The NearMe Wireless Proximity Server

Transcription

1 UbiComp The Sixth International Conference on Ubiquitou Computing, pp September 7-10, 2004, Nottingham, England. The NearMe Wirele Proximity Server John Krumm and Ken Hinckley Microoft Reearch Microoft Corporation One Microoft Way Redmond, WA, USA Abtract. NearMe i a erver, algorithm, and application programming interface (API) for client equipped with wirele networking (Wi-Fi) to compute lit of people and thing that are phyically nearby. NearMe compare client lit of Wi-Fi acce point and ignal trength to compute the proximity of device to one another. Traditional location ening ytem compute and compare abolute location, which require extenive a priori calibration and configuration. Becaue we bae NearMe entirely on proximity information, NearMe work out of the box with no calibration and minimal etup. Many location-aware application only require proximity information, and not abolute location: example include dicovering nearby reource, ending an to other peron who are nearby, or detecting ynchronou uer operation between mobile device. A more people ue the ytem, NearMe grow in both the number of place that can be found (e.g. printer and conference room) and in the phyical range over which other people and place can be found. Thi paper decribe our algorithm and infratructure for proximity ening, a well a ome of the client we have implemented for variou application. 1 Introduction One of the goal of ubiquitou computing i to build application that are enitive to the uer context. An important part of context i the lit of people and place that are cloe to the uer. One common way to determine proximity i to meaure abolute location and compute ditance. However, computing abolute location i not necearily eay (ee [1] for a urvey), epecially indoor, where GPS doe not work, and where people pend mot of their time. The NearMe wirele proximity erver dipene with the traditional computation of abolute location, and intead etimate proximity (ditance) directly. The advantage of uing proximity i that, unlike location ening technique, it doe not require any a priori geometric calibration of the environment where the ytem i to be ued.

2 NearMe i a erver, algorithm, and application programming interface (API) meant to compute lit of nearby people and place for client running on variou Wi-Fi device. NearMe determine proximity by comparing lit of Wi-Fi acce point (AP) and ignal trength from client. We refer to thee lit a Wi-Fi ignature. By comparing Wi-Fi ignature directly, NearMe kip the intermediate tep of computing abolute location, which mean it work without calibration for client equipped with Wi-Fi device. Our ytem exploit the growing ubiquity of Wi- Fi acce point, uing them not necearily a entry point to the network, but a ignature that ditinguih one location from another, much like mot Wi-Fi location effort (e.g. RADAR[2] and Place Lab[3]). NearMe compute proximity a oppoed to abolute location. While proximity can be eaily computed from abolute location, NearMe demontrate that computing proximity directly can be much eaier. Proximity i ueful for polling for nearby people and place and for computing how far away they are. Proximity cannot, in general, anwer quetion about the abolute location of omething nor how to get there. Therefore, our ytem i not intended to be ued to find lot thing nor to map route to detination. Intead, NearMe i intended to dicover what i already nearby and to augment context for ubiquitou computing. NearMe divide proximity into two type: hort range and long range. People and place in hort range proximity are defined a thoe with at leat one Wi-Fi acce point in common. We have developed a function that etimate the ditance between client in hort range proximity baed on imilaritie in their repective Wi-Fi ignature. Short range proximity i primarily intended for finding people and place within the coverage of one acce point, which generally range from meter. Long range proximity mean that the two object of interet are not within range of any one acce point, but are connected by a chain of acce point with overlapping coverage. The NearMe erver maintain a lit of overlapping acce point that i automatically built from acce point data that client provide during the normal ue of the NearMe erver. The erver periodically can through all it tored acce point data to create a topology of overlapping AP. It alo examine time tamp on the data to create travel time etimate between pair of acce point. Thee travel time and AP hop are provided to client a etimate of the nearne of people and place in long range proximity. Both hort range and long range proximity are computed from Wi-Fi ignature without any explicit calibration, meaning that deployment of NearMe i only a matter of getting people to run the oftware. People can ue NearMe by running one of a few different client we have written to run on a Wi-Fi-capable device. The client i operated by firt regitering with the ytem, ending a Wi-Fi ignature to the erver, and then querying for people and variou type of object or place nearby. Object like printer and place like conference room and other reource are inerted into the databae by a uer phyically viiting that place, regitering a the object or place, and ending in a Wi-Fi ignature. Once regitered in thi way, object and place can be found by anyone ele uing the ytem. Traditional location-baed ytem ue the ame ort of regitration of meaningful location, only they alo require an intermediate tep of calibration to go from enor meaurement to abolute location. For intance, Wi-Fi baed poitioning ytem need a ignal trength map generated from

