ESC: Energy Synchronized Communication in Sustainable Sensor Networks

ESC: Energy Synchronized Communication in Sustainable Sensor Networks Yu (Jason) Gu, Ting Zhu and Tian He Department of Computer Science and Engineering

Background • Sustainable Sensor Networks • Aimed to operate unattended for a very long period of time (tens of years) • Scavenge energy from ambient environment (e.g., solar energy) • Energy is stored in ultra capacitors or batteries TwinStar Platform (MobiSys’09)

Why Different ? • Scavenged energy varies significantly both in Time and Space. • Only can afford low-duty-cycle operation

Conserving Energy is not Always Beneficial! • Energy Storages (batteries, capacitors) are limited in capacity. • Energy conservation with reduced performance during energy-rich periods is wasteful. • In sustainable sensor networks, energy management shall focus on balancing(synchronizing)energy supply with demand, instead of saving as much energy as possible.

ESC Design Objective • Transparent middleware • Only adjust RF activities at individual nodes • Support existing routing protocols • Distributed implementation at individual nodes

ESC Optimization Objective • Minimizing the average delay of arbitrary traffic patterns in the presence of energy dynamics by allocating (increase/reduce) duty cycles in an optimal way. • Energy-rich time: • Increased duty-cycle  reduce a maximal amount of network delay • Energy-poor time: • Decreased duty-cycle increase a minimal amount of delay

Agenda • Motivations and Design Objective • Network Model and Delay Modeling • Energy Synchronization Control • Evaluation • Conclusion

How to Represent Working Schedule? Period = 100 An Active Instance active dormant active dormant 1 2 83 84 Node Working Schedule : { 1, 83 } Node Duty Cycle : 2 / 100 = 2%

Delivery Latency in Low-Duty-Cycle Networks Sleep latency dominates communication delay! {1} {41} {71} {91} 1 2 3 4 Sleep latency is 40 Sleep latency is 30 Sleep latency is 20 End-to-end communication delay is 90

Cross-Traffic Delay G A E H D B C F I

Cross-Traffic Delay • Expected delay for packets from all predecessors to corresponding successors via node D. • Capture the most generic many-to-many communication pattern • We aim at minimizing cross-traffic delay so as to minimize network wide delay G E A E A D H D B B F F Predecessor Successor C I

Cycle Representation of Working Schedule Period = 100 Period = 100 active active active active 21 22 63 64 121 122 163 164 0 t=91 t=11 Sleep Latency is 10. 21 Sleep Latency is 30. 63

Delay Modeling : Single Link Delay p Working schedule of node D A D For a packet sent by predecessor A at time t: tn … t1 t4 t DAD(t) = p×(t2-t) +(1-p) ×p× (t3-t) +(1-p) ×(1-p) ×p× (t4-t) + … t2 t3

Delay Modeling: From a Predecessor to a Successor Cross traffic delay from A through D to E is: t1 p1 DDE(t1) t2 p2 DDE(t2) … … … tn pn DDE(tn) DAD(t) Sending time: t A D E DAE(t) = DAD(t) + p1×DDE(t1) + p2×DDE(t2) + … + pn×DDE(tn)

Delay Modeling: From all Predecessors to all Successors Weighted average for packets from all packet ready times at predecessors to all successors A W2 E W1 D DD = W1×DAE + W2 ×DAF + W3 × DBE + W4 × DBF W4 B F W3 Predecessor Successor

Agenda • Motivations and Design Objective • Network Model and Delay Modeling • Energy Synchronization Control • Evaluation • Conclusion

Energy Synchronization Control: Decrease Duty-Cycle • Method (exhaustive search): • Remove an active instance from the working schedule one by one, calculating corresponding new cross-traffic delay • Remove the active instance yields the minimal new delay • Time complexity is O(n), but n is bounded and small in low-duty-cycle network. t1 t2 … tn {t2, t3,…, tn}  D1 {t1, t3,…, tn}  D2 … {t1,t2,…, tn-1}  Dn D Min{D1,D2,…,Dn} = D2 Remove t2 from working schedule

Energy Synchronization Control: Increase Duty-Cycle (1) Cross-traffic delay: 80 20 60 A D E 52 28 Cross-traffic delay: 80 {21} {53} {81} {1} Cross-traffic delay at node D is a constant between a time interval ( e.g., (1,81) ) formed by a predecessor A and a successor E

Energy Synchronization Control: Increase Duty-Cycle (2) {1, 76} {35, 99} 99 D4 A D E 1 D3 D1 (1,35), (35,76), (76,99), (99,1) Only need to attempt to augment active instance for these 4 intervals (instead of all possible 100 time instances). The complexity is also a constant 76 D2 35

Significance of the Stair Effect of Cross-Traffic Delay 36 53 80 90 151 189 Period = 200 200 vs. 6 times ! Predecessor schedule: {36, 53, 80} Successor schedule: {90, 151, 189}

Bursty Duty-Cycle Increase/Decrease • Exhaustive search yields exponential complexity • Greedy solution is optimal ! • For increase/decrease n active instances • Apply active instance increase/decrease n times • Complexity is linear

Agenda • Motivations and Design Objective • Network Model • Modeling of Cross-Traffic Delay • Energy Synchronization Control • Evaluation • Conclusion

Evaluation • Test-bed Implementation • 30 MicaZ nodes, random placement, 4-hop network • Large-Scale Simulation • Up to 1200 nodes, 100 repeated experiments for each data point • Routing Protocols • Link-Quality-based: ETX in MobiCom’03 • Sleep-Latency-based: DESS in INFOCOM’05

Test-bed Performance ESC effectively synchronize cross-traffic delay with energy-harvesting rate

Test-bed Delay Distribution 65% delay gap at 80% percentile 200% delay gap at 100% percentile

Impact of Duty Cycle Random Avg. Delay is 1010 ESC Avg. Delay is 684 ESC has over 30% less avg. delay than the Random scheme

Conclusion • We are the first to propose the concept of Energy Synchronized Computing. • The first installment is an Energy Synchronized Communication middleware for existing network protocols. • Discover the stair-effect of cross-traffic delay. • Design a constant time complexity energy synchronization middleware that can be generically applied to many existing routing algorithms.

http://mess.cs.umn.edu

ESC: Energy Synchronized Communication in Sustainable Sensor Networks