HyungJune Lee, Martin Wicke , Branislav Kusy , Omprakash Gnawali , and Leonidas Guibas

1. Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction (IPSN 2010) HyungJune Lee, Martin Wicke, BranislavKusy, OmprakashGnawali, and LeonidasGuibas Stanford University, University of California, CSIRO ICT Centre 2011/03/14, Junction

2. Outline • Motivation • Contributions • Proposed Protocol • Offline Learning Phase • Routing • Evaluation • Conclusion

3. Traditional Data Delivery to Mobile Sinks in Wireless Ad-Hoc/Sensor Networks • 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? • Immediate delivery from data source to mobile sinks • Proactive scheme: DSDV, OLSR • Reactive scheme: DSR, AODV Performance degrades rapidly with increasing mobility

4. Overview: Predictive Mobile Routing 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

5. Outline • Motivation • Contributions • Proposed Protocol • Offline Learning Phase • Routing • Evaluation • Conclusion

6. 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

7. Outline • Motivation • Contributions • Proposed Protocol • Offline Learning Phase • Mobility Trajectory Model • Routing • Evaluation • Conclusion

8. 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

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

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

11. 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

12. Step III: Probabilistic Representation • 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 -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

13. Mobility Trajectory Clustersin an off-line phase Trajectory sequences ……………… ………………………. …………………. …………………………. ……………

14. Outline • Motivation • Contributions • Proposed Protocol • Offline Learning Phase • Routing • Prediction of Future Connectivity Model • Prediction Data Delivery to Mobile Users • Evaluation • Conclusion

15. Prediction of Future Relay Connectivity • 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 ? Test sequence: . . . R C E C N C Profile: J Mobility Profile Database

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

17. 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

18. 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 T5 N A M1 M2

19. Outline • Motivation • Contributions • Proposed Protocol • Offline Learning Phase • Routing • Evaluation • Dynamic Mobile Model • Routing Performace • Conclusion

20. 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

21. 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

22. Scalability depending on # of mobile sinks • Data stashing consumes less energy than immediate point-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)

23. 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

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

25. Running time for a source to compute stashing nodes • 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 (lower is better)

26. Outline • Motivation • Contributions • Proposed Protocol • Offline Learning Phase • Routing • Evaluation • Conclusion

27. 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