Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction

1. Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction HyungJune Lee,Martin Wicke, BranislavKusy, OmprakashGnawali, and LeonidasGuibas Stanford University ACM/IEEE IPSN’10April 15, 2010

2. Traditional Data Delivery to Mobile Sinks in Wireless Ad-Hoc/Sensor Networks ? • Immediate delivery from data source to mobile sinks • Proactive scheme: DSDV, OLSR • Reactive scheme: DSR, AODV Performance degrades rapidly with increasing mobility • Data MULEs to collect data as it passes each of the sensor nodes • Wait until mobile sinks come to collect Often infeasible if we cannot control the movement • What’s a compromise between two extremes? • How to exploit the tolerated delay? • How to use regularity of mobility pattern? • How to select only a partial set of effective relays?

3. 1. Trajectory Prediction Anticipated trajectory nodes 2. Data request and trajectory announcement 3. Stashing node selection To cover the likely paths and minimize the routing cost 4. Data stashing 5. Data collection by mobile nodes Overview: Predictive Mobile Routing

4. Summary of Contributions • Predictive Model of Users’ Trajectories • In the space of wireless connectivity • Capture • Long-term behavior (in minutes) • a set of the future connected relays • Predictive Data Delivery • Propose an energy-efficient data delivery scheme to mobile sinks • Turn even limited knowledge of future connectivity into networking benefit A

5. Outline [Off-line Learning Phase] • Mobile Trajectory Model • In the space of wireless connectivity • For packet delivery purpose [Routing] • Prediction of Future Relay Connectivity • Predictive Data Delivery to Mobile Users [Evaluation]

6. Capturing Mobile Trajectory Patterns • Background • Trajectory: a sequence of node associations on a given spatial path • Trajectories from the same spatial trajectory are not necessarily identical • Due to imperfect links and radio signal strength fluctuations • Goal • To cluster similar mobile trajectories • General trajectory pattern models explored by a number of spatial trajectories p s y q t r a u l i b z x o T = a l o r t z b p y u T’ = a l q o r z s p i u z T’’= a q r t z t s b y i x

7. Constructing trajectory clusters • Step I. Similarity measure • Step II. Hierarchical clustering • Step III. Compact representation

8. Similarity measure (normalized) Not a distance metric Step I: Similarity Measure

9. Step II. Hierarchical Clustering • Hierarchical clustering : • Every point is its own cluster • Find most similar pair of clusters • Merge it into a parent cluster • Calculate the average similarity between objects in two clusters • Repeat

10. Execute multiple sequence alignment(using ClustalW tool)- Computation complexity Construct Profile : A probabilistic representation for efficient search in the usage phase R T E A C E G I P D S R E C E I G I P S D S Y E C I R E C E I C G I G N G N D S E D E C I G P D S R E C H C I G K D S R E C I G C R I E C G S G D L D K S K E C G I G T D W D S R E C N I G D G T D S R E P E C N I G I D G D K D S Step III: Probabilistic Representation -RT-EACE-GIP----D--S -R--E-CEIGIPS---D--S --Y-E-C---I--------- REC-EICG--IGNG-ND--S -ED-E-C---IGP---D--S -R--E-CH-CIGK---D--S -R--E-C---IGC------- -RI-E-CG--SG-D-LDK-S --K-E-CG--IGTD-WD--S -R--E-CN--IG-DGTD--S -REPE-CN--IGID-GDKDS

11. Summary: Mobility Trajectory Clustersin an off-line phase Trajectory sequences ……………… ………………………. …………………. …………………………. ……………

12. Outline [Off-line Learning Phase] • Mobile Trajectory Model [Routing] • Prediction of Future Relay Connectivity • Predictive Data Delivery to Mobile Users [Evaluation]

13. Given a partial test sequence, 1) First find the closest cluster A variant of Smith-Waterman algorithm for local matching With the largest F(*,*) among all profiles 2) Find the highly overlapped region Prediction of Future Relay Connectivity ? Test sequence: . . . R C E C N C Profile: J Mobility Profile Database

14. 3) Obtain the most probable subsequences starting from J+1 through J+W Prediction of Future Relay Connectivity W J

15. Optimal Route Selection Using Predictive Knowledge • Data stashing: Given a set of future trajectories of multiple mobile users, • Find the optimal stashing nodes for each data source • Considering • Cover all possible future trajectories • Minimize routing cost to the selected relay nodes T2 T3 T1 T4 T6 T5 N A M1 M2

16. Optimal Route Selection Using Predictive Knowledge • Optimization problem • For sensor node A, • Minimize total routing cost • From sensor node itself • To the selected stashing nodes • Subject to • Stashing nodes cover all possible future paths of multiple mobile users • Solved by LP/IP solvers such as CPLEX, Gurobi, GLPK, … T2 T3 T1 T4 T6 T5 N A M1 M2

17. Outline [Off-line Phase] • Mobile Trajectory Model [Routing] • Prediction of Future Relay Connectivity • Predictive Data Delivery to Mobile Users [Evaluation] • Dynamic mobility model • Prediction Accuracy • Routing performance • Scalability • Tolerated Delay • Load Balance • Computation for Selecting Stashing Nodes

18. Prediction Accuracy of Mobile Trajectory Model • Validated trajectory clustering using UMass DieselNet real-world dataset : 34 buses, 4198 APs, 789 bus trips around UMass campus • Prediction method results in excellent stashing node selections for real-world data

19. Simulation Setup for Routing • TOSSIM under ‘meyer-light’ interference • 830x790 m2 • 716 nodes • 20 mobile trajectories • Vehicle moves at a random speed N(30, 52) km/h • Vehicle sends a beacon every 1 sec • Each sensor node has data to deliver to mobile sinks

20. Scalability depending on # of mobile sinks • Data stashing consumes less energy thanimmediatepoint-to-point routing • Scalable with # of mobile sinks! • Data stashing keeps high packet delivery even for network congestion • Data stashing performs closely to the upper bound by perfect prediction • Even limited knowledge of future trajectories can significantly improve routing performance! (lower is better) (higher is better)

21. Tolerated Delay W (lower is better) (higher is better) W: # of future trajectory hops Large W means more chance to exploit data stashing scheme As W 1, data stashing should break ImplicationTrade-off: Tolerated delay vs.Network performance

22. Load Balance better Data Stashing Immediate Routing Data stashing has a good load balancing performance compared to a point-to-point routingimmediatelyto mobile sinks

23. Running time for a source to compute stashing nodes (lower is better) PC: Dell Precision 390 (2.4 GHz Core 2 Duo)Small Embedded: fit-PC2 (Intel Atom Z530 1.6GHz) Measured running time for solving the optimization problem - binary integer program Feasible even in a small embedded platform, taking less than 500ms

24. Conclusion • Dynamic mobile trajectory model in the space of wireless connectivity, capturing wireless volatility • Mobile data delivery can be improved through mobility pattern learning and prediction • Even limited knowledge of the future trajectory can improve networking performance • Take-home lesson:“If you know where someone is going (even uncertainly), you can deliver data to him more efficiently and reliably.”

25. Limitations & Future Works Two problems • Current delivery scheme is “best-effort” • Current clustering method cannot share common pieces of trajectories More robust packet delivery: • When the system detects delivery would fail, restashing can significantly improve robustness • Trajectory prediction and data stashing can be more intertwined Multi-tier clustering: • Long trajectories can be partitioned into short pieces for efficient clustering • On-line clustering • A multi-tier clustering approach can deal with extremely large complex networks

26. Questions? HyungJune Lee abbado@stanford.edu