear wireless network nodes in 2D or four (4) non-coplanar wireless network nodes in 3D;
b. A function for determining a position for each wireless network node that is consistent with both the specified absolute position of wireless network nodes and a wireless proximity topology.

16. System 15. where function b. uses weighed centroid calculation to compute the position of wireless network nodes.

17. System 15. where function b. uses a minimization technique to determine the position of wireless network nodes.

18. System 15. where function b. further accepts constraints on the position of Wireless Network Nodes.

19. System 15. including a further function for plotting the position of wireless network nodes in geographical coordinates or some other Cartesian coordinates to obtain a wireless network map.

20. A system for determining the attenuation of wireless network radio signals comprising:
a. A function for comparing the absolute distance separating wireless network nodes connected by a wireless proximity topology graph edge, to the local proximity metric along the graph edge; and b. A function for assigning greater attenuation where this proximity is smaller than what would be expected from the distance.

21. System 20. where function b includes the specification of constraints as to the location of attenuating bodies.

22. System 20. including the further function of computing the attenuation for multiple graph edges and plotting these in geographical coordinates or some other Cartesian coordinates to obtain an attenuation map.

23. A system for determining and eliminating anomalous nodes in a wireless proximity topology, based on the observation of an anomalous number of edges connected to a node.

24. A system for determining and eliminating anomalous nodes in a wireless network map, based on the observation of an anomalous distance between connected to a node.

25. A method for enabling radio signal strength measurement in a wireless communication network from uncalibrated nodes, consisting of:
a. Contemporaneously = measuring the absolute radio signal strength with which a calibrated node detects the uncalibrated node, and = obtaining an uncalibrated indication of radio signal strength with which the uncalibrated node detects the calibrated node;

b. Deriving a correspondence between the absolute radio signal strength and uncalibrated indication of radio signal strength from one or more instances of the above measurement for the uncalibrated node;
c. Using the above correspondence to recast into an absolute radio signal strength any uncalibrated indication of radio signal strength by the uncalibrated node.

26. A method as in 25 with added step of correct the radio signal strength to account for any asymmetry in power output or antenna gain in the calibrated and uncalibrated nodes.

27. A method as in 25 with the added step of combining the radio signal correspondence for multiple uncalibrated nodes that belong to a class, and to derive a correspondence that applies to that class rather than to individual uncalibrated nodes.

28. A system for enabling radio signal strength measurement in a wireless communication network from uncalibrated nodes comprising functional modules to:

a. Contemporaneously .cndot. measure the absolute radio signal strength with which a calibrated node detects the uncalibrated node; and .cndot. obtain an uncalibrated indication of radio signal strength with which the uncalibrated node detects the calibrated node.
b. Derive a correspondence between the absolute radio signal strength and uncalibrated indication of radio signal strength from one or more instances of the above measurement for the uncalibrated node.
c. Translate any uncalibrated indication of radio signal strength by the uncalibrated node to a radio signal strength using the above correspondence.

29. A system as in 28 with added functional modules to correct the radio signal strength to account for any asymmetry, if any, in power output or antenna gain in the calibrated and uncalibrated nodes.

30. A system as in 28 with the added functional modules to combine the radio signal correspondence of multiple uncalibrated nodes that belong to a class, and to derive a radio signal correspondence that applies to that class rather than to individual uncalibrated nodes.

31. The method of locating a device connected to the Internet consisting of:
a. Determining the location or the range of location covered by an Internet Protocol subnet by examining the location or range of location determined for wireless network nodes accessing the Internet through the Internet Protocol subnet; and b. Determining the location or range of possible location for the device, by attributing to it the location or range or location thus determined for the IP subnet it is a member of.

32. The method 31 in the particular case where the Internet Protocol subnet is an atomic subnet, that is, it consists of a single Internet Protocol address.

33. A system for locating a device connected to the Internet comprising functions to:

c. Determining the location or the range of location covered by an Internet Protocol subnet by examining the location or range of location determined for wireless network nodes accessing the Internet through the Internet Protocol subnet; and d. Determining the location or range of possible location for the device, by attributing to it the location or range or location thus determined for the IP subnet it is a member of.

34. A system as in 32 in the particular case where the Internet Protocol subnet is an atomic subnet, that is, it consists of a single Internet Protocol address.

35. A method for improving the manageability of networks comprising:
a. Executing the following functions on an end user device which has the capability of:

.cndot. Monitoring status and performance counters for various parameters relevant to communication over a particular network interface;

.cndot. Issuing or receiving test traffic for the purpose of characterizing the communication performance and diagnosing communication problems over the particular network interface;
.cndot. Controlling some aspects of the configuration of the particular network interface to optimize communication performance, enforce communication service level, or improve network performance; and .cndot. From time to time, communicating or receiving communication from a remote control device;
b. Executing the following functions on one or more device, remote from the end user devices:

.cndot. From time to time, communicating or receiving the aforementioned information from end user devices;

.cndot. Analyzing status, performance counters and test traffic reports form the aforementioned end user devices; and .cndot. Reporting on the status of communication for end user devices.

36. The method 35 where functions executed on the end user device include making decisions about the configuration of the particular network interface.

37. The method 35 where functions executed on the end user device can be activate or deactivate depending on which network the particular network interface is connected to.

38. The method 35 where the functions executed on the end user device obtain new control parameters or update for their logic, from some remote device.

39. The method 35 where the functions executed on the one or more remote device include making decisions about the configuration of a particular network interface and communicate this decision to the end user device.

40. The method 35 where the functions executed on the remote device include modifying the control parameters or updating the logic of the program running on end user devices.

41. A system for improving the manageability of networks comprising:

a. A program (firmware or software) on an end user device which has the capability of:
.cndot. Monitoring status and performance counters for various parameters relevant to communication over a particular network interface;

.cndot. Issuing or receiving test traffic for the purpose of characterizing the communication performance and diagnosing communication problems over the particular network interface;

.cndot. Controlling some aspects of the configuration of the particular network interface to optimize communication performance, enforce communication service level, or improve network performance; and .cndot. From time to time, communicating or receiving communication from a remote control device;
b. A program running on one or more device, remote from the end user device, which has the capability of:

.cndot. From time to time, communicating or receiving communication from the aforementioned program on end user devices;

.cndot. Analyzing status, performance counters and test traffic reports form the end user devices; and .cndot. Reporting on the status of communication for end user devices.

42. The system 41 where the program on the end user device has the added capability of making decisions about the configuration of the particular network interface.

43. The system 41 where the program on the end user device has the ability to activate or deactivate itself depending on which network the particular network interface is connected to.

44. The system 41 where the program on the end user device has the ability to obtain new control parameters or updates for its own logic, from some remote device.

45. The systems 41 where the program on the one or more remote device has the added capability of making decisions about the configuration of a particular network interface and communicating this decision to the program on the end user device.

46. The systems 41 where the program on the one or more remote device has the added capability of modifying the control parameters or updating the logic of the program running on end user devices.