IOT and Indoor Localization

Dr. David Chieng Wireless Innovation Lab MIMOS, Berhad Content • IoT • Location, the missing context? • Motivations for getting indoor • Wireless indoor positioning techniques • Deployment approaches • MIMOS Indoor Location Platform • Research challenges & potential solutions • Conclusions

2 ASEAN RISE 2016, Hanoi IoT

• Billions of devices  around us • Billions worth of market opportunities?

Wireless Access Operations Applications/ Sensor Networks Management Services Networks

Markets and Markets, Nov 2014

• Smart home, smart office, smart health, smart manufacturing, smart retail, etc  Indoor

3 ASEAN RISE 2016, Hanoi Location, the Missing Context?

• Intelligent = Context-aware • 5 elements of context: Who, What, Why, When and WHERE • Typical context-aware IoT applications. E.g. – Play my favourite music(what) when I enter my(who) bedroom (where) – Call nearest(where) person(who), when home alarm(what) triggered – When did Johnny(who) reach/left school(where)? • In indoor environment, the “where” is largely missing – 80% people are indoor, 80% of the time….

4 ASEAN RISE 2016, Hanoi What can Location Info offer for IoT? • With location awareness, a more meaningful interactions between human, things, events and location can take place • Semantic positioning – beyond geo spatial info. Deriving user’s position & action through IoT sensing • Such a rich set of contextual info can be translated to a wide range of innovative location-based applications: – Trigger services based on what user is doing? – Advertise based on user’s state?

5 ASEAN RISE 2016, Hanoi Motivations for Getting Indoor

• Buildings getting higher, shopping malls getting bigger • “ABI Research forecasted that total indoor location revenues will reach US$10 billion in 2020, driven primarily by BLE Beacons and advertising”, May 2015. • Close to 50 shopping malls in Valley alone and around 10 more to be added by end of this year. • Stiff competition implies the need to differentiate

6 ASEAN RISE 2016, Hanoi Shopping Malls in Klang Valley 1 24 2 25 3 Sogo 26 4 Suria KLCC 27 5 28 Citta Mall 6 Intermark 29 Center Point 7 Avenue K 30 IOI Mall () 8 31 IOI City Mall () 9 32 Alamanda (Putrajaya) 10 33 Tropicana City Mall 11 Low YatAt Plaza least 10 more34 Ikano malls (Power Station) to be 12 35 The Curve (eCurve) 13 36 Publika 14 opened in37 Setia 2016 City Mall 15 Leisure Mall 38 Paradigm Mall 16 Sentral Mall (Cheras) 39 Mines Resort City 17 40 (Klang) 18 41 19 Quill Mall 42 AEON Bukit Tinggi Shopping Centre 20 Nu Sentral 43 One City Mall 21 44 Gateway (KLIA2) 22 Festival Mall (Setapak) 45 Mitsu Outlet 7 23 Shopping Center ASEAN RISE 2016, Hanoi Wireless Positioning Techniques • Trilateration (TOA, TDOA, RSSI strength) • Triangulation (angle-based) • Fingerprinting (pattern-based) – Zero or minimal infra cost – Fast setup – Device availability  smartphones – Suited for Indoor

8 ASEAN RISE 2016, Hanoi Deployment Approaches

• Two main approaches: 1. Infrastructure dependent a) Green field - need large scale deployment of devices (WiFi/BLE/Femto/Light/Sound) b) Brown field – only require to install an app in smart phone (relying on EXISTING APs or BLEs). 2. Infrastructure less – based on built-in sensors such as magnetometer, gyroscope, accelerometer, etc.

9 ASEAN RISE 2016, Hanoi MIMOS Indoor Location Platform • Use existing WiFi or BLE signals • Smartphone-based (fingerprinting) • Simple and intuitive setup process • Modified Bayesian estimation • Accuracy within ~ 5 to 10m

10 ASEAN RISE 2016, Hanoi Mi-Loc: Software Architecture

User Applications

APIs

11 ASEAN RISE 2016, Hanoi Potential Services

Panic Button

12 ASEAN RISE 2016, Hanoi Pilot Trial in IOI City Mall

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ASEAN RISE 2016, Hanoi Research Challenges & Solutions • Within fingerprinting approach: – Device heterogeneity. Potential solutions: • Relative signals • Pattern-based – Dynamic wireless environment. Potential solutions: • Crowdsensing/data collection • Semi permanent calibrator • There is a need to have hybrid approaches - integrating with sensor- based tracking e.g. step sensor

14 ASEAN RISE 2016, Hanoi Device Heterogeneity study in real

environment (mall data)

Mean Error (m) Mean Error

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ASEAN RISE 2016, Hanoi Conclusions • Location is a critically missing context for IoT applications/services indoor. • With location info, a richer variety of new applications/services can be created with pervasive interactions with networked of things. • More interesting with sub meter granularity. • Lots of challenges but it is getting better

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ASEAN RISE 2016, Hanoi