1. Extending wireless Ad-Hoc
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Extending wireless Ad-Hoc
networks Lifetime
via Genetic Algorithm
Presenting: Igor Bakman
Supervisor: Professor Michael Segal

2. Contents Background – Ad-Hoc wireless networks
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Contents
Background – Ad-Hoc wireless networks
Problem definition – Lifetime problem
Offered solution – using GA to enhance lifetime
GA simulation
Example
Probabilities
GA results
Compared algorithms
Comparison results
Results analysis
Conclusions

3. Ad-Hoc wireless networks
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Background
Ad-Hoc wireless networks
Definition –
A wireless Ad-Hoc network is a decentralized wireless network. The network is Ad-Hoc because it does not rely on a preexisting infrastructure, such as routers in wired networks or access points in managed wireless networks.
Instead, each node participates in routing by forwarding data to other nodes.
The determination of which nodes forward data is made dynamically based on the connectivity of a given network.

4. Ad-Hoc wireless networks
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Background
Ad-Hoc wireless networks
Application –
The decentralized nature of wireless Ad-Hoc networks makes them suitable for a variety of applications where central nodes can't be relied on. Minimal configuration and quick deployment make Ad-Hoc networks suitable for emergency situations like natural disasters or military conflicts. The presence of a dynamic and adaptive routing protocol will enable Ad-Hoc networks to be formed quickly.

5. Problem definition Lifetime problem
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Problem definition
Lifetime problem
An acute issue in Ad-Hoc wireless networks is the lifetime problem. Despite the great advantages of Ad-Hoc wireless networks, the lifetime problem overshadows them. All the positive features depend on how long can the networks last.
The most strict scenario calls a network failure when a single node collapses due to battery depletion.
This project is another attempt to find a solution to this problem or perhaps to minimize the disadvantage to a point that it is overshadowed by the advantages.

6. Offered solution Genetic algorithm
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Offered solution
Genetic algorithm
Definition –
GA is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics.
Furthermore, genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

7. Offered solution Genetic algorithm
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Offered solution
Genetic algorithm
General algorithm –
Choose the initial population of individuals.
Evaluate the fitness of each individual in population.
Repeat on this generation until termination: (time limit, sufficient fitness achieved, etc.)
Select the best-fit individuals for reproduction.
Breed new individuals through crossover and mutation operations to give birth to offspring.
Evaluate the individual fitness of new individuals.
Replace least-fit individuals with new individuals.

8. GA simulation Lifetime ended, Crossover Init Network id dead Yes
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GA simulation
Crossover
Lifetime ended,
Network id dead
Init
Yes
Use fitness
To choose
solution
Set schedule
Is there
a depleted
Battery?
Set
population
Transmission No

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Example

10. Probabilities The number of all solutions in the network is:
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Probabilities
The number of all solutions in the network is:
The number of solutions the algorithm offers is:
Given the formulas above, next are the probabilities to include the optimal solution in the population:
Probability with 10 nodes ~ 1/20
Probability with 16 nodes ~ 1/612
Probability with 25 nodes ~ 1/134217
Probability with 50 nodes ~ 1/1*10^12
Probability with 100 nodes ~ 1/6*10^26

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GA results
Figure 1: the relation between how many mean transmissions each node has left to transmit to one of its neighbors and the number of nodes
Figure 2: the relation between the percent of successful missions transmitted and the number of nodes.

12. Compared algorithms Simulated annealing – Minimal longest edge –
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Compared algorithms
Simulated annealing –
SA is a generic-probabilistic algorithm that solves global optimization problems. Specifically, it gives good approximation to a global optimum for a given function in a search space.
Minimal longest edge –
A deterministic version of SA.
Shortest path (Dijkstra) –
Dijkstra finds the best route (i.e. lowest cost) for every pair of source-destination.

13. Comparison results הפקולטה למדעי ההנדסה
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Comparison results

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Results analysis
The mean results decline as the number of nodes in the network grows. This phenomenon can be explained by the probabilities for the optimal solution above.
The result graphs and probability analysis illustrate an ambiguous situation. On the one hand probability analysis showed clearly that my algorithm gives small probability that just shrinks as the network grows. Yet on the other hand, the results shows a very solid algorithm that gives fair results.
In fact, there is no contradiction. The probabilities stand for the optimal solution. Yet, there are many more solutions that may not be optimal but will provide a fair solution.

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Conclusions
The ingredient in Genetic that had the most impact on results in terms of lifetime is the fitness function. It is highly crucial to choose wisely the fitness, yet fitness that will suit one scenario can be ineffective in other scenarios; thus fitness should be fitted to the scenario definition.
Crossover function did not contribute as it should have according to the theory. From this I conclude that the crossover is not fitted to this kind of search problem and thus does not contribute to the algorithm; its running time and space could be either exploited in favor of population or excluded completely from the algorithm, consequently improving the running time and saving memory space.

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Conclusions
Despite low probabilities for having the optimal solution, GA utilizes the fact that there are many more close to optimal solutions that can provide a fair and solid outcome for the lifetime problem. In fact the probability is grater considering the tradeoff in performance.
In comparison to the other algorithms GA undisputedly outperformed them in small sized networks. The comparison graph shows different behavior (steeper decline) of GA next to the other algorithms. This can be attributed to the different utilization of probabilistic qualities by GA compared to SA and the deterministic nature of the other algorithms.

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The end.