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Funneling-MAC: A Localized, Sink-Oriented MAC For Boosting Fidelity in Sensor Networks

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Funneling-MAC: A Localized, Sink-Oriented MAC For Boosting Fidelity in Sensor Networks Gahng-Seop Ahn, Emiliano Miluzzo, Andrew T. Campbell, Se Gi Hong, Francesca Cuomo – PowerPoint PPT presentation

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Title: Funneling-MAC: A Localized, Sink-Oriented MAC For Boosting Fidelity in Sensor Networks


1
Funneling-MAC A Localized, Sink-Oriented MAC
For Boosting Fidelity in Sensor Networks
Gahng-Seop Ahn, Emiliano Miluzzo, Andrew T.
Campbell, Se Gi Hong, Francesca Cuomo Columbia
University, Dartmouth College, University La
Sapienza SenSys 2006 Presenter Tsung-Han Lin
2
Outline
  • Background
  • Funneling effect
  • Protocol design
  • Evaluation
  • Conclusion

3
MAC in Sensor Nets
  • Collision avoidance
  • Increase throughput
  • Duty cycling
  • Energy efficiency
  • Latency

4
Contention based
  • S-MAC (Infocom 2002)
  • Coordinated radio on-off
  • Energy-latency trade-off
  • Looser time sync demand than TDMA
  • T-MAC (SenSys 2003)
  • Enable longer sleep period of S-MAC if no traffic
    is detected

5
Contention based
  • B-MAC (SenSys 2004)
  • Low power listening
  • Flexible check period can be adjusted based on
    app traffic pattern

6
Contention based
  • SCP-MAC (SenSys 2006)
  • Reduce preamble with synchronous check period and
    send time
  • Allow ultra-low duty cycle
  • X-MAC (SenSys 2006)
  • Shorter preamble

7
TDMA based
  • TRAMA (SenSys 2003)
  • Distributed algorithm
  • Maintains two-hop neighbors info
  • High message overhead
  • DESYNC (IPSN 2007)
  • Self organized protocol
  • Auto adjust slot size
  • Very low complexity, high channel utilization

8
TDMA/CSMA Hybrid
  • Z-MAC (SenSys 2005)
  • CSMA in low contention TDMA in high contention
  • DRAND distributed coloring algorithm to assign
    slots

9
Funneling Effect
  • The majority of packet loss occurs within the
    first few hops from the sink

10
Quantifying funneling effect
(1.5m)
  • Dartmouth testbed 45 Mica2 nodes,16 random
    sources
  • Delivery ratio 1 hop neighbor gt 80, 2 hop
    neighbor lt 20
  • MintRoute link quality based routing, data-ack
    approach
  • B-MAC

11
Traffic rate vs. Throughput
  • To understand the impact under different network
    load
  • 0.2 pps (light), 1 pps (medium), 4 pps(overload)

12
Impact of funneling effect
  • Overall loss rate 67 to 95
  • Hop 1 and 2 have high loss rates
  • 80- 90 of losses happen within the first 2 hops
    from the sink!
  • Still true for light loaded network

13
Any MAC resolves this?
  • Z-MAC probably
  • High contention TDMA
  • However, Z-MAC schedules the whole network, which
    is too costly
  • Funneling-MAC
  • The scheduling is localized to sink
  • Reacting dynamically to network condition

14
The idea
  • Hybrid TDMA/ CSMA scheme inside the intensity
    region
  • Pure CSMA - pure CSMA scheme outside the
    intensity region
  • Sink oriented TDMA scheduling
  • Maintenance of the intensity region dynamically
    operated by the sink

15
Protocol Design
  • On-demand beaconing
  • Sink-oriented scheduling
  • Dynamic-depth tuning
  • Meta-schedule advertisement

16
On demand beaconing
  • To decide the size of intensity region
  • To synchronize nodes in the region

17
On demand beaconing
  1. Sink broadcast beacon periodically

18
On demand beaconing
  1. Sink broadcast beacon periodically
  2. Nodes receive beacon as f-nodes, also synchronize
    the clock

19
On demand beaconing
  1. Sink broadcast beacon periodically
  2. Nodes receive beacon as f-nodes, also synchronize
    the clock
  3. Decide path head with data flow

