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

2. Contents Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Our proposal multiplying entries, evaluating entries Our proposal multiplying entries, evaluating entries Simulation and result effect of each model and formula Simulation and result effect of each model and formula Conclusion Conclusion

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

4. Home Network source destination think solution of MANET environment obstacle short range

5. Road Network short range traffic jam think solution of MANET environment

6. MANET environment An epoch-making way of wireless communication multi-hop wirelessad-hoc peer-to-peerautonomous

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

8. Use mobile agent as control packet Advantages Advantages It is easy to install new routing system. It is easy to install new routing system. Heterogeneous networks are linked together. Heterogeneous networks are linked together. Agents release resources after they left. Agents release resources after they left. Disadvantages Disadvantages It is easy for malicious hosts to attack agents. It is easy for malicious hosts to attack agents. Agents are interpreted slowly and gain weight. Agents are interpreted slowly and gain weight.

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

10. AntNet Ants follow & deposit pheromone trails. Ants follow & deposit pheromone trails. pheromone pheromone frequency Pheromone trails are piled up on the ground. frequency Pheromone trails are piled up on the ground. quantity The rich food is, the more ants deposit. quantity The rich food is, the more ants deposit. freshness pheromone evaporates along time. freshness pheromone evaporates along time. Agent-based algorithm for wired network

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

12. About our model algorithm (mind) algorithm (mind) Making good routes in a sense of probability by ants ’ path-finding model Making good routes in a sense of probability by ants ’ path-finding model framework (body) framework (body) A simple decentralized management by multi-agent system A simple decentralized management by multi-agent system

13. Contents Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Our proposal multiplying entries, evaluating entries Our proposal multiplying entries, evaluating entries Simulation and result effect of each model and formula Simulation and result effect of each model and formula Conclusion Conclusion

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

15. Multiply entries (table example) Pheromone trails are piled up on the ground. Pheromone trails are piled up on the ground. More route information from EAs are held in the routing tables. More route information from EAs are held in the routing tables. destnext NA OB Pnull QA RC C A null D A next C A null C A next C B null C D next CR AQ nullP BO AN nextdest multiplied up to 4 entries new old old a single entry

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

17. Evaluate entries (table example) two attached sub-entries time time the number of hops the number of hops destnextnextnextnext NADAA OBCCD Pnullnullnullnull QABAA RCCCC destnextnextnextnexttimehopstimehopstimehopstimehops NADAA 283266253174 OBCCD 229213152123 Pnullnullnullnull nullnullnullnullnullnullnullnull QABAA 28132622011162 RCCCC 302252203102

18. Evaluate entries (the way of evaluation) D R h-1 R3R3R3R3 R2R2R2R2 R1R1R1R1S destination node Explorer Agent The total reliability source node p : the broken-link ratio a time p : the broken-link ratio a time t : the time since info. gotten t : the time since info. gotten h : #hops to the destination h : #hops to the destination h EA : #hops EAs move a time h EA : #hops EAs move a time

19. Evaluate entries (the way of evaluation) D R h-1 R3R3R3R3 R2R2R2R2 R1R1R1R1S destination node Explorer Agent The total reliability source node p : the broken-link ratio a time p : the broken-link ratio a time t : the time since info. gotten t : the time since info. gotten h : #hops to the destination h : #hops to the destination h EA : #hops EAs move a time h EA : #hops EAs move a time

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

21. Contents Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Our proposal multiplying entries, evaluating entries Our proposal multiplying entries, evaluating entries Simulation and result effect of each model and formula Simulation and result effect of each model and formula Conclusion Conclusion

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

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

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

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

26. Result (average and standard deviation) Model Connectivity Route length Average Std Dev Average Miner ’ s model 66%9%2.80.4 the 1st model 83%7%3.20.5 the 2nd model 93%5%2.70.3 the ideal model 98%2%2.10.2

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

28. Result (The average route length over agents) approaching to the ideal the 2 nd model

29. Contents Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Our proposal multiplying entries, evaluating entries Our proposal multiplying entries, evaluating entries Simulation and result effect of each model and formula Simulation and result effect of each model and formula Conclusion Conclusion

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

31. Future Works breed our model breed our model compare our model compare our model

32. 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