Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks

1. Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks Yu (Jason) Gu1, Tian He1, Mingen Lin2 and Jinhui Xu2 Department of Computer Science and Engineering 1University of Minnesota, Twin Cities 2State University of New York at Buffalo

2. Disaster Response Traffic Control Assisted Living Target Tracking Border Control Infrastructure Protection Motivation Long-term operation (Low-Duty-Cycle) How to achieve delay requirements in low-power networks ? + Real-time data delivery

3. Design Objectives • Real-time guarantee of communication delay for long-term low-duty-cycle sensor network applications • Can be applied to generic low-duty-cycle network model • Minimum energy/system cost

4. Related Works • Low-Duty-Cycle Networking • Scheduling • Yang et al.(PTW), RTAS’04; Lu et al. (DESS), INFOCOM’05; Gu et al. (ESC), ICNP’09 • Unicast • Gu et al (DSF), SenSys’07; Su et al., ICNP’08 • Multicast and flooding • Guo et al. MobiCom’09; Wang et al. , INFOCOM’09; Su et al., ICNP’09; Sun et al. (ADB), SenSys’09 • Real-Time Communication • Traffic Regulation • Vasudevan et al., SenSys’03; He et al. (AIDA), TECS’04, Karenos et al., RTSS’06 • Feedback-based • Lu et al. (RAP), RTAS’02; He et al. (SPEED), ICDCS’03; Felemban et al. (MMSPEED), INFOCOM’05 • Traffic Scheduling • Carley et al., RTSS’03; Li et al., RTAS’05 • Analysis Method • Mohan et al., RTSS’04; Abdelzaher et al., RTSS’04 We are the first to address real-time issue in low-duty-cycle Networks

5. What is a Low-Duty-Cycle Network • A low-duty-cycle network is formed by nodes that listen briefly and shut down their radios most of the time (e.g., 95% or more). • To communicate, a wakeup schedule must be shared among neighboring nodes. An Active Instance active active Period = 100 2 3 83 84 Node Working Schedule : { 2, 83 } Node Duty Cycle : 2 / 100 = 2%

6. Delay in Low-Duty-Cycle Networks • Usually packet can be successfully delivered from a sender to a receiver within an active instance. • TOS packet size 47 bytes, 20ms active instance duration, 13 tx by using CC2420. Above 30% link quality ensures 99% delivery ratio. {1} {41} {71} {91} A B C D Sleep Latency = 40 A B C D Packet Arrival Time : 1 41 71 91 End-to-end communication delay is 90

7. Agenda • Motivations and Design Objective • Network Model • Delay Control • Temporal Delay Control • Spatial Delay Control • Hybrid Design • Evaluation • Conclusion

8. How to Temporally Reduce Delay? {2,41} {1} {41} {3,79} A B C A B C Packet Arrival Time : Original 1 41 79 A B C Packet Arrival Time : New 1 2 3 Active Instance Augmentation Scheme

9. Optimization Goal {15} {41} D B {1} A {92} {73} E {38} C S Sink Node How to augment a minimum number of active instances into the network, such that E2E delays from data source nodes to the sink node are all below delay bound ?

10. Where to Augment Active Instance? The augmented active instance should always reduce sleep latency to 1 {2,41} {1} {41} {25,79} E2E delay = 24 A B C E2E delay = 24 {24,41} A B C A B C 1 2 25 1 24 25 Waiting in the network can never reduce E2E delay!

11. How to Find Optimal Active Instance Augmentation ? • Dynamic programming • Intermediate State Lij(m,h): The minimal delay a packet arrives at node j after traversing at most m edges from node i. Among m edges, the sleep latencies of h edges are reduced to 1 by augmenting h active instances along the path. • i: source • j: destination • m: edges traversed • h: number of active instance augmented A B C LAB(1,0) : Minimal delay from node A to B through edge AB without any active instance Augmentation LAC(2,1) : Minimal delay from node A to C through edge AB and BC by reducing the edge length of either AB or BC to 1

12. Example Walkthrough: Initial States • Lij(m,h) • i: source • j: destination • m: edges traversed • h: number of active instance augmented {2,41} {41} • Initial States: • LAB(1,0) = 40, LAC(1,0) = 14 • LAB(1,1) = 1 , LAC(1,1) = 1 • LAD(2,2) = 2 B {25,97} {1} {3,25,97} A D {2,15} {15} C { dij m=1,h=0 Lij(m,h) = m m = h