3 either manually meauring ignal trength or from imulating them baed on meaured acce point location, e.g. RADAR[2]. NearMe kip thi geometric calibration tep in favor of a collaborative proce of regitering ueful location by multiple uer which are then hared with all uer. Hence the ytem can gain acceptance by gradual adaptation without an onerou up-front invetment to calibrate a pecific environment. Thi alo make the ytem potentially more amenable to inevitable change in Wi-Fi acce point: If a Wi-Fi ignature i no longer valid, uer would be motivated to report a freh Wi-Fi ignature for the place that are important to them. The next ection of thi paper decribe related work. The NearMe client and erver function are dicued in Section 3 and 4. Section 5 decribe our experimental work to develop a robut function to etimate ditance between client in hort range proximity by comparing their Wi-Fi ignature. In Section 6 we decribe ome of the application we have implemented uing NearMe, and we conclude in Section 7. 2 Related Work The reearch decribed in thi paper i related to everal other project and technologie in ubiquitou computing, including location ening, proximity meaurement, and device dicovery. There are many way to automatically meaure location [1], including Wi-Fi ignal trength, GPS, and active badge. Our proximity technique ue Wi-Fi ignal trength. Wi-Fi ha been uccefully ued for computing location, tarting with the RADAR ytem [2] and continuing with Intel Reearch growing Place Lab initiative [3], among other. Some location ytem require the deployment of pecialized hardware in the environment, e.g. atellite for GPS and pecial receiver and/or tranmitter for active badge. All of them require offline etup in the form of calibrating the region of ue or mapping of bae tation. NearMe i different in two ignificant way: (1) it depend only on exiting Wi-Fi acce point; and (2) for finding nearby Wi-Fi device, it require no calibration or mapping. For finding nearby place, it only require that the place ha been regitered once with the Wi-Fi ignature from that location. Proximity, a ditinct from location, i an important part of a peron context. Schilit et al.[4], in an early paper on context-aware computing, define context a where you are, who you are with, and what reource are nearby. Note that the latter two of thee three element of context depend only on what i in a uer proximity, and do not require abolute location. Hightower et al. [5] decribe how locationdependent part of context can be derived from raw enor meaurement in a Location Stack. An Arragement layer take location inference from multiple people and thing to arrive at concluion about proximity, among other thing. NearMe jump directly from enor meaurement (Wi-F ignal trength) to proximity arrangement without the intermediate complexitie of computing location. Several ytem provide wirele conference device that are aimed at aiting conference attendee with proximity information. Thee are generally mall wirele

4 device that can be eaily carried or worn, normally by people in large group. Example include ntag [6], SpotMe [7], IntelliBadge [8], Conference Aitant [9], Proxy Lady[10], and Digital Aitant [11]. Among the feature of thee device are their awarene of location and/or who i nearby. Some of them ue bae tation in the environment to meaure location, while other ue peer-to-peer communication to find other nearby conference device. Except for the Conference Aitant, thee are pecialized hardware device, wherea NearMe run on any client that upport network acce and Wi-Fi. In addition, NearMe need no pecial infratructure, and it give proximity information about people and thing that can be much farther away than the range of regular peer-to-peer communication by uing it knowledge of adjacencie of overlapping acce point. There are well-etablihed protocol for peer-to-peer device dicovery uing Bluetooth and Infrared Data Aociation (IrDA) [12]. Bluetooth work in the 2.4GHz RF range and dicover other Bluetooth device by hopping through a equence of channel looking for device of a pecified type, like PDA or printer. NearMe client may alo earch for pecific type of thing, including people, printer, and conference room. But unlike NearMe, Bluetooth cannot dicover thing that are along a chain of device with overlapping coverage. Thu the dicovery range of Bluetooth i limited to about 10 meter. While Bluetooth doe not require a clear line of ight between device, IrDA doe, and it only work over a range of about one meter. Detecting ynchronou uer operation, or hared context in enor data, repreent another related et of technologie. For example, Smart-It Friend [13], ynchronou geture [14], and Are You With Me? [15] detect imilar accelerometer reading due to haking, bumping, or walking. In general, any ynchronou uer operation can be ued to identify device. For example, SyncTap [16] form device aociation by allowing a uer to imultaneouly pre a button on two eparate device. Stitching [17] i a related technique for pen-operated device: a uer make a connecting pen troke that tart on the creen of one device, kip over the bezel, and end on the creen of another device. Thi allow the uer to perform an operation that pan a pecific pair of device, uch a copying a file to another device. NearMe complement thi cla of technique, becaue NearMe allow uch ytem to narrow the et of potential aociation to only thoe device that are actually in phyical proximity. Thi help reolve unintentional coincidence in ened context, and it reduce the number of poible device that need to be earched for aociation. Section 6.3 decribe how we ue NearMe to implement thi functionality for the Stitching technique. NearMe i mot cloely related to two commercial ytem: Trepia [18] and peerto-peer ytem like Apple ichat AV [19]. Trepia let uer communicate with other nearby uer that it find automatically. Uer can manually pecify their location and Trepia alo ue wired and Wi-Fi network commonality to infer proximity. While NearMe alo ue Wi-Fi, it make ue of ignal trength to etimate finegrained proximity, and it alo ue an automatically updated table of phyically adjacent acce point to determine longer range proximity. ichat AV let uer on the ame local network find each other for intant meaging or video conferencing. Similar ytem for computer game let uer on the ame network find other nearby gamer. NearMe i more general in that it doe not require uer to be on the ame net-

