1. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks Yu (Jason) Gu 1, Tian He 1, Mingen Lin 2 and Jinhui Xu 2 Department of Computer Science and Engineering 1 University of Minnesota, Twin Cities 2 State University of New York at Buffalo

2. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 22 Motivation Target Tracking Border Control Infrastructure Protection Traffic Control Assisted Living Disaster Response Real-time data delivery Long-term operation (Low-Duty-Cycle) + How to achieve delay requirements in low- power networks ?

3. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 33 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 44 Related Works 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 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 We are the first to address real-time issue in low-duty-cycle Networks

5. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 55 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. 2 383 active 84 Period = 100 active Node Working Schedule : { 2, 83 } Node Duty Cycle : 2 / 100 = 2% An Active Instance

6. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 66 Delay in Low-Duty-Cycle Networks A A B B C C D D Packet Arrival Time : {41}{71}{91} 417191 {1} 1 End-to-end communication delay is 90 BCDA Sleep Latency = 40  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.

7. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 77 Agenda

8. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 88 How to Temporally Reduce Delay? A A B B C C {41}{3,79}{1} 4179 1 Packet Arrival Time : {2,41} 23 1 Packet Arrival Time : BCA BCA Original New Active Instance Augmentation Scheme

9. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 99 Optimization Goal A A B B C C E E D D S S {41} {38} {73} {1} {92} {15} 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 10 Where to Augment Active Instance? The augmented active instance should always reduce sleep latency to 1 A A B B C C {41}{25,79}{1} {2,41} Waiting in the network can never reduce E2E delay! E2E delay = 24 {24,41} E2E delay = 24 225 1 BCA 2425 1 BCA

11. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 11 How to Find Optimal Active Instance Augmentation ? Dynamic programming –Intermediate State L ij (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. A A B B C C L AB (1,0) : Minimal delay from node A to B through edge AB without any active instance Augmentation L AC (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 i: source j: destination m: edges traversed h: number of active instance augmented

12. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 12 Example Walkthrough: Initial States A A B B D D {41} {25,97} {1} Initial States: –L AB (1,0) = 40, L AC (1,0) = 14 –L AB (1,1) = 1, L AC (1,1) = 1 –L AD (2,2) = 2 L ij (m,h) i: source j: destination m: edges traversed h: number of active instance augmented {2,41} {3,25,97} C C {15} {2,15} L ij (m,h) = { d ij m=1,h=0 m m = h

13. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 13 Recursive Computation i i p p j j { L ip (m-1,h-1) + 1 L ij (m,h) = min 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 L ip (m-1,h) + d pj

14. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 16 Agenda

17. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 17 How to Spatially Reduce Delay ? A A B B C C E E D D F F Z Z Y Y 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 18 How to Find Optimal Sink Nodes? A A D D C C B B E E Assume Delay Bound is 100: S A ={A,C,D}S B ={B,C}, S C ={A,B,C}, S D ={A,D,E}, S E ={D,E} The problem transforms to set cover problem A A B B C C D D E E A A B B C C D D E E 01857316124 066290247102 99015511820 15320139477 101144830172

19. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 19 Solving the Set Cover Problem –Repeatedly choose the set that contains the largest number of uncovered nodes –Best-possible polynomial time approximation under plausible complexity assumptions. A A D D C C B B E E S A ={A,C,D}, S B ={B,C}, S C ={A,B,C}, S D ={A,D,E}, S E ={D,E}

20. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 20 Agenda

21. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 21 Drawbacks of Temporal Delay Control Not effective when delay bound is very small !

22. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 22 Drawbacks of Spatial Delay Control Inefficient for augmenting last a few sink nodes!

23. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 24 Hybrid Design Cost Ratio: –Augmenting a sink node over augmenting an active instance –Based on hardware cost, lifetime of sink and sensor nodes, human intervention cost, … A A D D C C B B E E S A ={A,C,D}, S B ={B,C}, S C ={A,B,C}, S D ={A,D,E}, S E ={D,E} Number of active instance augmentation for Node A,D,E Cost(Sink) >Cost(Active Inst. Aug.) –Augment Active Instances for Node A, D, E Cost(Sink) <Cost(Active Inst. Aug.) –Augment Sink Node D

25. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 26 Energy Efficiency of Temporal Delay Control Consume half amount of energy than the baseline

27. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 27 Deadline Miss Ratio vs. Augmented Sink Larger delay bounds lead to smaller miss ratios

28. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 28 Hybrid Performance Hybrid is able to achieve the minimum system cost

29. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 29 Testbed Performance We are able to bound E2E delays on real system

30. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 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. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 31 http://mess.cs.umn.edu

32. December 3, 2009 Yu (Jason) Gu @ RTSS ‘09 32 Recursive Computation Example A A B B D D {41} {25,97} {1} C C {15} { Initial States: –L AB (1,0) = 40, L AC (1,0) = 14 –L AB (1,1) = 1, L AC (1,1) = 1 –L AD (2,2) = 2 L AB (1,0) + (42-41) = 40 + 1 = 41 L AC (1,0) + (16-15) = 14 + 1 = 15 L AD (2,1) = min = 15 {25,42,97}{16,25,97} L AB (1,1) + (25 - 2) = 1 + 23 = 24 L AC (1,1) + (25 - 2) = 1 + 23 = 24 {2,41} {2,15} L ip (m-1,h-1) + 1 L ij (m,h) = min L ip (m-1,h) + d pj { Recursive Equation: