Hybrid Indoor Positioning with Wi-Fi and Bluetooth: Architecture and Performance IEEE Mobile Data Management 2013 Artur Baniukevic†, Christian S. Jensen‡, Hua Lu† CS in Aalborg University†, CS in Aarhus University‡ Presenter: ByungHun Kim

Outline Introduction Background Improvement Techniques –Deployment of Bluetooth Hotspot –Enhanced Partition Switching –Architecture Options Evaluation 2 / 30

Introduction Location based services(LBS) in indoor spaces is of practical importance –Indoor navigation system can guide visitor –Location-aware boarding reminder service in an airport can remind passengers Each individual wireless technologies, such as WiFi, Bluetooth, IR and RFID, has limitations as a means for supporting indoor positioning. 3 / 30

Introduction Wi-Fi –Signals diffuse in space and have very large coverage –Require additional improvement technique of positioning accuracy RFID & Bluetooth –Only detect object within the quite limited detection range Infrared –Low data rate, strict directionality 4 / 30

Background Proposal is based on their previous suggestion [1] –Wi-Fi Based Positioning Fingerprinting –Bluetooth Based Positioning Proximity analysis [1] “Improving Wi-Fi Based Indoor Positioning Using Bluetooth Add-Ons”, MDM / 30

Background Fingerprinting (Wi-Fi Based Positioning) –Based on received radio signal strengths –Two phases exist (offline, online) Offline phase –Signal strengths of all detectable APs’s are collected(in a number of user-specified positions) –Pre-selected positions work as reference positions –A database of all collected fingerprints is created as a radio map 6 / 30

Background Fingerprinting (Wi-Fi Based Positioning) –Two phases exist (offline, online) Online phase –A user obtains the signal strengths from all detectable APs –Compare the current strengths with the pre-collected radio map –The reference location with the best match is returned as the user’s current location(NNSS method) 7 / 30

Background 8 / 30 User device’s fingerprint q = (33, 56, 59, …, 86, 86)

Background Proximity Analysis(Bluetooth based Positioning) –Corresponding position is returned as the user’s current position 9 / 30

Background 10 / 30

PERFORMANCE IMPROVEMENT TECHNIQUES 11 / 30 Deployment of Bluetooth Hotspot Enhanced Partition Switching Architecture Options

Deployment of Bluetooth Hotspot Deployment Motivation –The NNSS method is not effective in distinguishing resembling reference positions Resembling reference positions: Different reference positions which may share similar Wi-Fi fingerprints 12 / 30

13 / 30 Deployment of Bluetooth Hotspot 1 2 NNSS method A position with Minimum Euclidean Distance is returned Return 1 or 2 ? User device’s fingerprint q = (40, 40, 41, …, 80, 81)

Deployment of Bluetooth Hotspot Deployment Motivation  Bluetooth hotspots divide their Wi-Fi fingerprints into different radio map parts 14 / 30

Deployment of Bluetooth Hotspot Deployment Algorithms 15 / 30

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PERFORMANCE IMPROVEMENT TECHNIQUES 17 / 30 Deployment of Bluetooth Hotspot Enhanced Partition Switching Architecture Options

Previous Partition Switching 18 / 30 Select the two partitions adjacent to the most recently seen Bluetooth hotspot –Reducing the number of candidates 1 2 a b

Enhanced Partition Switching 19 / 30 Select one single partition More reduced candidates Naive approach: Single position estimate  single position estimate is not reliable –Limitation of Wi-Fi signal Noise, Fluctuation, random strengths 1 2 Estimated partition could be “2” with position “a” a b

Enhanced Partition Switching 20 / 30

Enhanced Partition Switching 21 / 30 Partition 1’s score = {(3+4+5)/5}·1 = 2.4 Partition 2’s score = {(1+2)/5}·2 = 1.2 Weighted method

PERFORMANCE IMPROVEMENT TECHNIQUES 22 / 30 Deployment of Bluetooth Hotspot Enhanced Partition Switching Architecture Options

Considerations –Energy efficiency –Coexistence of Wi-Fi and BT 23 / 30

EVALUATION 24 / 30

Indoor Environments 25 / 30 Indoor Environment AIndoor environment B Space size Total Wi-Fi APs at each floorMore than 30More than 60 One AP’s visible positionsAt most 10At most 20 # of Collected fingerprints 12 (about 3 meters apart) 23 (about 10 meters apart)

Effect of Bluetooth hotspot deployment 26 / 30 Manual deploymentAlgorithmic deployment 9.75(m)7.57(m) Error distance of each deployment method

Position accuracy 27 / 30 Pure Wi-FiHybrid-1Hybrid Error distance of each method

Summed mA for all setting 28 / 30 Power consumption Thick-client > Thin-client > Medium-client Thick-client Thin-client Medium-client Summed mA for all setting

Online positioning delay 29 / 30 network overhead and computation overhead

Number of visible Wi-Fi AP 30 / 30

Q&A 31 / 30