1. Prediction Assisted Single-copy Routing in Underwater Delay Tolerant Networks Zheng Guo, Bing Wang and Jun-Hong Cui Computer Science & Engineering Department, University of Connecticut, Storrs, CT, 06269 IEEE Globecom 2010 1

2. Outline Introduction Network Model Aggressive Chronological Projected Graph (ACPG) Prediction Assisted Single-Copy Routing (PASR) Performance Evaluation Conclusion 2

3. Introduction Many routing protocols have been proposed to deal with the lack of contemporaneous end-to-end paths in delay tolerant networks (DTNs). Due to node mobility and sparse node deployment, UWSNs can be treated as DTNs. Limited bandwidth High power consumption Mobility patterns 3

4. Introduction UWSNs are extremely resource stringent since acoustic communication. Furthermore, the mobility patterns in an UWSN can vary dramatically over time depending on the environment. These two characteristics render existing multi-copy based DTN routing protocols unsuitable for UWSNs. Waste Energy 4

5. Goal Proposed a generic scheme, prediction assisted single-copy routing (PASR), for UWSNs. PASR can be instantiated to efficient single-copy routing protocols under different mobility models. 5

6. Network Model Consider a data collection underwater sensor network, which consists of M layers. Surface Layer 1 Layer 2 Layer M Super Source …… Sink Sensor Current Link Water Currents Each Sensor ： Buffer Battery 6

7. Aggressive Chronological Projected Graph (ACPG) Proposed a greedy algorithm to capture the network mobility properties and the common characteristics of near optimal routes. ACPG compresses the evolving network topology and connectivity to a single graph (,) chronologically, efficiently finds routes from the graph in a slot by slot manner. 7

8. Aggressive Chronological Projected Graph Construction G(V, E) Vertex v ij ∈ V is the j th node in the i th layer. Edge (v ij, v kl ) ∈ E represents the connection between these two nodes during a certain time slot. Layer 1 Layer 2 Layer 3 v 31 v 32 v 33 v 34 v 21 v 22 v 23 v 24 v 11 v 12 v 13 v 14 (v 21, v 12 ) vdvd vsvs (u,v,t,C) u v the capacity of that connection 8

9. Aggressive Chronological Projected Graph A. Construction G(V, E) Vertex v ij ∈ V is the j th node in the i th layer. Edge (v ij, v kl ) ∈ E represents the connection between these two nodes during a certain time slot. Node v ∈ V can be in two status: inactive or active. Node v U v ： The upstream node. I v (i) ： The maximum number of packets that can be transmitted or received during the ith slot. C v (i) ： The available storage in the ith slot P v ： The residual power for transmissions v 31 v 32 v 33 v 34 v 21 v 22 v 23 v 24 v 11 v 12 v 13 v 14 vdvd vsvs 9 Layer 1 Layer 2 Layer 3

10. Aggressive Chronological Projected Graph B. Operations of ACPG The operations of ACPG at each slot t, t > 0, include two routines: 1.Edge projection during which connections in a time slot are projected to G as edges 2.Routes reservation and graph update during which routes are discovered and G is updated. 10 Layer 1 Layer 2 Layer 3 v 31 v 32 v 33 v 34 v 21 v 22 v 23 v 24 v 11 v 12 v 13 v 14 vdvd vsvs (u,v,t,C) u v (v 32,v 23,2,5 ) (v 32,v 23,5,4 )

11. Aggressive Chronological Projected Graph An example of projected graph for 12 nodes in 3 layers. 11 Layer 1 Layer 2 Layer 3 v 31 v 32 v 33 v 34 v 21 v 22 v 23 v 24 v 11 v 12 v 13 v 14 vdvd vsvs Inactive node Active node (1,5)(2,7) (3,5)/(7,9) (4,6)/(8,5) (5,3)(6,6) (7,7) (9,10) (v d ←v 12 ← v 22 ← v 31 ← v s ) Capacity ： 5 Packets Delay ： 8 slots (v d ←v 12 ←v 22 ← v 23 ← v 33 ← v s ) Capacity ： 2 Packets Delay ： 7 slots (7,4) (7,2) (9,5)

