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Department of Computer Science and Engineering UESTC 1 RxLayer: Adaptive Retransmission Layer for Low Power Wireless Daibo Liu 1, Zhichao Cao 2, Jiliang.

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Presentation on theme: "Department of Computer Science and Engineering UESTC 1 RxLayer: Adaptive Retransmission Layer for Low Power Wireless Daibo Liu 1, Zhichao Cao 2, Jiliang."— Presentation transcript:

1 Department of Computer Science and Engineering UESTC 1 RxLayer: Adaptive Retransmission Layer for Low Power Wireless Daibo Liu 1, Zhichao Cao 2, Jiliang Wang 2 Mengshu Hou 1 and Yunjun Li 1 1 University of Electronic Science and Technology of China 2 Tsinghua University

2 Department of Computer Science and Engineering UESTC 2 Wireless data transmission Data ACK Data Packet loss Countermeasures: –Retransmission –Link quality update –Change next-hop Data Retransmission & update link quality Data ACK Change next-hop Communication over unreliable wireless links

3 Department of Computer Science and Engineering UESTC 3 Link burstiness However.. Receivers' PRR The change of RSSI at receiver Appearance of Obstacles Many factors have effect on link burstiness Link burstiness brings about consecutive retransmission failures

4 Department of Computer Science and Engineering UESTC 4 Link correlation However.. Three receivers' PRR Different concurrent interference R1 R2 R3 Link correlation brings about ill-advised next-hop change, e.g., replacing R1 with R2. Interference(dBm)

5 Department of Computer Science and Engineering UESTC 5 Serious situation Complicated external circumstance aggravates link burstiness and correlation

6 Department of Computer Science and Engineering UESTC 6 I ll -advised retransmission Consequence.. Quick decrease of ' link quality is relatively stable is not the optimal link now S is source node. A is S’ parent node. B, C, and D are S’ candidate next-hop.

7 Department of Computer Science and Engineering UESTC 7 I ll -advised retransmission S is source node. A is S’ parent node. B, C, and D are S’ candidate next hop node. Consecutive retransmission strategy is inefficient when link is severely degraded. It is ineffectual to change next-hop node only according to link quality. Consecutive retransmission strategy misleads link estimator in the presence of link burstiness and correlation. How to accurately perceive the link burstiness? How to select the optimal candidate receiver? Consequence..

8 Department of Computer Science and Engineering UESTC 8 Efficiency of retransmission Quantify the conditional probability of immediate retransmission –Conditional packet delivery functions (CPDF) CPDF(i) is the probability that the i th retransmission successes after i-1 consecutive failures. N i is the cumulative count that packets are retransmitted no less than i times.

9 Department of Computer Science and Engineering UESTC 9 Efficiency of retransmission CPDF quickly slips down to 0.2. Consecutive retransmission (CR) is inefficient. A interrupt point of CR is needed. Pause consecutive retransmissions Quantifying the conditional probability of immediate retransmission –Conditional packet delivery functions (CPDF)

10 Department of Computer Science and Engineering UESTC 10 Dynamic feature of CPDF OutdoorIndoor Two hours later Link CPDF –Different scenarios –Different time However.. Link burstiness is time-varying and spatial-varying. Online capturing link burstiness is needed.

11 Department of Computer Science and Engineering UESTC 11 Online model for link burstiness Solution Update link burstiness by moving average is the cumulated CPDF i. Value of α: is the updated CPDF i. Making a tradeoff between the adaptability to network dynamics and accuracy. Long-term trace data could learn an appropriate value.

12 Department of Computer Science and Engineering UESTC 12 Correlation of link pair High PRR and low P(0,6) indicating link 6 is not good when link 0 fails. Low PRR and high P(0,9) indicating link 9 is a good substitution for link 0 1 Source node 16 Receivers 1000 Packets P(i, j) is the probability that a packet transmission will success in link j while failed in link i. Correlation between link pair: P(i, j) A high quality link is not always an optimal candidate for a severely degraded link. The correlation between each pair of links should be captured with low overhead.

13 Department of Computer Science and Engineering UESTC 13 Capturing link correlation P(B,A)P(B,C) P(A,B) P(C,A) P(A,C) P(C,B) Bitmap and uniform broadcast sequence number(BSN) Bitmap and uniform broadcast sequence number(BSN) However, bitmap size is limited, e.g., 2 bytes. Solution

14 Department of Computer Science and Engineering UESTC 14 Online model for link correlation Solution Model for capturing link correlation: ω Update by using moving average : the probability, when S transmits, that a packet succeed on link S  j given that it failed on link S  i. : is the probability of packets failed on link S  i. is the accumulated correlation between link i and j. is the computed correlation using the latest BSN set.

15 Department of Computer Science and Engineering UESTC 15 Online model for link correlation Solution Correlation update Value of θ ω i is the correlation calculated by moving average by hearing the ith routing beacon. M i is the computed correlation using collected BSNs from 0 to i..

16 Department of Computer Science and Engineering UESTC 16 RxLayer: Decision maker rules Transmission model Link burstiness model Link correlation model 1. Transmission failure/success 2. Immediate retransmission 3. Pause consecutive retransmissions 4. Change next-hop node Solution Exploit link burstiness and correlation Link burstiness model: update CPDF, pause consecutive retransmission. Link correlation model: select the optimal candidate receiver. Transmission model: transmit packet and report result to network layer.

17 Department of Computer Science and Engineering UESTC 17 RxLayer in protocol stack Integrate RxLayer into protocol stack Solution Beneath network communication layer; Above MAC layer; Connecting with link estimator.

18 Department of Computer Science and Engineering UESTC 18 Implementation: -Integrating with CTP built upon LPL in TinyOS 2.1.1 Goals - High energy efficiency - Improvement on forwarding delay, network reliability Scenarios - Indoor Testbed: 22 Telosb nodes - Outdoor Scenario: 30 Telosb nodes Evaluation

19 Department of Computer Science and Engineering UESTC 19 Network reliability IndoorOutdoor Indoor, the average PRR CTP+LPL+RxLayer with 1.53% improvement Than CTP+LPL Outdoor, the average PRR CTP+LPL+RxLayer with 7.82% improvement than CTP+LPL

20 Department of Computer Science and Engineering UESTC 20 Transmission efficiency IndoorOutdoor Indoor, the avg. tx count CTP+LPL+RxLayer with 24.7% improvement than CTP+LPL Outdoor, the avg. tx count CTP+LPL+RxLayer with 36.3% improvement than CTP+LPL

21 Department of Computer Science and Engineering UESTC 21 Energy consumption IndoorOutdoor Indoor, the avg. radio duty cycle Using RxLayer, radio duty cycle is reduced by about 3.5%. Outdoor, the avg. duty cycle Using RxLayer, duty cycle is decreased from 19.3% to 10.4%

22 Department of Computer Science and Engineering UESTC 22 - Online link burstiness model - Online link correlation model Key design - Indoor and outdoor experiments - Improvements on network efficiency Evaluation - Large testbed - Dyanmic forwarding Future works Conclusions RxLayer is a ready-to-use module for existing protocol stack

23 Department of Computer Science and Engineering UESTC 23 Thank you! Q&A Q&A

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