Location Based

Services - SIMPLE

NZNOG 2006, VUW March 22-24, 2006

Jonathan WierengaPeter Komisarczuk

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?

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

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.

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

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

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

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

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

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

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 2000m2 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)

MUM 2005, © VUW, 2005

MUM 2005, © VUW, 2005

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.

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RSSI

At each marking position li, a set On = {o1

n, o2n.. , oj

n} is formed where oj

n is the RSSI value from AP aj in the nth scanning cycle.

MUM 2005, © VUW, 2005

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Signal Strength (dBm)

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RSSI DistributionsHorusIBM

Distributions are close to Gaussian

(they can be approximated using their mean and standard deviation)

MUM 2005, © VUW, 2005

Estimating a position• RF map is used to estimate a position at an unknown

location x. • An observation O={o1, o2,..,oj, ok } is made where oj is

an estimation from AP aj , with a max of k APs.• Bayes’ Theorem used to express probability of the

location being li , given O. This is expressed as:

• P(O|li) is calculated by:

which results in a set of possible locations ordered by probability.

MUM 2005, © VUW, 2005

Potential PitfallsShort term sampling does not model long term variances

A model based training scheme is used (smoothing and trailing functions)

Calculating P(O|li) 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 li 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.

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

MUM 2005, © VUW, 2005

The effect of calculation optimisations

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

MUM 2005, © VUW, 2005

The effect of number of Intervals and Samples in creating RSSI

distributions

MUM 2005, © VUW, 2005

Effect of smoothing functionRate of smoothing

MUM 2005, © VUW, 2005

Effect of adding trailing probabilities“Amount of trailing”

MUM 2005, © VUW, 2005

Inclusion of prior probability, and time averaging

Effect of considering prior probability

Effect of time averaging