5 work in order to find each other, and that it let uer find nearby place a well a other people. 3 The NearMe Client The client portion of NearMe i a program that uer run to interact with the proximity erver. The programmatic interface to the erver i a web ervice which preent a imple et of API for a client to ue, making it i eay to write new client. We have written even: four for Window XP, two for Pocket PC 2003, and one in the form of an active erver page (ASP). Each client perform the ame three function: 1. Regiter with the proximity erver. 2. Report Wi-Fi ignature. 3. Query for nearby people and place. We will preent a general Window client a an example a it demontrate mot of the ytem functionality. Some of the other application-pecific client are detailed in Section 6. The main work of NearMe i performed by the erver, which we dicu in Section 4. The next three ubection explain the above three tep of uing the client. 3.1 Regiter with Proximity Server The uer firt tep in uing the proximity erver i to regiter with a choen name, a hown in Figure 1-a. New uer can type in any name, and they alo choe an expiration interval in hour a well a a uniform reource locator (URL) that other can ue to look up more information. The expiration interval erve a a trigger for the erver to automatically delete old uer. More importantly, it allow a uer name to be automatically removed from the erver to help preerve privacy after he or he i no longer uing the erver. One cenario we enviion i that a uer will regiter with the erver at the beginning of a meeting in order to find the name of other people in the ame room. Since thi uer know the meeting will end in one hour, he et the expiration interval to one hour, meaning he will not need to remember to remove hi name from the erver after the meeting. Upon regitration, the client application receive a globally unique identifier (GUID) from the erver. Thi GUID i ued by the erver to identify which data to aociate with which uer. If a uer quit the client application and want to retart later, the regitration function give him or her opportunity to regiter a a previou uer intead of a new one. The erver then repond with the GUID of the choen previou uer which i ued by the client to tag future tranmiion.

6 a) George Wahington regiter with hi name and a URL. He could have alo regitered a one of everal different place or thing lited in the left column. b) He report hi current Wi-Fi ignal trength to the erver. He could optionally tart a periodic equence of report with a choen time interval. c) George Wahington querie for nearby people, finding Thoma Jefferon haring an acce point. Two other are ome number of acce point hop away, a given in the lower right lit. Thi lit give the ditance to the two other both in term of acce point hop and the minimum time it ha taken anyone to walk between them. d) He querie for receptionit dek and find four, but none hare an acce point. The left lit give the variou type of place that can be queried. Figure 1: Thee creen hot how a typical erie of action and repone by a uer of the NearMe Window client.

7 A uer can regiter a a peron or a any of the poible type below: peron elevator kitchen bathroom conference room tair mail room titchable device printer cafeteria reception dek demo peron The non-peron type are intended to allow a uer to tag an object or location with a Wi-Fi ignature. Each regitered non-peron intance i given a name, jut like uer, but there i no expiration interval. Once tagged, human uer can query the erver for nearby intance of thee type a well a people. For an enterprie, an alternative, more ecure regitration method would be to ue the uername/paword cheme in force for the enterprie computer network. A wider deployment could ue a publicly acceible authentication ervice uch a Microoft Paport.NET. Alo, it would be valuable to add the ability to limit a uer viibility to jut a certain group, like hi or her lit of intant meenger buddie. 3.2 Reporting Wi-Fi Signature Once regitered, a client can report acce point and their meaured Wi-Fi ignal trength to the erver a hown in Figure 1-b. The Window client allow the uer to make a one-time report or et up a periodic erie at a choen time interval. The periodic mode i intended to be ued by a moving client. A client make generic API call to retrieve a lit of acce point Media Acce Control (MAC) addree (one for each detectable acce point) and the aociated received ignal trength indicator (ri) from it wirele device. Thi lit i the Wi-Fi ignature. We only ue AP that are in infratructure mode, not ad hoc, a infratructure mode AP are normally tatic. Ri i normally meaured in decibel referred to one milliwatt, or dbm. The uual range i approximately -100 to -20 dbm, and the API we ue report ri a an integer. Ri generally decreae with ditance from the acce point, but it i affected by attenuation and reflection, making the relationhip between location and ri complex. MAC addree are 6-byte identifier that uniquely identify acce point. Our client adhere to the general recommendation that one need to give an network interface card (NIC) at leat three econd to can for acce point after the can i triggered. The client do no filtering of detected acce point, o the lit can contain acce point aociated with any network, whether or not the client ha credential to interact with them. The client can alo detect acce point with no network connection that are effectively functioning a only location beacon. The et of MAC addree and ignal trength i the Wi-Fi ignature. The client report conit of the client GUID and Wi-Fi ignature, which we repreent a { GUID ( m, )(, m, ),, (, )}, 2 m K n n (1) for n detectable acce point, were ( ) i i m, are the MAC addre and ri of the th i detected acce point repectively. Thee ordered pair are not reported in any particular order.

