IODetector: A Generic Service for Indoor Outdoor DetectionPengfei Zhou†, Yuanqing Zheng†, Zhenjiang Li†, Mo Li†,

and Guobin Shen‡†Nanyang Technological University, Singapore

‡Microsoft Research Asia, Beijing, ChinaSensys 2012

Presenter: SY

Goal

• Define indoor/outdoor– High accuracy– Prompt response– Energy efficiency

How

• Mobile phone– Light sensor– Cellular RSSI– Magnetic field signal

• Detection Aggregation

Applications

• GPS management • Wifi scanning• Context aware computing• Activity recognition

Outline

• System design– Light detector– Cellular detector– Magnetism detector

• Aggregation• Evaluation• Case Study• Conclusion

System Overview

Light Sensor – Key Observation

• Reading from mobile phones (discrete)

Light Sensor – Key Observation

• Reading from TelosB

• Rotation in outdoor

Light Sensor – Detection ProcessQuery proximity sensor for readings

If > threshold s1, it is outdoor/semi-outdoor with high confidence

If it is daytime, it is indoor with high confidence

Else, not sure1. Check another threshold s2

1. If s2 < L < s1 indoor, CL = (s1-L)/s12. if L < s2 outdoor, CL = (s2-L)/s2

Cellular Signal – Key Observation

• Signal from current active cell tower– Handover problem– Corner effect

Cellular Signal – All Towers

Cellular Detector

• Use all visible cell towers

n number of visible cell towersN+(t) -> number of towers whose RSS increases more than vN-(t) -> number of towers whose RSS decreases more than vN0(t) -> number of towers whose RSS change between +/-v

Magnetic Detector

VarianceEmpirical threshold a = 18Compute variance over t = 10sConfidence level Cm = t/10

Pros And Cons• Fast and accurate• Indoor vs outdoor/semi-outdoor• Not always available

• Widely available• Indoor vs outdoor/semi-outdoor• Require sufficient # of towers

• Indoor/semi-outdoor vs outdoor• Available only when moving

Light Detector

Cellular Detector

Magnetism Detector

Aggregated IODetector

• Stateless IODetector

Find the highest confidence level

State Changes

• Current state is usually related to previous states

Stateful IODetector

• First order HMM

• Transition and emission probabilities are determined by training experiments

Transition probabilities

Aggregated IODetector

• Stateless– Estimate based on instant detection results– Not that stable

• Stateful – Infers current environment considering previous state– Robust to noises– Needs continuous detection

• Use accelerometer to trigger detection

Experiment Setup

• Mobile phones– Samsung Galaxy S2 i9100, HTC Desire S, and HTC

Sensation G14• Sensor nodes – TelosB– Connects to mobile phone (for light sensor)

• Environments

Sub-detector Performance

Aggregated IODetector

Energy Consumption

• Negligible

Case Study – Adaptive GPS

GPS Performance

IODetector-Augmented GPS

Energy Consumption

Conclusion

• Use available sensors on mobile phone• Lightweight – Low energy consumption

• Pretty good accuracy

• Arguments in case study is probably weak