1. Deployment of Surface Gateways for Underwater Wireless Sensor Networks Saleh Ibrahim Advising Committee Prof. Reda Ammar Prof. Jun-Hong Cui Prof. Sanguthevar Rajasekaran

2. Multiple Surface Gateway Nodes Relay Traffic between Underwater Nodes and the Control Center Underwater Wireless Network Architecture with Surface Gateways

3. Given: Underwater Sensor Deployment – Node Locations and Data Generation Rates Find: Gateway Deployment Locations – Of a given number of surface gateways Optimizing: Variety of Obj. Functions – Latency, Energy, Network Lifetime, Reliability Surface Gateways Deployment Problem

4. 1. Deployment Optimization Model 2. Quality of Greedy Heuristic Solutions 3. Geometry-Enhanced Formulation Outline

5. V : set of underwater nodes g (v) : data generation rate of node v  V T : set of candidate locations x (t) : gateway presence indicator of t  T E : set of possible communication links f (e) : data flow rate in link e  E 1. Deployment Optimization Model A) Definitions

6. Limit number of surface gateways No flow to a candidate location t i where no gateway is present (i.e. x (t i )=0) – G : maximum possible flow 1. Deployment Optimization Model B) Constraints

7. 1. Deployment Optimization Model B) Constraints : Flow Conservation* Flow conservation at each node End-to-End Flow conservation

8. Underwater Nodes Surface Nodes 1. Deployment Optimization Model B) Constraints : Medium Access*

9. Delay d of Edge e –L message length, B bit-rate, l(e) distance, v p propagation velocity. Minimize expected end-to-end delay –Minimize 1. Deployment Optimization Model C) Objective : Minimize Expected Delay

10. Energy per packet  of Edge e –L message length, B bit-rate,  s transmission power corresponding to edge e. Minimize expected energy per packet –Minimize 1. Deployment Optimization Model C) Objective : Expected Energy Per Packet

11. 1. Deployment Optimization Model D) Results : Uniform UW Deployment

12. 1. Deployment Optimization Model D) Results : Random UW Deployment

13. 2. Evaluation of Greedy Heuristics Problem: – ILP is NP-hard Proposed Solution – Greedy algorithm – Greedy-interchange algorithm

14. 2. Evaluation of Greedy Heuristics A) Greedy Algorithm

15. 2. Evaluation of Greedy Heuristics B) Greedy-Interchange Algorithms Start from a greedy partial solution Allow at most any ONE of the already selected candidate locations to be exchanged for a better unselected location – at the same time choose an additional candidate location in a greedy manner

16. 2. Evaluation of Greedy Heuristics C) Complexity Analysis Define k: – the upper bound on the runtime of the network optimization algorithm that calculates the value of the objective function for a given deployment Optimal Greedy Greedy-Interchange

17. 2. Evaluation of Greedy Heuristics D) Evaluation Technique Reference Deployment Techniques – Random Pick the gateway candidate locations at random – Optimal Solve the ILP Test Cases – Uniform underwater deployment – Random underwater deployments Measure the decay in optimization goal – Increase in delay

18. 2. Evaluation of Greedy Heuristics D) Results : Uniform UW Deployment

19. 2. Evaluation of Greedy Heuristics D) Results : Random UW Deployment

20. 3. Geometry-Enhanced Formulation Problem: Quality of solution depends on the choice of candidate locations

21. 3. Geometry-Enhanced Formulation A) Definitions First Degree Candidate Second Degree Candidate – Where L is the line segment

22. 3. Geometry-Enhanced Formulation A) Definitions Third Degree Candidate – where – is the surface of the triangle

23. 3. Geometry-Enhanced Formulation A) Definitions Neighbors in T within a distance d of point s

24. 3. Geometry-Enhanced Formulation B) Algorithm

25. 3. Geometry-Enhanced Formulation C) Illustration of Algorithm

26. 3. Geometry-Enhanced Formulation D) Results

27. Thank you