8 3.3 Querying for Nearby People and Place The lat client function i to make a query for nearby people or place a hown in Figure 1-c and Figure 1-d. The uer elect a type to query for, either other people or omething ele from the lit of type, e.g. printer, conference room, etc. The erver repond with two (poibly empty) lit of nearby intance of the requeted type. The firt lit, in hort range proximity, how thoe intance that have at leat one detectable acce point in common with the querying client, orted roughly by ditance. The econd lit, in long range proximity, contain intance that can be reached by hopping through acce point with overlapping coverage, orted by the number of hop required. Some of the intance found within hopping ditance are alo reported with an etimate of the amount of time it would take to travel to it. Section 5 explain how we ort the lit of hort range proximity. Section 4 explain how we compute hop and travel time for long range proximity. 3.4 Other Client A web ervice act a the API for acceing the NearMe databae. Thi make it eay to write other client. We have a PocketPC client that duplicate the functionality of the Window client decribed above. We alo have an Active Server Page (ASP) client that run in a conventional web brower in repone to a URL that ha the Wi-Fi ignature encoded a imple ASCII parameter. Since the web ervice interface to the erver i baed on the imple object acce protocol (SOAP), any SOAP client could acce the ervice, including thoe running on Linux and MAC OS. 4 The NearMe Server The NearMe erver i a SQL databae that maintain table of active uer, tatic reource (like printer and conference room), and their aociated Wi-Fi ignature. It alo maintain metric and topological data about the phyical layout of acce point derived from Wi-Fi ignature. It ue thee table to repond to client requet poed through an API in the form of a web ervice. The ret of thi ection decribe the major element of the NearMe erver. 4.1 Scan Source Scan ource are people or place that can be aociated with Wi-Fi ignature. Along with a can ource type, each can ource i repreented with a GUID, a friendly name, an optional URL, an optional addre, and an expiration time for people. The NearMe erver check for expired can ource every hour and delete their name.

9 4.2 Wi-Fi Signature Wi-Fi ignature are lit of MAC addree of infratructure mode acce point and their aociated ignal trength generated on the client device. On the erver, each Wi-Fi ignature i tagged with the GUID of it can ource and a ever-generated time tamp. Wi-Fi ignature are never deleted, even if their aociated can ource i deleted due to expiration. Becaue they are only identified with the GUID of the can ource, uch orphaned ignature cannot be traced back to their originating can ource. We preerve all the Wi-Fi ignature in order to compute table decribing the layout of acce point, decribed next. 4.3 Acce Point Layout Time-tamped Wi-Fi ignature are a valuable ource of information regarding the phyical layout of acce point. Layout information can in turn be ued to aid the computation of long range proximity. The NearMe erver procee the Wi-Fi ignature in two way. Firt, the erver compute the topology of the acce point by examining which pair of acce point have been detected imultaneouly by the ame client. Thi indicate that the acce point have phyically overlapping coverage and are therefore conidered adjacent. Note that adjacent acce point do not have to be on the ame network backbone nor even on any backbone at all. Conceptually, the NearMe erver build an adjacency matrix of acce point with overlapping coverage. From thi matrix, it compute an undirected graph with acce point a node and edge between adjacent node. In reality, the erver compute a table of pair of acce point and the minimum number of edge or hop between them, up to ome maximum number of hop (currently eight). Our erver i programmed to recompute thi table every hour in order to keep up to date with the latet Wi-Fi ignature. In thi way, the phyical cope of NearMe automatically grow a more uer report Wi-Fi ignature from more location. Thi table i ued to find people or thing in long range proximity of a client, where long range indicate that the two hare no detectable acce point but can be connected by ome number of hop between adjacent acce point. The number of hop i reported to client to give the uer a rough idea of the ditance to a can ource in long range proximity. Thi table of adjacent acce point i alo ued a an anti-poofing guard. Client can be optionally programmed with a web ervice call that check to ee if the acce point in a Wi-Fi ignature have ever before been een together by any other client. If they have not, thi raie the upicion that the Wi-Fi ignature i not valid and that it wa created artificially. While thi anti-poofing check help maintain the integrity of the databae, it alo prevent any growth in the lit of adjacent acce point, o it i only ued on untruted client. The econd piece of layout information concern the metric relationhip between acce point, and it come from the time tamp on the Wi-Fi ignature. Thee are ued to find the minimum tranit time between pair of acce point, which can give a uer an idea of how long it will take to travel to omeone or omething that appear

