Computing Mobile Agent Routes with Node-Wise Constraints in Distributed Communication Systems

  • Amir Elalouf
  • Eugene Levner
  • T. C. Edwin Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)


A basic problem in the quality-of-service (QoS) analysis of multiagent distributed systems is to find optimal routes for the mobile agents that incrementally fuse the data as they visit hosts in the distributed system. The system is modeled as a directed acyclic graph in which the nodes represent hosts and the edges represent links between them. Each edge is assigned a cost (or benefit) and weights that represent link delay, reliability, or other QoS parameters. The agent scheduling problem is viewed as a constrained routing problem in which a maximum-benefit (or minimum-cost) route connecting the source and the destination subject to QoS constraints is to be found. We study approximation algorithms called ‘fully polynomial time approximation schemes’ (FPTAS) for solving the problem. We suggest an accelerating technique that improves known FPTAS, e.g., Hassin’s (1992); Camponogara & Shima’s (2010); and Elalouf et al. (2011) algorithms, and present new FPTASs.


Multi-agent distributed systems Mobile agent Agent routing Routing algorithm FPTAS Acceleration technique 


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  1. 1.
    Guilfoyle, C., Warner, E.: Intelligent Agents: New Revolution in Software, p. 214. Ovum Ltd. Publisher, London (1994)Google Scholar
  2. 2.
    Torsun, I.S.: Foundations of Intelligent Knowledge-Based Systems, p. 507. Academic Press, London (1995)Google Scholar
  3. 3.
    Jennings, N.R., Wooldridge, M.J.: Applications of Intelligent Agents. In: Jennings, N.R., Wooldridge, M.J. (eds.) Agent Technology: Foundations, Applications and Markets, pp. 3–28. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Shen, W., Norrie, D.H., Barthes, J.-P.: Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing, p. 386. Taylor and Francis, London (2001)Google Scholar
  5. 5.
    White, J.E.: Telescript Technology: The Foundation for the Electronic Marketplace, White Paper. General Magic, Inc., USA (1994)Google Scholar
  6. 6.
    Tsichritzis, D.: Objectworld, Office Automation. Springer, Heidelberg (1985)CrossRefzbMATHGoogle Scholar
  7. 7.
    Elalouf, A., Levner, E., Cheng, T.C.E.: Efficient Routing of Mobile Agents for Agent-based Integrated Enterprise Management: A General Acceleration Technique. LNBIP, vol. 88, pp. 1–20. Springer, Berlin (2011)Google Scholar
  8. 8.
    Papaioannou, T.: Using Mobile Agents to Improve the Alignment between Manufacturing and its IT Support Systems. In: Robotics and Autonomous Systems. Elsevier (1999)Google Scholar
  9. 9.
    Peng, Y., Finin, T., Labrou, Y., Chu, B., Long, J., Tolone, X., Boughannam, A.: A Multi-Agent System for Enterprise Integration. In: Proceedings of PAAM 1998, London, UK, pp. 155–169 (1998)Google Scholar
  10. 10.
    Qi, H., Iyengar, S.S., Chakrabarty, K.: Multi-Resolution Data Integration Using Mobile Agents in Distributed Sensor Networks. IEEE Trans. Systems, Man, and Cybernetics Part C: Applications and Rev. 31(3), 383–391 (2001)CrossRefGoogle Scholar
  11. 11.
    Wu, Q., Rao, N.S.V., Barhen, J., Iyengar, S.S., Vaishnavi, V.K., Qi, H., Chakrabarty, K.: On Computing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks. IEEE Transactions on Knowledge and Data Engineering 16(6), 740–753 (2004)CrossRefGoogle Scholar
  12. 12.
    Gens, G.V., Levner, E.V.: Fast Approximation Algorithms for Job Sequencing with Deadlines. Discrete Applied Mathematics 3, 313–318 (1981)CrossRefzbMATHGoogle Scholar
  13. 13.
    Gens, G.V., Levner, E.V.: Fast Approximation Algorithms for Knapsack Type Problems. LNCIS, vol. 23. Springer, Berlin (1980)zbMATHGoogle Scholar
  14. 14.
    Hassin, R.: Approximation Schemes for the Restricted Shortest Path Problem. Mathematics of Operations Research 17(1), 36–42 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Goel, A., Ramakrishnan, K.G., Kataria, D., Logothetis, D.: Efficient Computation of Delay-sensitive Routes from One Source to All Destinations. In: IEEE Infocom 2001, pp. 854–858. IEEE Press (2001)Google Scholar
  16. 16.
    Xue, G., Sen, A., Zhang, W., Tang, J., Thulasiraman, K.: Finding a Path Subject to Many Additive QoS Constraints. IEEE Transactions on Networking 15, 201–211 (2007)CrossRefGoogle Scholar
  17. 17.
    Lorenz, D.H., Raz, D.: A Simple Efficient Approximation Scheme for the Restricted Shortest Path problem. Operations Research Letters 28(5), 213–219 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ergun, F., Sinha, R., Zhang, L.: An Improved FPTAS for Restricted Shortest Path. Information Processing Letters 83(5), 287–291 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Camponogara, E., Shima, R.B.: Mobile Agent Routing with Time Constraints: A Resource Constrained Longest-Path Approach. Journal of Universal Computer Science 16(3), 372–401 (2010)zbMATHGoogle Scholar
  20. 20.
    Sahni, S.: Algorithms for Scheduling Independent Tasks. Journal of the ACM 23(1), 116–127 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, ch. 24.2. MIT Press (2001)Google Scholar
  22. 22.
    Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows. Prentice Hall, New Jersey (1993)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amir Elalouf
    • 1
  • Eugene Levner
    • 1
    • 2
  • T. C. Edwin Cheng
    • 3
  1. 1.Bar Ilan UniversityRamat GanIsrael
  2. 2.Ashkelon Academic CollegeAshkelonIsrael
  3. 3.The Hong Kong Polytechnic UniversityHong Kong

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