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Location Systems for Ubiquitous Computing Part of slides from

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Location Sensing Techniques Triangulation – Lateration (using distance) – Angulation (using angles) Proximity – Contact – Contactless Scene analysis

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Triangulation Compute object locations using the properties of triangles (e.g. law of sines, Pythagorean theorem etc) Several combinations of distance and angle measurements would work Necessary conditions: – 2D: 3 non-collinear points are needed – 3D: 4 non-collinear points are needed

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Lateration

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Lateration Measurements Time-of-Flight Time-of-flight of the radio signal between transmitter and receiver – Measure time and then calculate the distance using the speed of the signal Example: sound waves – speed 344m/s at 21 o C – distance = time x speed – speed depends on environmental conditions – depends on accurate timings (clock synchronization)

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Lateration Measurements Time-of-Flight: Problems Often requires high time resolution (for accurate light or radio propagation measurements) – A light pulse which travels at 299,792,458m/s will cover 5m in 16.7ns – sec error leads to 200 miles error! Clock synchronization critical – Accurate synchronization between reference beacons and receivers – Beacons could use atomic clocks ($100k cost) – Could improve using extra measurements

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Lateration Measurements Signal attenuation Signal attenuation, i.e.. drop in the strength of a signal as it propagates in space – Measure the signal at the receiving end and then calculate the distance as the drop to what the signal was at the source Example: free space path loss model – FSPL(dB) = 20*Log F + 20*Log R F: frequency (hertz) and R: distance (m) Problem: – Attenuation varies based on environment due to obstacles, mobility of users, etc.. – Requires more sophisticated signal propagation models

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Lateration Measurements Example: Global Positioning System 27 satellite constellation More than 50 launched since 1978 Powered by solar energy Each carries a 4 rubidium atomic clocks – locally averaged to maintain accuracy – updated daily by US Air Force Ground control – Satellites are precisely synchronized with each other 400 M USD per year

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Lateration Measurements Example: Global Positioning System Algorithm – Satellites transmit their local time in the signal – Receivers compute their difference in time-of-arrival – Receivers estimate their position (longitude, latitude, elevation) using (at least) 4 satellites GPS receiver requires clock synchronization (w/ satellites) Accuracy is about 5 meters (20 meters until recently when random error was introduced) Differential GPS provides extra accuracy approx. 2 meters European solution: Galileo

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Angulation Location sensing in 2D requires – 2 angle measurements from known location – 1 distance measurement (between the 2 locations above) Example system: phased antenna array – Multiple antennas with known separation (i.e. distance) – Each measures time-of-flight of signal using the difference in times and the (known) geometry of the receiving array, we can calculate the required angle – If there are enough elements in the array and large separation, angulation can be performed accurately

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Proximity Physical contact – Pressure, touch sensors or capacitive detectors – Computer login – Credit card sale Within range of an access point – GSM, wi-fi, Bluetooth – RFID – Visual

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Scene Analysis Compares scenes to reference scenes – Image, electromagnetic spectrum Construct a signature of a position and apply pattern matching techniques with this signature Differential scene analysis – Tracks differences in scenes Issues – Observer needs access to the features of the environment against which it will compare its observed scenes – Changes of the environment that affects these features may require their reconstruction

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Location System Properties Physical position and symbolic location information Absolute versus relative locations Localized location computation capability Accuracy and Precision Scale Recognition capability Cost Limitations

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Physical position and symbolic location Location information can be – Physical (47º3917 N by 122 º1823 W) – Symbolic (in the kitchen, next to a mailbox) Symbolic location information can be derived by physical position with additional information. Using only symbolic location information can yield very coarse-grained physical positions

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Absolute versus relative Absolute location system – Shared reference grid for all objects – Can be transformed into a relative location Relative location system – Each object may have own frame of reference – Can transform into absolute location from relative location readings Must know absolute position of reference points

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Localized location computation Location computation can happen in: – The object being located (e.g., GPS receiver) Ensures privacy – The external infrastructure Lower computational and power demands on objects Many more applications possible

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Accuracy and precision Accuracy – Grain size (e.g. within 10 meters) Precision – Probability of achieving a particular accuracy Sensor Fusion – Tries to improve accuracy and precision using multiple location systems Adaptive Fidelity – Ability to adjust precision in response to dynamic events like partial failures.

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Scale Scale assessed by: – Coverage area per unit of infrastructure e.g 1 base station per 10 square meters – Number of objects the system can locate per unit of infrastructure per time interval e.g. 25 computations per room per second Larger scale achieved by increasing infrastructure (but cost matters..)

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Recognition Necessary for applications that take specific actions based on location of object – e.g. airport baggage handling system GUID (Globally Unique ID) – Used to provide recognition capability – Combined with other contextual information allows for different object interpretations in different settings. (e.g retrieving museum information in a particular language)

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Cost Time – Installation process length – System administration needs Space – Amount of installed infrastructure – Hardware size Capital – Price per mobile unit or infrastructure element – Support personnel salaries

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Place Lab21 Past Location Results Cost per User Cost per ft 2 MSR RADAR 2 m E911* 300 m GPS 3 m Active Bat.03 m MIT Cricket.2 m Motionstar.001 m Olivetti Active badge 4 m Place Lab

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Limitations Improper functionality in certain environments: – Signal strength indoors – Exceeding request limits – Frequency interference

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Practical Metropolitan-Scale Positioning for GSM Phones

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GSM vs. WiFi GSMs periodic beaconing (pilot signal) Benefits – GSM cell can span up to 35km (vs. WiFi < 500m) – GSM networks are stable – GSM bands are licensed, less prone to interference

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Measurement Platform 1 WiFi card 2 GPSs 3 Sony Erricson GM28 GSM modems 3 Audiovox SMT5600 phones (HTC Typhoon)