10 on the long range proximity lit. Every hour, our erver i programmed to create group of Wi-Fi ignature that hare the ame GUID, meaning they came from the ame can ource (e.g. the ame peron). It contruct all poible unique pair of acce point within each group. For each member of each pair, the erver look up their repective time tamp and aign the reulting time interval to the pair. All thee pair are recombined, where all but the minimum time interval i kept for duplicate pair. The reult i a lit of MAC addre pair and the minimum time any client wa able to tranition between them. Thee time are included in the lit of can ource in long range proximity, a hown in Figure 1 c-d. The time erve a an upper bound on how long it would take to travel directly to that can ource. It i an upper bound becaue we cannot guarantee that the minimum time oberved actually came from a direct travere between the two acce point. A more ophiticated verion of thi analyi could cluter travel time between acce point to account for the different peed of different poible mode of tranportation, like walking, biking, and driving. Both the topological and metric table provide valuable proximity information and are computed automatically without any extra calibration work required from either the human client nor the ytem maintainer. All the data for thee table i contributed by human uer, but their data i anonymized by default after expiration. We enviion thi type of proximity information to be ued to find people and place that might typically be out of range of one acce point, like a receptionit dek in a large office building, a cafeteria, a friend on campu, or a cutodian. The travel time data would be ueful for picking the nearet of the requeted item a well a to plan how much time to allow to reach it. The long range proximity table are computed baed on all pat data ubmitted to the erver. If acce point in the environment are removed or added, long range proximity computation will till be valid. Moving an acce point, epecially to another part of the topology, would create invalid graph link. One olution we have not implemented i to expire Wi-Fi ignature older than a certain threhold. A of thi writing, our databae ha 1123 unique acce point recorded from around our intitution. On average, each acce point overlap with 16.6 other acce point. The average number of acce point per Wi-Fi ignature i 6.1. Our databae of acce point i imilar in ome way to thoe ued for Intel Reearch Place Lab initiative [3] and publicly acceible war driving databae like NetStumbler [20] and WiGLE [21]. The main difference i that our databae i not dependent on traditional war driving where acce point data mut include abolute location. Intead, our databae i built up in the normal coure of uing our client, with the only ground truth data being the name of location of interet, like printer and conference room. Thu NearMe ha a lower barrier to entry, albeit at the expene of not giving abolute location. The more traditional war driving databae could be eaily adapted to work with NearMe. Indeed, one of the NearMe client allow the databae to be updated from a war driving log file. An intereting quetion i how NearMe could benefit from the addition of ome abolute location data.

11 5 Range Approximation for Short Range Proximity People and place within hort range proximity of a client are defined a thoe that hare at leat one acce point with the client. In computing the hort range lit on the erver, it i ueful to ort the lit by ditance from the client. Then a uer can, for intance, pick the nearet printer or pick the N nearet people. If NearMe were a location-baed ytem, then orting by ditance would be an eay matter of computing Euclidian ditance and orting. However, ince we intentionally avoid the computation of abolute location, we mut find another way. Intuitively, the ditance between two can ource hould be related to the imilarity of their Wi-Fi ignature. If they ee everal acce point in common, and if the ignal trength from thoe acce point are imilar, then it i more likely that the two are nearby each other. We deigned an experiment to ee how accurately we could compute the ditance between client and which feature of the Wi-Fi ignature were bet to ue. 5.1 Similarity Feature Suppoe the two Wi-Fi ignature from client a and b are {( m, )(, m, ), K,( m )} and {(, )(, m, ),,( m )} n, a na m K n, b nb The m are the AP MAC addree, and the are the aociated ignal trength. Client a detected n a acce point and client b detected n b. In order to define imilarity feature, we firt form the et of acce point that were detected by both client and the aociated ignal trength from each client: {( m,, )(, m,, ),,( m, )}, 1,1,1,2,2,2 K, n, n, Here there were n acce point that were detected by both client, the i th of which wa m, i, which client a and b meaured at ignal trength, i and, i, repectively. Our goal wa to find a numerical function of the two Wi-Fi ignature that give the phyical ditance eparating the two client. We firt had to create numerical feature from the two ignature that we thought might be ueful for computing ditance. The four feature we experimented with are: 1. The number of acce point in common between the two client, repreented by n. We expect that an increaed n i an indication of horter range. 2. The Spearman rank-order correlation coefficient [22], denoted by ρ. Thi number repreent how cloely the two client ranked their common acce point by ignal trength. Intuitively, a more imilar ranking indicate the client are cloer together. The ranking approach wa inpired by the RightSPOT ytem [23], which ue, n

12 ranking of FM radio tation ignal trength to claify a mall device into one of a dicrete et of location. The advantage of ranking i that different radio reciever, uch a the Wi-Fi NIC in our client, may well meaure ignal trength in different way. The ranking of the acce point by ignal trength will be the ame on both client if they both receive the ame ignal trength and they both have a monotonic function relating input and meaured ignal trength. While thi ignore information contained in the abolute ignal trength, it i robut to inevitable variation in NIC, including difference in deign, manufacturing, hielding, and antenna orientation. Mathematically ρ i computed by firt making two orted lit of the ignal trength een in common by both client. For example, thee lit might be (, 1,,2,,3) = ( 70, 50, 80) and (, 1,,2,,3 ) = ( 90, 60, 70). In each lit, we replace each ignal trength with the acending rank of that ignal r, r, r 2,3,1 and trength in it own lit to make two rank lit, e.g. ( ) = ( ) (, r, r ) ( 1,3,2 ) ρ i given by[22]: r. The Spearman = ( r )( ) i i r ri r 2 ( ri r ) ( ri r ) ρ = (2) i (a) (b) where r and r are the mean of the rank. In our example, ρ = 0.5. ρ range from -1 to 1, indicating poor to exact correlation between ranking, repectively. 3. Sum of quared difference of ignal trength: ( r ) 2 i ri i c = (3) i A maller value of c indicate more imilar ignal trength and preumably horter range. Thi doe not account for the variability in meauring ignal trength that the ranking coefficient ρ i intended to ignore. 4. Number of acce point unaccounted for in each lit. Thi indicate the number of left over acce point that are not in the lit of common acce point, nu = na + nb 2 n. More unaccounted for acce point could indicate that the client are farther apart Range Experiment We gathered two et of Wi-Fi ignature with known ditance between can. One et, ued for training, wa taken on one floor of a normal office building. The other et, ued for teting, wa taken in a cafeteria. We walked to variou location in both venue, imultaneouly logging Wi-Fi ignature and the device approximate location by clicking on a building floor plan. In order to tet the effect of different Wi-Fi

