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PTunes: Runtime Parameter Adaptation for Low-power MAC Protocols Marco Zimmerling, Federico Ferrari, Luca Mottola *, Thiemo Voigt *, Lothar Thiele Computer.

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Presentation on theme: "PTunes: Runtime Parameter Adaptation for Low-power MAC Protocols Marco Zimmerling, Federico Ferrari, Luca Mottola *, Thiemo Voigt *, Lothar Thiele Computer."— Presentation transcript:

1 pTunes: Runtime Parameter Adaptation for Low-power MAC Protocols Marco Zimmerling, Federico Ferrari, Luca Mottola *, Thiemo Voigt *, Lothar Thiele Computer Engineering and Networks Lab, ETH Zurich * Swedish Institute of Computer Science (SICS) TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A A A A

2 2 Configuring a MAC Protocol is Not Easy Application Requirements End-to-end Reliability End-to-end Reliability End-to-end Latency End-to-end Latency Network Lifetime Network Lifetime

3 3 Configuring a MAC Protocol is Not Easy Application Requirements Low-power MAC protocol OFF time End-to-end Reliability End-to-end Reliability End-to-end Latency End-to-end Latency Network Lifetime Network Lifetime ?

4 4 A Real-world Example Adaptive Control Application TinyOS LPL Ceriotti et al., IPSN’ ms ? 500 ms 100 ms Final LPL wake-up interval Current practice: Experience Field trials Over-provision Current practice: Experience Field trials Over-provision

5 5 A Real-world Example Adaptive Control Application TinyOS LPL Ceriotti et al., IPSN’ ms ? 500 ms 100 ms Final LPL wake-up interval Current practice: Experience Field trials Over-provision Current practice: Experience Field trials Over-provision Requires expert knowledge Deployment-specific Time-consuming Sub-optimal performance Requires expert knowledge Deployment-specific Time-consuming Sub-optimal performance

6 6 Adapting a MAC Protocol is Even Harder 100 PRR (%) 0 t Sampling rate (1/min) 0 t 10 Need to adapt at runtime to changes in Topology (e.g., node failures, disconnects) Wireless link quality (e.g., interference) Traffic load (e.g., varying sampling rate) Need to adapt at runtime to changes in Topology (e.g., node failures, disconnects) Wireless link quality (e.g., interference) Traffic load (e.g., varying sampling rate)

7 7 pTunes in a Nutshell Sensor NetworkBase Station Application Requirements Application Requirements Network State MAC Parameters Network-wide Performance Model Optimization Trigger Solver starts used by

8 8 1.pTunes framework for runtime adaptability of existing low-power MAC protocols 2.Flexible modeling approach 3.Efficient runtime support to “close the loop” Contributions

9 9 pTunes in a Nutshell Sensor NetworkBase Station Network State MAC Parameters Network-wide Performance Model Optimization Trigger Solver starts used by Application Requirements Application Requirements

10 10 pTunes targets data collection scenarios – Tree routing – Low-power MAC Example requirements specification Application Requirements Network lifetime End-to-end reliability greater than 95 % End-to-end latency below 1 second Subject to: Maximize:

11 11 pTunes targets data collection scenarios – Tree routing – Low-power MAC Example requirements specification Application Requirements Network lifetime End-to-end reliability greater than 95 % End-to-end latency below 1 second Subject to: Maximize: pTunes determines at runtime MAC parameters whose performance meets the requirements pTunes determines at runtime MAC parameters whose performance meets the requirements

12 12 pTunes in a Nutshell Sensor NetworkBase Station Network State MAC Parameters Network-wide Performance Model Optimization Trigger Solver starts used by Application Requirements Application Requirements

13 13 Network-wide Performance Model Network-wide Performance Model MAC parameters Tree topology Link quality Traffic load Network lifetime End-to-end reliability End-to-end latency A B A B

14 14 Network-wide Performance Model Network-wide Performance Model MAC parameters Network lifetime End-to-end reliability End-to-end latency Using the model, pTunes can predict how changes in the MAC parameters affect performance Tree topology Link quality Traffic load A B

15 15 Network-wide Performance Model Network-wide Performance Model MAC parameters Network lifetime End-to-end reliability End-to-end latency Using the model, pTunes can predict how changes in the MAC parameters affect performance A B Tree topology Link quality Traffic load

16 16 Layered Modeling Approach Application-level Protocol-independent Protocol-specific Sender-initiated: X-MACReceiver-initiated: LPP S R S R t tt t

17 17 Layered Modeling Approach Application-level Protocol-independent Protocol-specific Sender-initiated: X-MACReceiver-initiated: LPP Only the lowest layer must be changed to adapt the model in pTunes to a given MAC protocol Only the lowest layer must be changed to adapt the model in pTunes to a given MAC protocol S R S R t tt t

18 18 Layering in Action: X-MAC End-to-end Reliability Application-level (source-sink paths) Protocol-independent (child-parent link) X-MAC-specific (single transmission) OFF time ON time max. no. of retransmission

19 19 pTunes in a Nutshell Sensor NetworkBase Station Network State MAC Parameters Network-wide Performance Model Optimization Trigger Solver starts used by Application Requirements Application Requirements

