Deployment of Surface Gateways for Underwater Wireless Sensor Networks

Saleh Ibrahim

Advising Committee Prof. Reda Ammar Prof. Jun-Hong Cui Prof. Sanguthevar Rajasekaran

Multiple Surface Gateway Nodes Relay Traffic between Underwater Nodes and the Control Center

Underwater Wireless Network Architecture with Surface Gateways

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

1. Deployment Optimization Model

2. Quality of Greedy Heuristic Solutions

3. Geometry-Enhanced Formulation

Outline

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 ModelA) Definitions

Limit number of surface gateways

No flow to a candidate location ti where no gateway is present (i.e. x (ti)=0)

– G : maximum possible flow

1. Deployment Optimization ModelB) Constraints

1. Deployment Optimization ModelB) Constraints : Flow Conservation*

Flow conservation at each node

End-to-End Flow conservation

Underwater Nodes

Surface Nodes

1. Deployment Optimization ModelB) Constraints : Medium Access*

Delay d of Edge e

– L message length, B bit-rate, l(e) distance, vp propagation velocity.

Minimize expected end-to-end delay

– Minimize

1. Deployment Optimization ModelC) Objective : Minimize Expected Delay

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 ModelC) Objective : Expected Energy Per Packet

1. Deployment Optimization ModelD) Results : Uniform UW Deployment

1. Deployment Optimization ModelD) Results : Random UW Deployment

2. Evaluation of Greedy Heuristics

Problem:– ILP is NP-hard

Proposed Solution– Greedy algorithm– Greedy-interchange algorithm

2. Evaluation of Greedy HeuristicsA) Greedy Algorithm

2. Evaluation of Greedy HeuristicsB) 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

2. Evaluation of Greedy HeuristicsC) 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

2. Evaluation of Greedy HeuristicsD) 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

2. Evaluation of Greedy HeuristicsD) Results : Uniform UW Deployment

2. Evaluation of Greedy HeuristicsD) Results : Random UW Deployment

3. Geometry-Enhanced Formulation

Problem: Quality of solution depends on the choice of candidate locations

3. Geometry-Enhanced FormulationB) Algorithm

3. Geometry-Enhanced FormulationC) Illustration of Algorithm

3. Geometry-Enhanced FormulationD) Results

Thank you