13 NIC, we gathered data from ix different one: Dell TrueMobile 1150 Serie (built in to laptop) Microoft Wirele (USB Adapter) ORiNOCO (PC Card) Actiontec 802.1b Wirele Adapter (USB Adapter) Cico Aironet 340 Serie (PC Card) Linky b Wirele (USB Adapter) For each of the two venue, we created pair of Wi-Fi ignature uing the location tamp to determine their Euclidian eparation ditance in meter. We eliminated thoe pair that were taken with the ame Wi-Fi NIC in order to tet the more realitic ituation that the two Wi-Fi ignature will come from different NIC. In the office building data et, we gathered a total of 2218 Wi-Fi ignature and created 1,441,739 pair of Wi-Fi ignature after eliminating thoe pair created with the ame NIC. For the cafeteria data et, we took 1038 Wi-Fi ignature and created 572,027 pair. Our goal wa to find a function that take ome or all of the feature of a pair of Wi-Fi ignature from Section 5.1 and return an etimate for the phyical ditance between them. We choe polynomial a our function, a there are no welletablihed phyical model that relate our feature and ditance. For our experiment we varied the order of the polynomial, N o, from one to four, and we varied the number of feature, N 4 f, from one to four. For each N f, we teted all ( 4 chooe N f N f ) poible combination of feature. For example, if N o = 2, N f = 3, and the three feature were n, ρ, and c, then the polynomial would be d = a a a a 2 200n 100 n ρ + a + a n a n c + a ρ + a ρ + a c c + ρ c + (4) where d i the phyical ditance between the location at which the two Wi-Fi ignature were taken, and the a are the coefficient we etimated uing leat quare. In computing the coefficient, we ued weighted leat quare to equalize the influence of each poible pair of NIC, becaue each NIC wa not repreented exactly equally in the experimental data. We ued the office building data a training data to compute polynomial coefficient. Becaue of the large number of data point, we performed the actual leat quare fitting on 10 ubet each coniting of a random 10% of the data, and we kept the coefficient that gave the minimum rm ditance error from each ubet. The reult are hown in Table 1. For the training data, the rm error wa in the vicinity of 7 meter, with a minimum of 6.43 meter for the 3 rd degree polynomial uing all four feature. We alo evaluated how well the computed polynomial ranked the ditance uing the Spearman rank correlation coefficient between the actual and computed ranked ditance. (Note that we ue Spearman twice: once a a way to meaure the

14 Number of Feature 1 2 Feature() AP In Common Spearman ρ RSSI Difference Unaccounted for AP AP In Common Spearman ρ AP In Common RSSI Difference AP In Common Unaccounted for AP Spearman ρ RSSI Difference Spearman ρ Unaccounted for AP RSSI Difference Unaccounted for AP Polynomial Degree RMS Err (m) Spearman ρ Train Tet Train Tet Number of Feature Feature() 3 AP In Common Spearman ρ RSSI Difference AP In Common Spearman ρ Unaccounted for Ap AP In Common RSSI Difference Unaccounted for AP Spearman ρ RSSI Difference Unaccounted for Ap 4 AP In Common Spearman ρ RSSI Difference Unaccounted for AP Polynomial Degree RMS Err (m) Spearman ρ Train Tet Train Tet Table 1: Reult of training and teting polynomial to etimate ditance from Wi- Fi ignature. The Train column under the RMS Err (m) column how the rm error in meter after the leat quare fit to the office building data. The Train column under Spearman ρ how how well the computed polynomial ranked the computed ditance compared to the actual ditance. The two Tet column how how well the office building polynomial coefficient worked on the cafeteria data. In general, increaing the number of feature and the degree of the polynomial did not ignificantly improve accuracy. rank imilarity of ignal trength and once a a way to ae how well our variou polynomial rank phyical ditance compared to ground truth.) Thi i ueful ince ome application may want to preent ranked lit of nearby people rather than their abolute ditance. The maximum Spearman correlation for the training et wa 0.49, alo for the 3 rd degree polynomial uing all four feature. We ued the polynomial coefficient from the office building training et to ee how well they worked for the cafeteria data et. Thi give u an idea of whether or not we could put forth a broad recommendation for which feature and function to ue for any general ituation. Thi will require more teting in the future, but the cafeteria data how reaonable performance with a minimum rm error of meter and a maximum Spearman correlation of 0.43, both uing a 1 t degree polynomial on n, ρ, and c. The number of unaccounted for acce point, n u, wa the wort performing ingle feature in term of rm error on the tet et. Intuitively, the mot attractive feature are n (the number of acce point in common) and ρ (the Spearman correlation of the ignal trength), becaue they are robut to meaurement difference between NIC. The tet data indicate that the bet performing polynomial for thee two feature wa a 1 t degree polynomial, giving an rm error of meter and a Spearman correlation of 0.39, both very cloe to the bet performance over all the tet cae. The actual polynomial wa

