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Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction HyungJune Lee, Martin Wicke, Branislav Kusy, Omprakash.

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Presentation on theme: "Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction HyungJune Lee, Martin Wicke, Branislav Kusy, Omprakash."— Presentation transcript:

1 Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction HyungJune Lee, Martin Wicke, Branislav Kusy, Omprakash Gnawali, and Leonidas Guibas Stanford University ACM/IEEE IPSN10 April 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 2 ? Whats 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 3/34 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

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 4

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] 5

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 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 6

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

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

9 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 9

10 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 10 - 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 Clusters in an off-line phase Trajectory sequences ……………… ………………………. …………………. …………………………. …………… 11

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

13 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 13 Mobility Profile Database J... ?

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

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 M1M1 M2M2 A T3T3 T1T1 T2T2 T4T4 T5T5 T6T6 N 15

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, … M1M1 M2M2 A T3T3 T1T1 T2T2 T4T4 T5T5 T6T6 N 16

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 17

18 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 Prediction Accuracy of Mobile Trajectory Model 18

19 Simulation Setup for Routing TOSSIM under meyer-light interference 830x790 m 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 19

20 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) 20

21 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 Tolerated Delay W (lower is better) (higher is better) 21

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

23 PC: Dell Precision 390 (2.4 GHz Core 2 Duo) Small Embedded: fit-PC2 (Intel Atom Z GHz) 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) 23 Running time for a source to compute stashing nodes

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. 24

25 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 25 Limitations & Future Works

26 Questions? HyungJune Lee 26


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