13. Recursive Computation i p j • Case 1: From i to p (possibly multiple hops), then to j through one single hop without any active instance augmentation • Case 2: From i to p (possibly multiple hops), then to j through one single hop by reducing sleep latency between p and j to 1 { Lip(m-1,h) + dpj Lij(m,h) = min Lip(m-1,h-1) + 1

14. How to Optimally Bound Pair-wise E2E Delay? • What we have known? • The minimum E2E delay between a source node and a destination node by augmenting h active instances • Given a Delay Bound • Find the minimum h value that yields the delay smaller than the bound and augment those active instances into the network

15. Many-to-Many Communication Bound • NP-Hard and inapproximable • Greedy Solution • Each active instance augmentation reduces maximal sum of E2E delays among all source nodes and all destination nodes.

16. Agenda • Motivations and Design Objective • Network Model • Delay Control • Temporal Delay Control • Spatial Delay Control • Hybrid Design • Evaluation • Conclusion

17. How to Spatially Reduce Delay ? B D A Z F Y E C How to select a minimum number of nodes as sink nodes such that E2E delay from any source node to a sink is within delay bound

18. How to Find Optimal Sink Nodes? A B C D E A B A 0 185 73 16 124 102 0 66 290 247 B C C 20 99 0 155 118 D E D 7 153 201 39 47 E 172 101 144 83 0 Assume Delay Bound is 100: SA={A,C,D} SB={B,C}, SC={A,B,C}, SD={A,D,E}, SE={D,E} The problem transforms to set cover problem

19. Solving the Set Cover Problem A B • Repeatedly choose the set that contains the largest number of uncovered nodes • Best-possible polynomial time approximation under plausible complexity assumptions. SA={A,C,D}, SB={B,C}, SC={A,B,C}, SD={A,D,E}, SE={D,E} C D E

20. Agenda • Motivations and Design Objective • Network Model • Delay Control • Temporal Delay Control • Spatial Delay Control • Hybrid Design • Evaluation • Conclusion

21. Drawbacks of Temporal Delay Control Not effective when delay bound is very small !

22. Drawbacks of Spatial Delay Control Inefficient for augmenting last a few sink nodes!

23. Hybrid Design Tradeoff • Temporal Delay Control • Pros: Little human intervention • Cons: Increase single node energy consumption • Spatial Delay Control • Pros: Bound E2E delays for a large number of nodes; No change on working schedule • Cons: Additional hardware cost and human intervention • We need to find a balanced configuration to achieve efficient power and cost management!

24. Hybrid Design SA={A,C,D}, SB={B,C}, SC={A,B,C}, SD={A,D,E}, SE={D,E} A B • Cost Ratio: • Augmenting a sink node over augmenting an active instance • Based on hardware cost, lifetime of sink and sensor nodes, human intervention cost, … Number of active instance augmentation for Node A,D,E C • Cost(Sink) >Cost(Active Inst. Aug.) • Augment Active Instances for Node A, D, E • Cost(Sink) <Cost(Active Inst. Aug.) • Augment Sink Node D D E

25. Evaluation • Large-Scale Simulation • Up to 5000 nodes, 100 repeated experiments for each data point • Baseline: Streamlined Wake-up in IPSN’05 • Test-bed Implementation • Linear Network, 5-hop network • 838 bytes of code memory, 12 bytes of data memory on top of a sensing application

26. Energy Efficiency of Temporal Delay Control Consume half amount of energy than the baseline

27. Deadline Miss Ratio vs. Augmented Sink Larger delay bounds lead to smaller miss ratios

28. Hybrid Performance Hybrid is able to achieve the minimum system cost

29. Testbed Performance We are able to bound E2E delays on real system

30. Conclusion • Delay Control in Low-Duty-Cycle networks is challenging! • Three schemes for delay control • Temporal solution by augmenting active instances • Energy optimal for bounding pair-wise communication • Spatial solution by augmenting sink nodes • Hybrid solution • Demonstrated effectiveness through large-scale simulation and test-bed experiments

31. http://mess.cs.umn.edu