15 d 2.53n 2.90ρ (5) = A expected, thi equation indicate that the etimated ditance in meter ( d ) decreae when more acce point are een in common ( n ) and when their relative ranking are more imilar ( ρ ). One intereting apect of thi equation i that d n d ρ, meaning that n and ρ have approximately the ame level of influence on the etimated ditance. Given thi imilarity in influence, if the goal i to ort Wi-Fi ignature pair by ditance, a reaonable heuritic i to imply ort by the um n + ρ. Thi i what we do on the erver to ort lit of intance in hort range proximity. Although thi equation worked reaonably well for our two data et, the actual coefficient are likely not broadly applicable to other location where there could be difference in building material, architecture, acce point denity, and acce point tranmiion trength. One example of it poible inapplicability i in an area denely populated with acce point. In uch a cae, n could be large enough that the computed ditance i negative. However, thi analyi doe indicate which feature of the Wi-Fi ignature are important, and it lead to the n + ρ heuritic for orting by ditance. No calibration i neceary to apply thi heuritic to a new environment, in contrat to Wi-Fi location ytem that normally require manually contructed or imulated radio map. Thee calibrated ytem do provide more accuracy, however, with median abolute location error of 2.37 meter for RADAR[2], 1.53 meter for LOCADIO[24], and 1 meter for the ytem of Ladd et al.[25]. For proximity, thi level of accuracy i not alway neceary. Short range proximity computation are robut to the addition and deletion of acce point, becaue the ditance computation i baed on only the lit of acce point that two Wi-Fi ignature have in common. A moved acce point could caue large error. However, for finding nearby people who are updating their Wi-Fi ignature frequently, a our baic client allow (Figure 1b), even moved acce point are eaily tolerated. 6 Application The functionality of the NearMe erver i expoed a a web ervice, making it eay to create new client. Thi ection decribe three potentially ueful client. 6.1 Sample Client with URL The ample client in Figure 1 allow people and place to be regitered with a URL. For example, people might regiter with their home page. For ome place, like reception dek, we regitered a URL giving a map to help viitor find their way. Intance with a regitered URL how up on the proximity lit with a behind their name. The uer can click on thee name to bring up a web brower howing their

16 Figure 2: Thi program allow a uer to end to other uer in proximity. URL. Each regitered peron and place i eentially tagged with a Wi-Fi ignature that erve for filtering baed on location. The changing lit of proximal people and place, along with their aociated URL, create a dynamic lookup ervice of what i available nearby. 6.2 Localized The creen hot in Figure 2 how our localized program. It allow a uer to regiter with NearMe with a name and addre. After updating the databae with hi or her Wi-Fi ignature, a lit of nearby regitered uer appear. The uer can elect name from thi lit and end an to them. Thi would be ueful for nearly immediate requet like going out to lunch or aking for face-to-face help with a problem. Becaue we ort the lit of potential recipient by phyical ditance, picking the top N in the lit i equivalent to picking the N nearet people, up to NearMe inherent ditance approximation error. Since NearMe range reolution i in the ten of meter, it error are likely tolerable for thi application. In the future, proximity could be one of a number of filter for recipient, optionally ued in addition to filter on recipient type (e.g. friend, colleague, upervior) and interet area. 6.3 Detecting Synchronou Uer Operation Another client we have implemented ue NearMe to aid in detecting ynchronou uer operation between mobile device for co-located collaboration. Stitching [17], ynchronou geture [14], and SyncTap [16] are all example uch technique. Stitching, for example, mut hare the creen coordinate, direction, and timing of pen troke with other nearby device to etablih when a pen troke pan the diplay of two device. Thi make it eay for uer to drag a file between two eparate penoperated wirele device, for example, a hown in Figure 3. A key problem in thi cla of ytem i to determine which device to conider a candidate for potential ynchronou uer operation [14]. SyncTap [16] propoe uing multicat to hare uer activity and timing with other device, but thi may needlely end information to a large number of irrelevant device that are too far apart to ever be intentionally aociated. Retricting communication to device that are truly

