1. Location Based Services - SIMPLE NZNOG 2006, VUW March 22-24, 2006 Jonathan Wierenga Peter Komisarczuk

2. MUM 2005, © VUW, 2005 Overview Context TIP – Tourist Information (service) Provider HIP – Health Information (service) Provider Aggregating of Location (service) Providers Location Determination Technology One LDT component –SIMPLE Components and Algorithms –SIMPLE Prototype Implementation –SIMPLE Results and Analysis Where to next?

3. MUM 2005, © VUW, 2005 Context Location Based Services (LBS) –New integrated wireless devices.... –Enhancing wireless services (new revenue streams) Value added multimedia delivery –Location Determination Technology (LDT) GPS Cellular Network Techniques (Cell ID, Angle Of Arrival etc) WLAN Bluetooth, RFID, IrDA

4. MUM 2005, © VUW, 2005 The TIP Tourist Information (service) Provider –Supporting the revenue stream from tourism –Where are you? (Privacy? Information not available outside the system!) –Now I am here what can I do? –What did my peers do? Recommendation service (friend of a friend) Reputation service (trust) Air Graffiti etc. –Value added multimedia content to enhance the museum, the arts festival etc.

5. MUM 2005, © VUW, 2005 TIP … Museum/heritage context –Virtual maps, guided tours, augmented artifact information –Imposes a number of constraints on LDTs High Accuracy, Low Computational Requirements, Pervasive Indoor Positioning (direction and 1m accuracy) etc. –Terminal equipment I/O? PDA/phone up to 640x480 IP Multimedia Subsystem delivery Through to voice only devices

6. MUM 2005, © VUW, 2005 The HIP Health Information (service) Provider –Consumers: Health sensor data monitoring/logging Emergency data/medical alerts Notification services Data filtering –Providers: Map information Location information Security (monitoring?) –Enhanced security required over TIP

7. MUM 2005, © VUW, 2005 Aggregating Location Provider Aggregation of location based services –Multiple TIP providers Overlapping or non overlapping –Analysis of positioning data from multiple sources Tracking for enhanced positioning GPS + WLAN + ????? –Federation: management, billing, integrated positioning, handover? –Filtering content push (preferences) –Privacy policy enforcement

8. MUM 2005, © VUW, 2005 What we’ve been doing… Some location determination WLAN experiments Basic TIP (museum like context) Beginning aggregation R&D The rest: –WLAN location determination (SIMPLE) experiments

9. MUM 2005, © VUW, 2005 WLAN Positioning Beacon Based Association Herecast Time Difference On Arrival Intel Precision Location RSSI Propagation Model Techniques TIX, Radar Empirical RSSI Techniques –Deterministic Methods Radar (k nearest neighbours) –Probabilistic Methods Horus

10. MUM 2005, © VUW, 2005 SIMPLE Overview “Simple Indoor Multi-floor Personal Location Engine” Based on probabilistic WLAN positioning Two phases –Offline – Builds a RF map of an environment –Online – Uses an observation and compares with the map to estimate a position Amalgamation of existing techniques from Horus and research at IBM Measurement of relative effectiveness of those techniques Extension to determine on which floor you are located

11. MUM 2005, © VUW, 2005 Probabilistic Positioning Horus –Ashok Agrawala, University of Maryland –Aims to overcome noisy characteristics of 802.11 channels –Privacy, decentralisation of positioning –Reducing computational requirements –.9m accuracy over 2000m 2 testbed IBM WLAN Positioning Engine –By Z. Xiang et al. IBM China Research Laboratory –Aims to reduce RF map building costs –Device tracking –2m accuracy (stationary) and 5m (moving device)

12. MUM 2005, © VUW, 2005

13. Building an RF Map An area is divided into a grid of marking positions, shown on the map. These should be in close proximity to APs. At each marking position l i, a set O n = {o 1 n, o 2 n.., o j n } is formed where o j n is the RSSI value from AP a j in the nth scanning cycle.

14. MUM 2005, © VUW, 2005 RSSI Distributions HorusIBM Distributions are close to Gaussian (they can be approximated using their mean and standard deviation)

15. MUM 2005, © VUW, 2005 Estimating a position RF map is used to estimate a position at an unknown location x. An observation O={o 1, o 2,..,o j, o k } is made where o j is an estimation from AP a j, with a max of k APs. Bayes’ Theorem used to express probability of the location being l i, given O. This is expressed as: P(O|l i ) is calculated by: which results in a set of possible locations ordered by probability.

16. MUM 2005, © VUW, 2005 Potential Pitfalls Short term sampling does not model long term variances A model based training scheme is used (smoothing and trailing functions) Calculating P(O|l i ) is computationally expensive Cluster locations into groups covered by APs. This greatly reduces no. of operations O may be affected by multipath fading or shadow fading Perturb observation if resulting location is unlikely. Choose a closer estimate A user may not be located at a marking position Weighted coordinate averaging treats location l i as a position in signal space whose weight is equal to its probability. A user may be standing still, but positions fluctuate (or moving too quickly to consider past history) Time averaging can help converge on a single location when recent estimates are similar.

17. MUM 2005, © VUW, 2005 Test Setup 2 test beds, 1 for testing map building parameters, another for location calculation optimisations Around 40 locations in each

18. MUM 2005, © VUW, 2005 The effect of calculation optimisations

19. MUM 2005, © VUW, 2005 Conclusions / Observations SIMPLE it is not… –Rich multipath environment –Cheap stock hardware? Noisy antenna Slow scanning operation –Scalability issues Modeling of the environment Device performance –Future solutions from Intel (TDOA) more likely to be best WLAN solution

20. MUM 2005, © VUW, 2005 The effect of number of Intervals and Samples in creating RSSI distributions

21. MUM 2005, © VUW, 2005 Effect of smoothing function Rate of smoothing

22. MUM 2005, © VUW, 2005 Effect of adding trailing probabilities “Amount of trailing”

23. MUM 2005, © VUW, 2005 Inclusion of prior probability, and time averaging Effect of considering prior probability Effect of time averaging