Xiaofan Jiang, Chieh-Jan Mike Liang, Kaifei Chen, Ben Zhang, Jeff Hsu Jie Liu, Bin Cao, and Feng Zhao Microsoft Research Asia Neight
MOTIVATION PROXIMITY ZONE Empirical Definition EVALUATION OF EXISTING TECHNOLOGIES LIVESYNERGY PLATFORM EVALUATION OF LIVESYNERGY APPLICATION DEPLOYMENT CONCLUSIONS Outline
To make applications intuitive to human users, the discovered objects in the environment must be within the personal interaction sphere Computer automatically wake up Refrigerator change its user interface Many typical low power communication technologies, (Bluetooth, ZigBee) have difficulties maintaining robust communication zones Motivation
propose methodologies and systematically compare the proximity zones created by various wireless technologies(BLE, ZigBee, and RFID reader) Design, Implement, and Evaluate a magnetic- induction based wireless proximity sensing platform Deploying LiveSynergy in an real-world application Contributions
Boundary sharpness: boundary of proximity zone should be binary Boundary consistency: detection should be consistent over time PROXIMITY ZONE
Obstacle penetration: Beaconing node and listening node can be mobile and against obstructions Additional metrics: 1. Range and geometric shape of zones 2. Beaconing frequency achievable 3. Power consumption 4. Form-Factor of the mobile tag 5. Cost of overall system PROXIMITY ZONE
Classification of Points
Classification of Zones
Three proximity zones
Questions? Proximity Zones
Use support vector machines (SVM) as the classifier seeks maximum-margin hyperplane to separate two classes w and b are the parameters to define the hyperplane to separate the two classes. Classifier
Two user-definable parameters: Error tolerance: - Smooth boundary vs. non-smooth boundary -Tradeoff between training loss and regularization -Cost parameter C Strictness: -Expect the white zone and the black zone contain no grey points -Related to error tolerance but non-symmetry Classifier
Cost parameter C: the cost of false positive C’: the cost of false negative C’ Strictness parameter: Classifier
Kernel Trick
Size: Size of the white and grey zone, which can be computed numerically based on the boundaries. Boundary sharpness: Fitness: How well the zone boundaries fit the data, or a confidence measure of the proximity zone classification. Matrix
Questions? Classifier
Hardware setup: TI CC2540 BLE dev boards (transmitting on 2.4 GHz at 0 dBm), A pair of TelosB motes with compliant TI CC24240 radio(transmitting on 2.4 GHz at 0 dBm) A Impinj Speedway R1000 RFID reader (transmitting on 902 MHz at 8 dBm) Boundary Sharpness and Consistency
Human Obstacle Penetration
Signal propagation and geometry: RFID antennas usually have a radiation angle less than 180 degrees Form Factor and Costs: RFID can produce a more consistent and smaller grey zone and BLE have advantages in both form factor and costs. Additional Metrics
Questions? Evaluation
Pulse Transmitter: (use AC power) Four primary hardware microcontroller (MCU) and radio magnetic transmitter tuned at 125kHz Energy metering mechanical relay for actuation. LIVESYNERGY PLATFORM
Link Receiver: ( battery-powered) Three primary hardware 9.2cm ×5.8cm × 2.3cm enclosure MCU and radio 3D magnetic coil wake up chip LIVESYNERGY PLATFORM
Boundary Sharpness and Consistency
human body has very little impact on the MI signal propagation Body orientation vs. distance
Geometry: two dimensions extends to all directions, covering 360◦ Range: maximum range (i.e., radius) is around 5m Additional Metrics
APPLICATION DEPLOYMENT Diners enter the cafeteria from the entrance at the lower left corner at different times
Each diner takes a different route and visits various food counters on the way Recorded a video as the customers walk around the cafeteria purchasing food. - Use video timestamps Experment
Result
Values: 1.Propose methodologies and systematically compare the proximity zones 2.Deploying LiveSynergy in an real-world application Future? 1.MI still can implement in mobile phone… Summary