2012 Inaugural Retreat
MIT Center for Wireless Networks and Mobile Computing
Efficient and Reliable Ultra Low Power RFID Networks
Jue Wang, Haitham Hassanieh and Dina Katabi
Reader shines an RF signal on nearby RFIDs
Tag reflects the reader’s signal using ON‐OFF keying
Motivation
• Wireless protocols require power and computation
• RFIDs are very flaky
• No power source
• Ultra‐low cost not much circuitry
• RFIDs can’t perform typical functions like carrier sense or rate adaptation
Machine‐Generated Data
RFID will be a major source of such traffic
• “number of RFID tags sold globally is projected to rise from 12 million in 2011 to 209 billion in 2021.”
– McKinsey Big Data Report 2011
• In Oil & Gas – about 30% annual growth rate
• In Healthcare – $1.3B revenue annually
Are Our Wireless Protocols Ready?
Background
Backscatter
Communication
Node Identification Problem
Challenge: RFIDs cannot hear each other
Collisions
Applications:
• Inventory management
• Shopping cart
Each object has an IDReader learns IDs of nearby objects
Data Communication Problem
Data communication in RFID networks performs poorly because it lacks rate adaptation
RFIDs always send 1 bit/symbol
Can’t exploit good channels to send more bits Inefficiency
Can’t reduce rate in bad channels Unreliability
ID = 1 ...ID = 2 ID = 4ID = 3 ID = 5 ID = NID = 6
System is represented by a vector
if node with ID = is in cart
Network‐Based Compressive Sensing
0
500
1000
1500
2000
4 8 12 16
Number of Tags
Our Design
Slotted Aloha
Number of Symbols to
Identify Nodes
5.5× reduction in symbols needed for identification
Reliability
Message Loss Rate
TDMA
CDMA
Our Design
0.57 bits/symbol
1.7bits/symbol
3.2bits/symbol
0%
10%
20%
30%
40%
50%
1 2 3Medium SNR(5dB 9dB)
High SNR(10dB 20dB)
Low SNR(0dB 4dB)
EfficiencyEvaluation
• Reader implementation on GNURadio USRP
• UMass Moo programmable RFIDs
Network‐Based Rate Adaptation
• Nodes transmit messages and collide
• Reader collects collisions until it can decode
• good channel decode from few collisions
• worse channel decode from more collisions
b1
b2
b3
⁞
bK
y1
y2
y3
y1 = h2 b2 + hK bK
⁞
y2 = h1 b1
y3 = h2 b2 + h3 b3 + hK bK
Collisions act as a distributed rateless code
Adapts bit rate to channel quality without feedback
Collisions act as a sparse random code
Quickly decode using a
Belief Propagation decoder
0 1 0 0 1 0 … 0
Node with ID = transmits
Collisions mix bits on the air
is distributed across all nodes!
is Sparse
Want the network to emulate acompressive sensing sender
Reader decodes using a
Compressive Sensing decoder Bits transmitted by different nodes
Received wireless symbols