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Wardriving Seattle metropolitan area

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Positioning Algorithms Centroid family – Geometric center of all the cell towers seen in the measurement (e.g., arithmetic mean) – Cell tower location info was calculated using measured data set

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Positioning Algorithms Fingerprinting (used in RADAR) – Training phase (building a fingerprint table): for each location, collect signal strength samples from towers, and keep the average for each location – Positioning phase: Calculate the distance in signal strength space between the measured signal strength and the fingerprint DB Select k fingers with the smallest distance, and use arithmetic average as the estimated location x y RSSI (x, y, z) = (-20, -10, -15) (-15, -12, 18) ………… L1=avg(x, y, z) = (xx, yy, zz) x y RSSI (x, y, z) = (-21, -40, -18) (-16, -42, 12) ………… L2=avg(x, y, z) = (xx, yy, zz) RSSI: Received Signal Strength Indicator

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Positioning Algorithm Probabilistic approach using Bayes Rule – For a given location, signal strength follows a certain distribution Using only average value is less accurate.. – Want to find P(cur_loc|samples)?? Bayes rule: P(loc|sam) = P(sam|loc)*P(loc) / P(sam) Maximum likelihood approach: arg max P(loc|sam) x y RSSI (x, y, z) = (-20, -10, -15) (-15, -12, 18) ………… z RSSI from x PDF RSSI from y PDF RSSI from z PDF

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Recursive Bayesian Updating Markov assumption: z n is independent of z 1,...,z n-1 if we know x. The estimated location = x with the maximum probability P(x) denotes the probability that a user is in location X -- without any prior information, we assume uniform dist..

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Bayesian Filtering: Framework Given: – Stream of observations z and action data u: – Sensor model P(z|x) RSSI distribution for a given location – Action model P(x|u,x) Mobility framework – Prior probability of the system state P(x) Wanted: – Estimate of the state X of a dynamical system. – The posterior of the state is also called Belief: X t-1 XtXt X t+1 Z t-1 ZtZt Z t+1 u t-1 utut action observation Current location

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Bayesian Filtering z = observation u = action x = state Bayes Markov Total prob. Motion (Prediction phase) Sensor observation (Update phase)

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Bayesian Filtering Challenges – Integration may be difficult to solve (non-linear cases) – Maintaining full probability states is exhaustive (aka. curse of dimension) (in terms of memory and complexity) Various Bayesian Filtering representation methods – Kalman filters (Gaussian cases mostly) – Multi-hypothesis tracking – Grid-based representations (discretization) – Topological approaches – Particle filters (Monte Carlo sampling)

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Represent density by random samples Estimation of non-Gaussian, nonlinear processes Monte Carlo filter, Survival of the fittest, Condensation, Bootstrap filter, Particle filter Filtering: [Rubin, 88], [Gordon et al., 93], [Kitagawa 96] Computer vision: [Isard and Blake 96, 98] Dynamic Bayesian Networks: [Kanazawa et al., 95] Particle Filters

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MCL: Global Localization Robot has a map: 3 doors and their locations Robot can detect a door, but cant distinguish one another

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MCL: Sensor Update Posterior belief Re-sampling Sample Importance Sample Importance DOOR FOUND!!

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MCL: Robot Motion Sample Importance Sample Prediction phase: Update sample locations based on motion model

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MCL: Sensor Update Sample Importance DOOR FOUND!! Sample Importance Most likely to be here..

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MCL: Robot Motion Sample Importance Sample Prediction phase: Update sample locations based on motion model

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MCL based localization Sensor model P(z1, z2,.., zn|x) – Gaussian model (ubicomp..) – Measured model (mobisys) Motion model – Not specified in the paper (ubicomp..) – Random mobility??? (mobisys..)

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Results: effect of algorithms Low density (residential): Gaussian (MCL) performs best High density (downtown): Fingerprinting performs best Cell density is critical

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Results: effect of training data set size Trade-off between training data size and error – High density means better performance 70% dropping is still good..

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Other results Cross-provider GSM beacons (ATT, T-mobile, Cingular) significantly improve the performance (thanks to more samples) Cross-device performance varies a lot – Centroid: <10% – Gaussian: ~60%..

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Accuracy Characterization for Metropolitan-scale Wi-Fi Localization

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slide47 Riding the Wi-Fi wave Wi-Fi is everywhere now – No new infrastructure – Low cost – APs broadcast beacons – War drivers already build AP maps Calibrated using GPS Constantly updated Position using Wi-Fi – Indoor Wi-Fi positioning gives 2-3m accuracy – But requires high calibration overhead: 10+ hours per building What if we use war-driving maps for positioning? Manhattan (Courtesy of Wigle.net)

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slide48 Methodology Training phase – Collect AP beacons by war driving with Wi-Fi card + GPS – Each scan records A GPS coordinate List of Access Points – Covers one neighborhood in 1 hr (~1 km 2 ) – Build radio map from AP traces Positioning phase – Use radio map to position the user – Compare the estimated position w/ GPS

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slide49 Compare Accuracy of Different Algorithms Centroid – Estimate position as arithmetic mean of positions of all heard APs Fingerprinting – User hears APs with some signal strength signature – Match closest 3 signatures in the radio map – RADAR: compare using absolute signal strengths [Bahl00] – RANK: compare using relative ranking of signal strengths [Krumm03] Using cosine similarity measure Particle Filters – Probabilistic approximation algorithm for Bayes filter

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Baseline Results Algorithms matter less (except rank) AP density (horizontal/vertical) matters Urban 13~20m, Suburban ~40m

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Effect of APs per scan More APs/scan lower median error Rank does not work with 1 AP/scan

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