1. Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction (IPSN 2010) HyungJune Lee, Martin Wicke, Branislav Kusy, Omprakash Gnawali, and Leonidas Guibas 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  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?

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 a l q o rt z b p y u i x s 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 1.Find most similar pair of clusters 2.Merge it into a parent cluster 3.Calculate the average similarity between objects in two clusters 4.Repeat

12. Step III: Probabilistic Representation 1. Execute multiple sequence alignment (using ClustalW tool) - Computation complexity 2. 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 Clusters in 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: Profile: R C E C N C Mobility Profile Database J... ?

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

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 M1M1 M2M2 A T3T3 T1T1 T2T2 T4T4 T5T5 T6T6 N

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, … M1M1 M2M2 A T3T3 T1T1 T2T2 T4T4 T5T5 N

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 m 2 716 nodes 20 mobile trajectories Vehicle moves at a random speed N(30, 5 2 ) 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  W: # of future trajectory hops  Large W means more chance to exploit data stashing scheme  As W  1, data stashing should break  Implication Trade-off: Tolerated delay vs. Network performance (lower is better) (higher is better)

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

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