Path heads
Path info is registered in the sink
20
On demand beaconing
  1. Sink broadcast beacon periodically
  2. Nodes receive beacon as f-nodes, also synchronize
    the clock
  3. Decide path head with data flow
  4. If sink can schedule more f-nodes, increase the
    transmission power

21
On demand beaconing
  1. Sink broadcast beacon periodically
  2. Nodes receive beacon as f-nodes, also synchronize
    the clock
  3. Decide path head with data flow
  4. If sink can schedule more f-nodes, increase the
    transmission power
  5. Repeat (2)

Determined by dynamic depth tuning
22
Sink-oriented scheduling
  • Assign slots based on path and traffic
  • Round robin for each path
  • Sink broadcasts the TDMA schedule to every f-node
  • Traffic measurement
  • pkts / scheduled frame
  • Moving average
  • Spatial reuse
  • The end of this path can share the same slot with
    the head of next path if they are 3-hop away

23
Sink-oriented scheduling
  • Path A 4-hop
  • Path B 4-hop
  • Path C 3-hop

4
1
2
5
Spatial reuse Path B longer than 3-hop, 1 slot
can be reused
8
3
6
9
10
4
7
24
Sink-oriented scheduling
4
1
2
5
8
3
6
9
10
4
7
25
Sink-oriented scheduling
  • If more traffic are presented

26
Framing
  • The schedule is carried with beacons
  • CSMA control pkts, unregistered event data
  • Synchronous LPL for f-nodes

27
Dynamic-depth tuning
  • Schedule more nodes, less loss?
  • Probability of collision decrease with hops
  • No
  • Amount of TDMA slots are limited over scheduling
    still leads to collisions
  • An optimal depth lies in between

28
Dynamic-depth tuning
  • Amax max number of slots that can be assigned
    given the TDMA capacity
  • A number of slots required to schedule
    path-heads traffic
  • if A lt Amax then sink increases beacon
    transmission power
  • if A gt Amax then sink decreases beacon
    transmission power

29
Meta-schedule advertisement
  • Reduce interference from possible interferer
  • Nodes within the intensity region but do not
    receive beacons due to radio reasons
  • Nodes close to the boundary of intensity region
  • Recover if any intermediate nodes do not receive
    beacon

30
Meta-schedule advertisement
  • Embed mini schedule in the data packet
  • Interferer knows when the CSMA slot starts
  • Beacon loss can recover from this
  • Only 4-byte
  • Superframe, TDMA frame, time left of current
    frame, superframe repititions
  • Only in the first packet each beacon interval

31
Evaluation
  • Depth tuning
  • Boundary node interference
  • Loss rate distribution
  • Multi-hop throughput
  • Energy tax and signaling overhead

32
Setup
  • Compared with B-MAC and Z-MAC
  • MintRoute routing

33
Impact of depth tuning
  • Traffic? optimal depth?
  • Dynamic depth tuning is in need

34
Impact of boundary node interference
  • Fixed power -7dBm
  • Variable power -6-8dBm
  • 8 nodes in the boundary area

35
Loss rate distribution
  • Reduce the loss rate in the first two hops

36
Throughput
  • All 44 nodes as sources with 5 pps (heavy load)
  • MintRoute take 20mins to establish paths
  • If no path, the data would go broadcast
  • Z-MACs performance degrades

37
Schedule drift
  • DRAND schedule may not be valid over time
  • 76.7 nodes have change in neighbor tables.
  • The change is around 25-45
  • Performance fall back to B-MAC
  • Periodic DRAND has large overheads

38
Varying workload
  • Schedule drift
  • More slots to nodes closer to sink

MintRoute fail to setup routing path
39
Energy tax
  • The total amount of cost to deliver a bit per
    node
  • Control overhead
  • B-MAC no
  • Z-MAC local time sync packet
  • Funneling-MAC beacon pkt, schedule pkt, path
    info header, meta-schedule header

40
Signaling overhead
  • Comparable with Z-MAC

41
Conclusion
  • Mitigating funneling effect through scheduling in
    the intensity region, even under lightly loaded
    traffic.
  • Funneling-MAC outperforms Z-MAC and B-MAC in
    various network conditions

42
Reference
  • Some slides from Emiliano Miluzzos talk in
    SenSys 2006
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