17 tranferred picture path taken by the pen Figure 3: NearMe i ued to find which device are in proximity o they can be aociated with a pen geture panning both creen. nearby reduce the potential for fale-poitive recognition of ynchronou uer operation (due to pure chance ynchronization of incidental operation on a large number of device) and alo may help to reduce power conumption requirement (by reducing wated computation and tranmiion of meage eeking to etablih ynchronization with non-proximal device). NearMe olve thee problem by providing a lit of nearby device for every device that eek aociation with other device. For our Stitching technique, we refer to thee a titchable device. Our Stitching ytem oftware only look for correlating pen troke within et of device that NearMe identifie a being within hort range proximity of one another. Stitchable device update their ignal trength with NearMe every 20 econd o that the et of titchable device at any one time i dynamic and dicoverable by any new client wihing to make itelf eligible for titching. While thi application conider aociation for any device within hort range proximity, it could be modified to conider only thoe device within ome phyical range baed on our ditance etimation. But even a implemented, NearMe reduce the lit of potentially aociable device from the whole world to jut thoe within the range of one acce point. 7 Concluion NearMe main feature i that it give lit of nearby people and place without computing their abolute location. Thi make it eaier to deploy than traditional locationbaed ytem. Even though it i unaware of abolute location, NearMe can till give abolute and relative ditance etimate for hort range proximity, and it can give travel time etimate for long range proximity. The databae grow a more people ue the client, which in turn increae the richne and range of people and place that can be found in proximity. The databae help protect the privacy of uer by anonymizing their data after a uer pecified time period, and it can protect itelf againt falified acce point ignature by verifying them againt what it ha already een. A thi work proceed, we would like to tet the feaibility of uing NearMe in a peer-to-peer fahion rather than depending on a central databae. For hort range

18 proximity, thi would be a imple matter of having peer exchange Wi-Fi ignature and then having the client evaluate our function for etimating eparation ditance. Another way to expand the cope of NearMe would be to incorporate other type of radio a location ignature, uch a Bluetooth, cell tower, and commercial broadcat of radio and TV. Reference 1. Hightower, J. and G. Borriello, Location Sytem for Ubiquitou Computing. Computer, (8): p Bahl, P. and V.N. Padmanabhan. RADAR: An In-Building RF-Baed Uer Location and Tracking Sytem. in INFOCOM Schilit, B.N., et al. Challenge: Ubiquitou Location-Aware Computing and the Place Lab Initiative. in The Firt ACM International Workhop on Wirele Mobile Application and Service on WLAN (WMASH 2003) San Diego, California, USA. 4. Schilit, B.N., N. Adam, and R. Want. Context-Aware Computing Application. in IEEE Workhop on Mobile Computing Sytem and Application Hightower, J., G. Borriello, and D. Fox, The Location Stack. 2003, Intel Reearch Seattle. p Cox, D., V. Kindratenko, and D. Pointer. IntelliBadge : Toward Providing Location-Aware Value- Added Service at Academic Conference. in UbiComp 2003: Ubiquitou Computing Seattle, WA, USA. 9. Dey, A.K., et al. The Conference Aitant: Combining Context-Awarene with Wearable Computing. in 3rd International Sympoium on Wearable Computer San Francico, California, USA. 10. Dahlberg, P., F. Ljungberg, and J. Sanneblad. Supporting Opportunitic Communication in Mobile Setting. in CHI 2000 Extended Abtract on Human Factor in Computing Sytem The Hague, The Netherland: ACM Pre. 11. Sumi, Y. and K. Mae. Digital Aitant for Supporting Conference Participant: An Attempt to Combine Mobile, Ubiquitou and Web Computing. in Ubicomp 2001: Ubiquitou Computing Atlanta, Georgia, USA: Springer. 12. Wooding, R., et al. Rapid Heterogeneou Ad Hoc Connection Etablihment: Accelerating Bluetooth Inquiry Uing IrDA. in Third Annual IEEE Wirele Communication and Networking Conference (WCNC '02) Orlando, Florida, USA. 13. Holmquit, L.E., et al. Smart-It Friend: A Technique for Uer to Eaily Etablih Connection between Smart Artefact. in Ubicomp 2001: Ubiquitou Computing Atlanta, Georgia, USA: Springer. 14. Hinckley, K. Synchronou Geture for Multiple Uer and Computer. in UIST'03 Sympoium on Uer Interface Software & Technology Leter, J., B. Hannaford, and G. Borriello. Are You With Me? Uing Accelerometer to Determine if Two Device are Carried by the Same Peron. in Pervaive Linz, Autria. 16. Rekimoto, J., Y. Ayatuka, and M. Kohno. SyncTap: An Interaction Technique for Mobile Networking. in Mobile HCI Hinckley, K., et al. Stitching: Pen Geture that Span Multiple Diplay. in ACM Advanced Viual Interface (AVI 2004) Pre, W.H., et al., Numerical Recipe in C. 1992, Cambridge: Cambridge Univerity Pre. 23. Krumm, J., G. Cermak, and E. Horvitz. RightSPOT: A Novel Sene of Location for a Smart Peronal Object. in UbiComp 2003: Ubiquitou Computing Seattle, WA: Springer. 24. Krumm, J. and E. Horvitz. LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strength. in Firt Annual International Conference on Mobile and Ubiquitou Sytem: Networking and Service (Mobiquitou 2004) Boton, MA. 25. Ladd, A.M., et al. Robotic-Baed Location Sening uing Wirele Ethernet. in International Conference on Mobile Computing and Networking Atlanta, GA: ACM Pre.