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pTunes: Runtime Parameter Adaptation for Low-power MAC Protocols IPSN 2012 Marco Zimmerling, Federico Ferrari (ETH - Zurich), Luca Mottola, Thiemo Voigt (Swedish Institute of Computer Science), Lothar Thiele (ETH - Zurich) BEST PAPER RUNNER UP - IP TRACK NSLab study group 2012/09/10 Presented by: Yuting 1
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Outline Introduction System Model Protocol Specific Modeling - X-MAC and LPP Evaluation Conclusion 2
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Introduction Separate protocol-dependent from protocol-independent aspects A complement of existing MAC protocol – X-MAC, LPP, etc Runtime adaptation – traffic load, link quality, topology Multi-objective – reliability R, latency L, lifetime T Parameter optimization – according to the running MAC protocol Centralized – a base station running ECLiPSe integrates with the application Utilize Glossy (IPSN'11) on Contiki 3
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Note No need to rely on expert knowledge to find optimized operating parameters – Experience, rules of thumb: performance may not be what we want – Several field trials: time consuming, deployment- specific Separating adaptivity from MAC operation release the limitation of its applicability 4
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Outline Introduction System Model Protocol Specific Modeling - X-MAC and LPP Evaluation Conclusion 5
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Framework 6 Challenges: - Minimum disruption - Timeliness - Consistency - Energy Efficiency by ECLiPSe Both collecting network state and disseminating MAC parameters exploit Glossy c=[T on, T off, N]
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Modeling Framework 7 (traffic load) (link quality) (routing tree) X-MAC, LPP, etc
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Optimization Multi-objective optimization problem (MOP): max R, min L, max T – Pareto-optimal solutions: trade-off between (R,L,T) Epsilon-constraint method: treat all but one objective as constraints, ex: If either constraint is unsatisfiable because of bad network situation? -> depends on user, ex: just maximize R 8
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Term Definition N: set of all nodes excluding the sink M ⊆ N: set of source nodes L: set of communication links Pn ⊆ L: path originating at node n ∈ M includes all intermediate links that connect node n to the sink 9
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Application-level Metrics 10 LL L
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Protocol-independent Modeling 11 Note: here N is "the maximum number of rtx(retransmisstion) per packet", not the set of nodes excluding sink Note: N ftx,l depend on p s,l and N Note: Q is battery capacity (current*time) Note: 1. similar to packet generation rate, but this is packet transmission rate 2. will be used to deduce D tx,n and D rx,n in protocol-specific modeling
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Outline Introduction System Model Protocol Specific Modeling - X-MAC and LPP Evaluation Conclusion 12
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X-MAC: sender-initiated For broadcast: T m = 2*T on + T off 13 at most T m (= T on + T off ?)
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Protocol-specific Modeling MAC parameters c=[T on, T off, N] Reliability Latency Lifetime 14 : duration of a strobe iteration at the sender Tb: backoff before rtx : expected number of strobe iterations (attempts to rx)
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LPP: receiver-initiated For broadcast: T m = 2*T l + T off (It seems that they mistype T on as T m ) 15 at most T on
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Protocol-specific Modeling MAC parameters c=[T on, T off, N] Reliability Latency Lifetime 16 Tb: backoff before rtx : LPP duty cycle period ; {0,…,Trm}: random dist. for probe : wait time before rx a probe, pi: prob of rx i-th probe Ti: expected time to await i-th probe
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Outline Introduction System Model Protocol Specific Modeling - X-MAC and LPP Evaluation Conclusion 17
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Framework Evaluation 18 Collection period Tc: can range from a few tens of seconds to several minutes Glossy: ~5.2ms for a single flood duty cycles of state-of-art MAC is 3-7%, much higher than the overhead of pTunes Only 6 bytes: nodeID, parentID, Fn, ratio of successful and total number of handshake H s,l /H t,l (both of H are counted by a way similar to EWMA) A few tens of seconds -> slow! will be faster if they use dedicated solution technique
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Testbed 44 Tmote Sky nodes and a interferer Tc = 1min 19
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Static MAC Configuration Optimized for Different Situation 20
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Model Validation TimedTrigger: every 10 min Inter-packet interval (IPI) of all nodes: from 300 s to 180, 60, 30, 20, 10, 5, and 2 s δ(Mi ) = m(Mi ) − e(Mi ) Results is very accurate: 21
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Bandwidth and Queuing 22
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Lifetime Gain TimedTrigger: every 10 min maximizes T subject to R ≥ 95 % Increase the IPI from 10 s to 20 s, 30 s, 60 s, 3 min, 5 min, and 20 min 1 st exp: use pTunes only in the very beginning 2 nd exp: let pTunes run throughout the exp Lifetime gain: ratio between the lifetime w/ and w/o pTunes 23
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Adaptation to Traffic Fluctuations 24
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Adaptation to Changes in Link Quality 25
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Interaction with Routing 26
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Outline Introduction System Model Protocol Specific Modeling - X-MAC and LPP Evaluation Conclusion 27
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Conclusion Runtime adaptability Flexible modeling approach Efficient system support to “close the loop” Meet the requirements of real world applications as the network state changes Eliminates the need for time-consuming, and yet error- prone, manual MAC configuration Some comment – Well written with systematical analysis – Good extended work of Glossy – Optimization solving time is not very fast, and it get slower when there are more nodes – Developer still need to model the protocol specific part 28
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