20 20 Piggybacking introduces a dependence on the rate and the reliability of application traffic Running a dissemination protocol concurrently may degrade the reliability of application traffic Communication Support to Close the Loop Need to decouple network state collection and MAC parameter dissemination from application data traffic

21 21 The Need for Consistency B A C B A C A reports B and resulting in a loop that never existed for real B A C B reports A as parent, Need to collect consistent snapshots of network state from all nodes Need to collect consistent snapshots of network state from all nodes

22 22 Closing the Loop: Our Solution t Period Application operation Collect network state snapshots Disseminate new MAC parameters Phases consist of non-overlapping slots, one for each node Each slot corresponds to a distinct Glossy network flood Phases consist of non-overlapping slots, one for each node Each slot corresponds to a distinct Glossy network flood

23 23 Temporal decoupling from application Timeliness – Fast collection and dissemination Consistency – Snapshots taken at all nodes at the same time – Simultaneous transition to new MAC parameters Energy efficiency (44-node TelosB testbed) Our Solution Achieves PeriodExcess Radio Duty Cycle 1 minute0.35 % 5 minutes0.07 %

24 24 Implementation – Sensor nodes: Contiki, Rime stack, X-MAC, LPP – Base station: Java, ECLiPSe Testbed Evaluation: Setup 44-node TelosB testbed ― Channel 26, max. transmit power Base Station

25 25 Metrics – Projected network lifetime: measured in software and computed based on V batteries – End-to-end reliability: packet sequence numbers – End-to-end latency: packet time stamps Requirements specification Compare to static MAC parameters determined using pTunes and extensive experiments Testbed Evaluation: Methodology Network lifetime End-to-end reliability greater than 95 % End-to-end latency below 1 second Subject to: Maximize:

26 26 1.Our X-MAC and LPP models are very accurate Testbed Evaluation: Overview

27 27 1.Our X-MAC and LPP models are very accurate 2.pTunes provides higher bandwidth against increasing traffic and prevents queue overflows Testbed Evaluation: Overview

28 28 1.Our X-MAC and LPP models are very accurate 2.pTunes provides higher bandwidth against increasing traffic and prevents queue overflows 3.pTunes achieves up to three-fold lifetime gains Testbed Evaluation: Overview

29 29 1.Our X-MAC and LPP models are very accurate 2.pTunes provides higher bandwidth against increasing traffic and prevents queue overflows 3.pTunes achieves up to three-fold lifetime gains 4.Adaptation to traffic fluctuations 5.Adaptation to changes in link quality 6.Interaction with routing Testbed Evaluation: Overview

30 30 Adaptation to Traffic Fluctuations End-to-end reliability [%] greater than 95 % End-to-end latency [sec] below 1 second Network lifetime [days] X-MAC OFF time [msec]

31 31 Adaptation to Traffic Fluctuations End-to-end reliability [%] greater than 95 % End-to-end latency [sec] below 1 second Network lifetime [days] X-MAC OFF time [msec] Static 1

32 32 Adaptation to Traffic Fluctuations End-to-end reliability [%] greater than 95 % End-to-end latency [sec] below 1 second Network lifetime [days] X-MAC OFF time [msec] Static 1 Static 2

33 33 Adaptation to Traffic Fluctuations End-to-end reliability [%] greater than 95 % End-to-end latency [sec] below 1 second Network lifetime [days] X-MAC OFF time [msec] Static 1 Static 2 pTunes

34 34 Adaptation to Traffic Fluctuations End-to-end reliability [%] greater than 95 % End-to-end latency [sec] below 1 second Network lifetime [days] X-MAC OFF time [msec] pTunes satisfies end-to-end requirements at high traffic while extending network lifetime at low traffic Static 1 Static 2 pTunes

35 35 Adaptation to Changes in Link Quality Base Station Interferer jams using modulated carrier: 1 ms ON / 10 ms OFF

36 36 Adaptation to Changes in Link Quality End-to-end reliability [%] greater than 95 %

37 37 Adaptation to Changes in Link Quality End-to-end reliability [%] greater than 95 % Static 1

38 38 Adaptation to Changes in Link Quality End-to-end reliability [%] greater than 95 % Static 1 pTunes

39 39 Adaptation to Changes in Link Quality End-to-end reliability [%] greater than 95 % pTunes reduces packet loss by 80 % during periods of controlled wireless interference Static 1 pTunes

40 40 Interaction with Routing Base Station Turn off 8 nodes × within the sink’s neighborhood × × × × × × × ×

41 41 Interaction with Routing End-to-end reliability [%] greater than 95 % Total number of parent switches

42 42 Interaction with Routing End-to-end reliability [%] greater than 95 % Total number of parent switches Static 1

43 43 Interaction with Routing End-to-end reliability [%] greater than 95 % Total number of parent switches Static 1 pTunes

44 44 Interaction with Routing End-to-end reliability [%] greater than 95 % Total number of parent switches pTunes helps the routing protocol quickly recover from node failures, thus reducing packet loss by 70 % Static 1 pTunes

45 45 Conclusions pTunes framework for runtime adaptability of existing low-power MAC protocols Flexible modeling approach Efficient system support to “close the loop” Testbed experiments demonstrate that – pTunes aids in meeting the requirements of real- world applications as the network state changes – pTunes eliminates the need for time-consuming, and yet error-prone, manual MAC configuration


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