12. Performance of ACPG Evaluate the performance of ACPG by comparing it with optimal solutions from integer linear programming (ILP). Each sensor has Buffer size=30 packets Transmission range=100 m 3 rd layer generating packets from the 500 th second to the 1000th second with the rate of one packet per second. 12 600m 90m Layer 1 Layer 2 Layer 3

13. Performance of ACPG 13 Comparison of ILP and ACPG.

14. Prediction assisted single-copy routing (PASR) A. How to works PASR? Propose prediction assisted single-copy routing (PASR), that utilizes ACPG in a training period to capture the characteristics of the mobility pattern, and provide guidance on route selection. Historical information Guidance from ACPG Predict the future 14

15. Historical information If the mobility pattern is stable for a long time, the history can tell the future. The most widely used historical information includes. Recent trajectory Average contact duration Average inter-contact duration Last contact time Contact frequency 15

16. Guidance from ACPG The following properties of routes and node contacts, which are closely related to the underlying mobility pattern, can be captured by ACPG: Geographic preference Contact periodicity Contact probability 16

17. Predict the future After ACPG characterizes the mobility pattern, it suggests what historical information can be used for prediction. 17

18. Prediction assisted single-copy routing (PASR) B. Instantiating PASR Consider three mobility models in an underwater sensor network. UWSN in Regular Currents UWSN in Currents with Randomness UWSN in Irregular Currents Each sensor has Buffer size ： 100 packets Transmission range ： 50m Transmission rate ： 50 packet/s Power capacity ： 300 to 30 Slot duration ： 10s 18 800m 40m Layer 1 Layer 2 Layer 3

19. UWSN in Regular Currents 1)Guidance from ACPG: Focus on two properties: 1.Geographic preference 2.Contact periodicity. 19 Layer 1 Layer 2 Layer 3 Sink Super Source Geographic preference

20. UWSN in Regular Currents 2) Protocol following ACPG: Based on the above guidance, proposed a specific PASR for this network, energy efficient history prediction assisted routing (EEHPA). 20

21. UWSN in Regular Currents (EEHPA) 2) Protocol following ACPG This scheme includes two essential operations: 1. Prediction update Each node u maintains its own prediction vector (PV), which is a vector of tuples (i,v,D v ). i is the prediction slot v is the best relay in this slot D v is the expected delay through this relay to the sink 2. Per-contact forwarding decision (1) D v ∈ [D v’, D v’ + δ 1 ) (2) D v ∈ [D v’ + δ 1, D v’ + δ 2 ] and node v is in the upper layer. 21 PV δ 1,δ 2 are called prediction error tolerances u v

22. UWSN in Currents with Randomness The randomness models the impact from environment, which may lead to estimation errors and prediction errors in real systems. PASR can tolerant these errors to some extent since ACPG just captures the general properties of the majority of nodes, who exhibit similar mobility patterns. 22 (EEHPA)

23. Assume that nodes in the first two layers will be affected by an irregular water current. Modify EEHPA to obtain a new PASR, named iEEHPA, according to the new guidance. Two guidance from ACPG: (1) A node in the same layer is preferred (2) Only predict for nodes in the same layer UWSN in Irregular Currents (iEEHPA) 23 Layer 1 Layer 2 Layer 3 Sink Super Source Irregular water current Regular current 10s Anchored node

24. Performance Evaluation Each sensor has Buffer size=100 packets Transmission range=50m Transmission rate=50 packet/s Power capacity = 300 to 30 Slot duration = 10s Bottom layer randomly generate 300 packets from the 500th second with the total generation rate of one packet per second. 24 800m 40m Layer 1 Layer 2 Layer 3 Sink Super Source

25. Performance Evaluation 25 EEPA : without kinematic model FC : First Contact Epidemic : A flooding based scheme

26. Performance Evaluation 26

27. Performance Evaluation 27

28. Conclusion Present a generic scheme prediction, assisted single-copy routing (PASR), for UWSNs. Design online heuristic protocols by choosing appropriate historical information and forwarding criteria based on the guidance from ACPG. Investigate an UWSN with various mobility patterns and randomness using two instantiated PASR schemes, EEHPA and iEEHPA. Simulation results show that ACPG captures the properties of various mobility patterns and provides corresponding guidance, and the instantiated PASR schemes outperform others. 28

29. Thanks for your attention 29