Skip to main content
SpringerLink
Log in

Using ant-based agents for congestion control in ad-hoc wireless sensor networks

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Ad-hoc wireless sensor networks suffer from problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address the issue of congestion in these networks. We propose a new routing protocol for wireless sensor networks namely Ant-based Routing with Congestion Control (ARCC), which takes into account the congestion of the network at a given instant and proposes to reduce it and then finds the optimum paths between the source and the sink nodes. Simulation results show that ARCC performs better with respect to the throughput, the number of packets lost and the priority performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.: Wireless sensor networks for habitat monitoring. In: Proc. of the ACM International Workshop on Wireless Sensor Networks and Applications, 2002

  2. Min, R., Bhardwaj, M., Cho, S., Shih, E., Sinha, A., Wang, A., Chandrakasan, A.: Low-power wireless sensor networks. In: Proc. of the 14th International Conference on VLSI Design, Bangalore, India, January 2001

  3. Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on wireless sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)

    Article  Google Scholar 

  4. Raghunathan, V., Schurgers, C., Park, S., Srivastava, M.B.: Energy-aware wireless microsensor networks. IEEE Signal Process. Mag. 19(2), 40 (2002) C50

    Article  Google Scholar 

  5. Misra, S., Woungang, I., Misra, S.C. (eds): Guide to Wireless Sensor Networks. Computer Communications and Networks Series. Springer, London (2009), 809 p. ISBN-10: 1848822170, ISBN-13: 978-1848822177

    Google Scholar 

  6. Narula, P., Misra, S., Dhurandher, S.K.: Swarm intelligence approach for ad-hoc networks. In: Rabucal, J.R., Dorado, J., Pazos, A. (eds.) Encyclopedia of Artificial Intelligence. Information Sciences Reference (formerly Idea Group Reference), pp. 1530–1536. Hershey, New York (2008)

    Google Scholar 

  7. Dhurandher, S.K., Misra, S., Dhawan, A., Tiwari, A.: Efficient solutions to various routing issues involved in mobile ad-hoc bio-sensor networks: applying appropriate motion trajectories. IET Commun. J. 3, 830–845 (2009)

    Article  Google Scholar 

  8. Woo, A., Culler, D.C.: A transmission control scheme and media access in sensor network. In: Proceedings of ACM Mobicom’01, Rome, Italy, July 16–21, 2004

  9. Wan, C.Y., Eisenman, S.B., Campbell, A.T.: CODA: congestion detection and avoidance in sensor networks. In: Proceedings of ACM Sensys’03, Los Angeles, CA, Nov. 5–7, 2003, pp. 266–279

  10. Wang, C., Li, B., Sohraby, K., Daneshmand, M., Hu, Y.: Upstream congestion control in wireless sensor networks through cross-layer optimization. IEEE J. Sel. Areas Commun. 25, 786–795 (2007)

    Article  Google Scholar 

  11. Ee, C.T., Bajecsy, R.: Congestion Control and Fairness (CCF) for many-to-one routing in sensor networks. In: Proc. ACM Sensys, Nov. 2004

  12. Hull, B., Jamesion, K., Balakrishnan, H.: Mitigating congestion in wireless sensor networks. In: Proceedings of ACM Sensys’04, Baltimore, MD, Nov. 3–5, 2004, pp. 134–147

  13. Levis, P., Patel, N., Culler, D., Shanker, S.: Trickle: a self regulating algorithm for zcode propagation and maintenance in wireless sensor networks. In: Proceedings of 1st Symposium on Networked Systems Design and Implementation, San Francisco, CA, Mar. 29–31, 2004, p. 2

  14. Wan, C.Y., Eisenman, S.B., Campbell, A.T., Crowcroft, J.: Siphon: overhead traffic management using multi radio virtual sinks in sensor networks. In: Proceedings of ACM Sensys’05, San Diego, CA, USA, Nov. 2–4, 2005, pp. 116–129

  15. Misra, S., Tiwari, V., Obaidat, M.S.: LACAS: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. IEEE J. Sel. Areas Commun. 27(4), 466–479 (2009)

    Article  Google Scholar 

  16. Dorigo, M., Colorni, A., Maniezzo, V.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26(1), 1–13 (1996)

    Article  Google Scholar 

  17. Dorigo, M., Caro, G.D., Gamberdella, L.M.: Ant algorithm for discrete optimization. Artif. Life 5(2) (1999)

  18. Di Caro, G., Dorigo, M.: AntNet: distributed stigmergetic control for communications networks. J. Artif. Intell. Res. (JAIR) 9, 317–365 (1998)

