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Evolution of Cooperation in Mobile Ad Hoc Networks

Evolution of Cooperation in Mobile Ad Hoc Networks. Jeff Hudack (working with some Italian guy). Prisoners’ Dilemma. Players choose between cooperation (C) and defection (D) Models a situation in which two players may not cooperate for mutual benefit B > A > C > D. C. D. C. D.

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Evolution of Cooperation in Mobile Ad Hoc Networks

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  1. Evolution of Cooperation in Mobile Ad Hoc Networks Jeff Hudack (working with some Italian guy)

  2. Prisoners’ Dilemma • Players choose between cooperation (C) and defection (D) • Models a situation in which two players may not cooperate for mutual benefit • B > A > C > D C D C D

  3. PD Example • Mutual cooperation is beneficial to both agents (certain payoff) • However, (D, D) is the strong equilibrium strategy C D C D

  4. Mobile Ad Hoc Networks • Self-interested devices, want • to have their own packets forwarded • to conserve power • Assumptions • All packets are of the same value and a neighbor will be punished for not forwarding any of them • Neighbors can be monitored to see their action and total payoff

  5. Direct vs. Indirect Packets A B C D In a purely selfish scenario B does not care about A’s packets and will not punish C if he drops them To simplify the game we assume that B will punish C for dropping any packets, regardless of origin

  6. Packet Forwarding Game C D C D • Mutual cooperation is great, all packets forwarded, but defecting saves power • Benefit of defection (b > 1) • (D, D) is a weak equilibrium

  7. Evolutionary Game Theory • Components • The Game • Interaction Model • Replicator Dynamics • Repeated game play using the interaction model • Strategies evolve according to replicator dynamic • Widely applicable: • Sociology: interaction among self-interested individuals in society • Biology: evolution of complex ecosystems • Physics: arrangement and interaction of particles • Computer Science: multi-agent systems with self-interested agents

  8. Interaction Model • Random Geometric Graph • Nodes placed randomly in space • Interact if within (Euclidean) distance r • Toroidal space to avoid border effects such as • Edge nodes have no packets to forward • Lower degree at edges • Mobility models tend to gather at center • Agents play all neighbors at each time step

  9. Strategy Evolution • Replication by imitation • Choose a neighbor j at random • If Pi > Pj, do nothing • Otherwise, adopt neighbors strategy with probability proportionate to how much better they did

  10. Mobility Model • Random Waypoint Model • Each agent chooses a destination point at random, moves towards it • When arrived, choose new waypoint • The most popular mobile ad hoc network simulation model (but not perfect!) • Parameters • v: velocity of agents • p: pause time (p=0)

  11. Expectations • Brownian movement keeps agents relatively close to one another • RWP inherently leads to constant changing of neighbors • It should be harder for RWP (a more realistic model) to converge to cooperation

  12. Experiments • Parameters • Fixed: N = 1000, r = 1 • Variable: b, ρ = N/L2 • XP1: Density -> % cooperation convergence • Fixed b = 1.1, v = {0.001, 0.01} • XP2: Comparison of Brownian and RWP models • Link Change Rate (LCR) - frequency of link generations/breaks • Link Duration (LD) - lifespan of links • XP3: b vs. v -> % cooperation convergence • Fixed ρ = 1.3

  13. XP1: Motivation • Show the transition of evolution to cooperation w.r.t. density • Brownian, v=0.01 Meloni, S., Buscarino, A., Fortuna, L., and Frasca, M. (2009). Effects of mobility in a population of prisoner’s dilemma players. pages 1–4.

  14. XP1: Agent Density (v=0.001)

  15. XP1: Interpretation • Convergence to cooperation is still possible with RWP! • However, RWP needs slower movement to counteract the volatility of the mobility model • Is it because the dynamic models are inherently different?

  16. XP2: Motivation • Brownian model with v = 0.01, σ = 1.3, b = 1.1 always converges to full cooperation • RWP model with same parameters converges to defection • RWP model with v = 0.001 has similar behavior as Brownian with v=0.01 • GOAL: Compare the dynamic properties of the mobility models

  17. XP2: Link Change Rate

  18. XP2: Link Duration

  19. XP2: Results • Brownian (v=0.01) • LCR: 0.033 • LD: 122.89 • RWP (v=0.001) • LCR: 0.0037 • LD: 1249.8 • Conclusion: Not even close!

  20. XP2: Interpretation • The LCR and LD are not the reasons for the different behavior • The must be a different dynamic, guessing something like “edge diversity” • NEED METRIC: How often are agents that disconnect reconnecting to each other?

  21. XP3: Motivation • Show the relationship between velocity and the benefit of defection • In progress! Had to restart due to an error with random seeding giving agents the same waypoint.

  22. Future Work • New mobility models • Gauss-Markov turning model • Squad-based movement • New replicator dynamics • Stochastic PD for direct vs indirect routing • Pockets of cooperation are no longer collection of individuals, but rather a structure with changing individuals • This may be the “big idea” for dissertation

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