### Design and Evaluation of a Wireless Magnetic

```Xiaofan Jiang, Chieh-Jan Mike Liang, Kaifei Chen, Ben
Zhang, Jeff Hsu
Jie Liu, Bin Cao, and Feng Zhao
Microsoft Research Asia
20120730-Neight
Outline
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MOTIVATION
PROXIMITY ZONE
Empirical Deﬁnition
EVALUATION OF EXISTING TECHNOLOGIES
LIVESYNERGY PLATFORM
EVALUATION OF LIVESYNERGY
APPLICATION DEPLOYMENT
CONCLUSIONS
Motivation
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 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
Contributions
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 propose methodologies and systematically compare
the proximity zones created by various wireless
 Design, Implement, and Evaluate a magneticinduction based wireless proximity sensing platform
 Deploying LiveSynergy in an real-world application
PROXIMITY ZONE
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 Boundary sharpness:
boundary of proximity zone should be binary
 Boundary consistency:
detection should be consistent over time
PROXIMITY ZONE
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 Obstacle penetration:
Beaconing node and listening node can be mobile
and against obstructions
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
Classiﬁcation of Points
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 Broadcasts at ﬁxed frequency f (α packets ∈ (, ′) )
 P = a point in space at a distance of ( ,  ,  ) from
the beacon
Classiﬁcation of Zones
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 white/grey boundary:
{P | Color(P, t, t’) = white}
{P | Color(P, t, t’) = grey}
> 0 if x∈  ,  ′ < 0 if x’∈

= 0 represents the decision boundary

 grey/black boundary:
> 0 if x∈  ,  ′ < 0 if x’∈

Three proximity zones
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Proximity Zones
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Questions?
Classiﬁer
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 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
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 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
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• Cost parameter
C: the cost of false positive
C’: the cost of false negative C’
• Strictness parameter:
Kernel Trick
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 RBF kernel as the kernel function
 Classifier:
   is the feature mapping function for RBF kernel
Matrix
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 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.
Classiﬁer
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Questions?
Boundary Sharpness
and Consistency
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 Hardware setup:
• TI CC2540 BLE dev boards (transmitting on 2.4 GHz
at 0 dBm),
• A pair of TelosB motes with 802.15.4-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
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 Parameters:
• packet reception data is collected over a period of
200 seconds
• WPRR using a windows size of 3 seconds and  = 0
• Strictness parameter = 0.99
 Results:
Boundary Sharpness
and Consistency
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Human Obstacle
Penetration
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 The user carries the receiver in the right pants pocket
- calculate PRR from 500 packets as the user changes
the body orientation by 90° each round at each distance
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 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
802.15.4 and BLE have advantages in both form factor
and costs.
Evaluation
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Questions?
LIVESYNERGY
PLATFORM
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 Pulse Transmitter: (use AC power)
Four primary hardware
 magnetic transmitter tuned at 125kHz
 Energy metering
 mechanical relay for actuation.
LIVESYNERGY
PLATFORM
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Three primary hardware
9.2cm ×5.8cm × 2.3cm enclosure
• 3D magnetic coil
• wake up chip
Boundary Sharpness
and Consistency
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Body orientation vs. distance
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 human body has very little impact on the MI signal
propagation
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 Geometry:
two dimensions extends to all directions, covering 360◦
 Range:
maximum range (i.e., radius) is around 5m
APPLICATION DEPLOYMENT
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Diners enter the cafeteria from the entrance at the lower left corner at different times
Experment
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 Each diner takes a different route and visits various
food counters on the way
 Recorded a video as the customers walk around the
- Use video timestamps
Result
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Summary
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 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…
```