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Sift: A MAC Protocol for Event-Driven Wireless Sensor Networks Kyle Jamieson , Hari Balakrishnan , Y.C. Tay MIT Computer Science and Artificial Intelligence Laboratory Dept. of Computer Science, National University of Singapore

Sift: A MAC Protocol for Event- Driven Wireless Sensor Networks Kyle Jamieson †, Hari Balakrishnan †, Y.C. Tay ‡ † MIT Computer Science and Artificial

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Sift: A MAC Protocol for Event-Driven Wireless Sensor

NetworksKyle Jamieson†, Hari Balakrishnan†, Y.C. Tay‡

† MIT Computer Science and Artificial Intelligence Laboratory‡ Dept. of Computer Science, National University of Singapore

Types of Traffic in Sensor Networks

• Periodic traffic– Animal habitat

monitoring– Indoor

environment• Temperature• Room occupancy

– Medical monitoring

• Patient vital signs

• Event-driven traffic– Failure of

mechanical structures

• Water pipes• Airplane wings

– Medical emergencies

– Vehicle tracking

Airplane Wing Example

For critical systems, low latency is important!

Sift

• Focus of our work– Designing MAC protocol to handle

event-driven workload

• Challenges– Low-latency– Good throughput– Good fairness

Problems for Traditional MAC

1. Spatially-correlated contention: correlation between geographical neighbors’ traffic.

2. Bursty traffic: the number of senders can quickly change.

3. Suppression (counter-intuitively)

Suppression: often, not all sensing nodes need to report an event.

The Status Quo: CSMA

Time

Busy Medium

MAC Goal: only one node transmit at a time

• Basis of existing sensornet MAC layers– B-MAC, S-MAC

• Timeslot: opportunity for a node to begin transmitting

• Process repeats after each packet

The Status Quo: CSMA

• Pick a timeslot chosen uniformly in [0, CW]• Listen up to chosen slot

– Transmit if nobody else started transmitting– Wait if somebody else started transmitting

Time

Example: A Successful Transmission

• A and B happened to choose different slots– Node A chooses slot 4, hears nothing, transmits– Node B chooses slot 8, hears Node A, waits

Success: exactly one node in first non-vacant slot

Node A:

Node B:

Slot choice (slot #4)

Slot choice (slot #8)

Time

Example: A Collision

• A and B happened to choose slot 4– Both listen and hear nothing– Both transmit simultaneously

Collision: ≥ 2 nodes in first non-vacant slot

Node A:

Node B:

Slot choice (slot #4)

Slot choice (slot #4)

Time

High Contention Causes Collisions in CSMA

Uniform distribution “fills up,” quickly

Numerical simulation

Unacceptable collision rate above ~15 transmitting sensors

Solving the Problem of Collisions in CSMA

1. Create more slots– Conventional approach– Called “binary exponential backoff”

(BEB)

2. Change the way we pick slots– Sift takes this approach

Create More Slots:Binary Exponential Backoff (BEB)

• The basis for Ethernet, B-MAC, S-MAC, 802.11, MACAW, many other MAC layers

Acknowledgement?

Reduce CWDouble CWand resend

Yes No

Problems with BEB

• Takes time for every node to increase CW– Especially if traffic is spatially-correlated

and bursty

• Waste backoff slots if collisions cause CW to increase– Especially with suppression

BEB causes performance to suffer

Our Proposal: Sift

• Sift is a MAC protocol for sensor networks– Event-driven traffic– Low-latency requirements

• Sift’s Properties– Extremely simple– Offers up to 7-fold lower latency– Maintains good channel utilization

(throughput)

Sift: Changing the Distribution

• Keep number of slots the same (simple)

• Use an increasing non-uniform slot selection probability distribution

– Make collisions unlikely for large range of N

1. Reduce the chance of collisions• Penalty: one packet- or RTS-time (ms)

2. Reduce wastage of backoff slots• Penalty: one slot time (μs)

Balls and Bins Analogy

• Bin represents a backoff slot in the contention window– Bin height represents probability of picking

that slot

• Ball represents a single node’s slot choice

A

Bins represent backoff slots →

Why an Increasing Slot-Selection Function?

Bins represent backoff slots →

Nod

es c

hoos

ing

each

slo

t →

Sift’s Slot Selection Distribution

rCW

CW

rp

1

)1(

Optimal Non-Persistent CSMA Performance

With knowledge of number of nodes (IEEE J-SAC ’04)

Numerical simulation

Sift Approaches Optimal

Sift needs no knowledge of the number of nodes

Numerical simulation

Sift keeps success rate above this unacceptable range

Experimental Setup

• Simulation-based results (ns-2)• Compare 802.11 (BEB), Sift,

and 802.11/copy– 802.11/copy: send CW in each

packet, copy overheard CW

Event-driven Traffic Pattern

• Event-based traffic pattern– Single-hop to one base

station– N nodes sense and report an

event– R ≤ N reports are required

• If a node hears ≥ R reports then it suppresses its own event report E.g. N=4, R=3

BaseStation

Sift Outperforms When N is Large

Experimental evaluation: R=1,16

R=16

R=1

Sift Outperforms as R Increases

Experimental evaluation: N=128

Exploring Sift’s Performance Space

Experimental evaluation

Hidden Terminal Experiment Setup

• Separate 128 sensors into mutually-hidden clusters– Nodes in one cluster cannot hear nodes in another

• All nodes send to the base station– Result: hidden

terminal collisions at the base station

Base Station

Sift Performs Well with Hidden Terminals

Experimental evaluation: N=128, R=1

Sift Resilient to Jitter in Event Time

Experimental evaluation: N=128, R=64

Sift Improves Fairness

Eight nodes 64 nodes

Experimental evaluation

Trace-Driven Experimental Setup

• Simulated vehicle tracking

• Captured live video from a street scene– Extract motion events

from image analysis

• Event trace drives ns-2 simulation– 128 sensors laid out in a

grid over the scene– Sensors nearby each

event send traffic in response to movement

Sift Outperforms When R is Large

Trace-driven experimental evaluation

Related Work

• TDMA suffers in terms of latency– PTD (Mowafi et al.), TSMA (Chlamtac et al.)

• BEB-based protocols waste time in backoff– MACAW (Bharghavan et al.), S-MAC (Ye et

al.), FAMA (Garcia-Luna-Aceves et al.)• The HIPERLAN standard for wireless LANs

uses noise bursts of exponentially-distributed length

• Periodic-sleeping and other MAC protocols can work with Sift– S-MAC (Ye et al.), B-MAC (Polastre)

Sift is a composable MAC primitive

Conclusion

• Sift is a latency- (and sometimes throughput-) enhancing MAC for event-driven sensor networks

• Sift can be used as a building block in many MAC protocols

http://nms.csail.mit.edu/projects/sift