Deployment of Surface Gateways for Underwater Wireless Sensor Networks Saleh Ibrahim Advising...
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Transcript of Deployment of Surface Gateways for Underwater Wireless Sensor Networks Saleh Ibrahim Advising...
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
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
2. Evaluation of Greedy Heuristics
Problem:– ILP is NP-hard
Proposed Solution– Greedy algorithm– Greedy-interchange 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
3. Geometry-Enhanced Formulation
Problem: Quality of solution depends on the choice of candidate locations