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The Multi-agent System for Dynamic Network Routing

The Multi-agent System for Dynamic Network Routing. Ryokichi Onishi The Univ. of Tokyo, Japan. Contents. Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Our proposal multiplying entries, evaluating entries Simulation and result effect of each model and formula

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The Multi-agent System for Dynamic Network Routing

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  1. The Multi-agent System for Dynamic Network Routing Ryokichi Onishi The Univ. of Tokyo, Japan

  2. Contents • Related theme and paradigmsMANET environment, AntNet, Miner’s Model • Our proposalmultiplying entries, evaluating entries • Simulation and resulteffect of each model and formula • Conclusion

  3. Computer and wireless computer networks - decentralized management wireless networks - centralized management

  4. multi-hop ad-hoc wireless peer-to-peer autonomous MANET environment An epoch-making way of wireless communication

  5. DB internet How good is the MANET ? • A basestation isn’t a must for their communication • more devices communicate with basestations DB the usual environment the MANET environment internet

  6. Q the latest RA from Node Q destination D A P source MA Routing Agent C B Message Agent Miner’s model Agent-based architecture for wireless network • MA moves to a neighbor node the latest RA from destination came through.

  7. AntNet Agent-based algorithm for wired network • Ants follow & deposit pheromone trails. • pheromone • frequencyPheromone trails are piled up on the ground. • quantityThe rich food is, the more ants deposit. • freshnesspheromone evaporates along time.

  8. Problems of AntNet • the blocking problem • If a good route is broken,searching another route needs long time. • the shortcut problem • Even if a better route appeared,this new route is seldom discovered. Our routing agents walk randomly, and don’t follow pheromone trails.

  9. About our model • algorithm (mind) • Making good routes in a sense of probabilityby ants’ path-finding model • framework (body) • A simple decentralized managementby multi-agent system

  10. Contents • Related theme and paradigmsMANET environment, AntNet, Miner’s Model • Our proposalmultiplying entries, evaluating entries • Simulation and resulteffect of each model and formula • Conclusion

  11. Q three EAs from Node Q destination D A P source MA Explorer Agent C B Messenger Agent Multiply entries (model example) • MA moves to a neighbor node the most EAs from its destination came through.

  12. new old dest next next next next N A D A A O B C C D P null null null null Q A B A A R C C C C multiplied up to 4 entries Multiply entries (table example) • Pheromone trails are piled up on the ground. • More route information from EAs are held in the routing tables. a single entry

  13. Q three EAs from Node Q destination D A P source MA Explorer Agent C B Messenger Agent Evaluate entries (model example) • MA moves to a neighbor node which has the highest value of information on its destination.

  14. Evaluate entries (table example) two attached sub-entries • time • the number of hops

  15. Explorer Agent The total reliability Evaluate entries (the way of evaluation) • p:the broken-link ratio a time • t:the time since info. gotten S R1 R2 R3 Rh-1 D source node destination node • h:#hops to the destination • hEA:#hops EAs move a time

  16. [ Ant metaphor ] Pheromone trails are piled up on the ground. Pheromone trails evaporate along time. The rich food is, the more trails ants deposit. Ant metaphor and our model [ Our model ] • Each next-node entry is multiplied. • Next-node info. is evaluated with freshness sub-info. • Next-node info. is evaluated with distance sub-info.

  17. Contents • Related theme and paradigmsMANET environment, AntNet, Miner’s Model • Our proposalmultiplying entries, evaluating entries • Simulation and resulteffect of each model and formula • Conclusion

  18. Gateway Node 4 [units], stationary information sources 60[m] radio wave range Mobile Node 100 [units] 3.6 [km/hr] const. vector 60[m] radio wave range 120m diameter 100m 200m 100m Explorer Agent 100 [units], move a sec movement history 10 random movement 400m square Simulation (network model) 1 meter = 0.625 mile

  19. Simulation (subject) [ Performance Characteristics ] • Connectivity • Route length [ Compared Models ] • 1 entry per a destination as Miner’s model • 60 entries per a destination as the first model • 20 entries with 40 sub-entries for evaluation as the second model • the ideal model

  20. stable after 50 seconds the ideal model the 2nd proposal the 1st proposal Miner’s model getting worse over time Result (The average connectivity over time)

  21. getting worse over time the 1st model Miner’s model the 2nd model stable after 50 seconds the ideal model Result (The average route length over time)

  22. Result (average and standard deviation)

  23. approaching to the ideal Result (The average connectivity over agents) the 2nd model

  24. Contents • Related theme and paradigmsMANET environment, AntNet, Miner’s Model • Our proposalmultiplying entries, evaluating entries • Simulation and resulteffect of each model and formula • Conclusion

  25. Conclusion • We proposed ants’ path finding algorithm suitable for the MANET environment. • It was proved that our model was proper, because … • our model showed better performance than Miner’s model. • the more route information were gathered,the better routing performance was improved.

  26. Future Works • breed our model • compare our model

  27. Thank you very much! Please get our paper and other related materials at http://www.sail.t.u-tokyo.ac.jp/~ryo ryo@sail.t.u-tokyo.ac.jp

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