    MATH  Google Scholar 

  19. Dhurandher, S.K., Misra, S., Obaidat, M.S., Gupta, N.: An ant colony optimization approach for reputation and quality-of-service-based security in wireless sensor networks. Secur. Commun. Netw. 2(2), 215–224 (2009)

    Article  Google Scholar 

  20. Chandrasekar, R., Misra, S., Obaidat, M.S.: FORK: a novel two-pronged strategy for an agent-based intrusion detection scheme in ad-hoc networks. Comput. Commun. 31(16), 3855–3869 (2008)

    Article  Google Scholar 

  21. Chandrasekar, R., Misra, S., Obaidat, M.S.: A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks. Int. J. Commun. Syst. 21(10), 1047–1073 (2008)

    Article  Google Scholar 

  22. Misra, S., Dhurandher, S.K., Obaidat, M.S., Verma, K., Gupta, P.: A low overhead fault-tolerant routing algorithm for mobile ad-hoc networks based on ant swarm intelligence. In: Proceedings of the IEEE International Conference on Communications (IEEE ICC 2009), Dresden, Germany, 2009

  23. Dhurandher, S.K., Misra, S., Obaidat, M.S., Gupta, P., Verma, K.: An energy-aware routing protocol for ad-hoc networks based on the foraging behavior in ant swarms. In: Proceedings of the IEEE International Conference on Communications (IEEE ICC 2009), Dresden, Germany, 2009

  24. Gwalini, S., Belding-Royer, E., Perkins, C.: AODV-PA: AODV with path accumulation. In: IEEE International Conference on Communications (ICC), vol. 1, pp. 527–531 (2003)

  25. Misra, R., Mandal, C.R.: AODV-DSR on-demand routing protocols for ad hoc networks in constraint situations. In: IEEE Conference on Personal Wireless Communications (ICPWC), Jan. 2005, pp. 86–89

  26. Perkins, C.E., Belding-Royer, E.M., Chakeres, I.: Ad hoc on demand distance vector (AODV) routing. IETF Internet draft (Oct. 2003)

  27. Perkins, C.E., Belding-Royer, E.M., Das, S.: Ad hoc on demand distance vector (AODV) routing. IETF RFC 3561

  28. Perkins, C.E., Royer, E.M., Das, S.: Ad hoc on demand distance vector (AODV) routing for IPv6. IETF Internet draft

  29. Chakeres, I.D., Belding-Royer, E.M.: AODV routing protocol implementation design. In: Proceedings of the International Workshop on Wireless Ad Hoc Networking (WWAN), Tokyo, Japan, March 2004

  30. Royer, E.M., Perkins, C.E.: An implementation study of the AODV routing protocol. In: Proceedings of the IEEE Wireless Communications and Networking Conference, Chicago, IL, September 2000

  31. Johnson, D.B., Maltz, D.A., Broch, J.: DSR: the dynamic source routing protocol for multi-hop wireless ad hoc networks, Chap. 5, pp. 139–172 (2001)

  32. Johnson, D.B., Maltz, D.A.: Dynamic source routing in ad hoc wireless networks. In: Imielinski, T., Korth, H. (eds.) Mobile Computing, Chap. 5, pp. 153–181. Kluwer Academic, Dordrecht (1996)

    Chapter  Google Scholar 

  33. Li, Y., Ma, Z., Cao, Z.: A mitigating stagnation-based ant colony optimization routing algorithm. In: Proceeding of ISCIT 2005, vol. 1, pp. 36–39 (2005)

  34. GloMoSim: Global Mobile Information Systems Simulation Library. http://pcl.cs.ucla.edu/projects/glomosim/ (2009)

  35. Zeng, X., Bagrodia, R., Gerla, M.: GloMoSim: a library for parallel simulation of large-scale wireless networks. In: Twelfth Workshop on Parallel and Distributed Simulation, May 1998, pp. 154–161

Download references

Author information

Authors and Affiliations

  1. CAITFS, Division of Information Technology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India

    Sanjay K. Dhurandher & Anubhav Agarwal

  2. School of Information Technology, Indian Institute of Technology, Kharagpur, West Bengal, India

    Sudip Misra

  3. Division of Computer Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India

    Harsh Mittal

  4. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

Authors
  1. Sanjay K. Dhurandher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudip Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harsh Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anubhav Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Isaac Woungang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjay K. Dhurandher.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dhurandher, S.K., Misra, S., Mittal, H. et al. Using ant-based agents for congestion control in ad-hoc wireless sensor networks. Cluster Comput 14, 41–53 (2011). https://doi.org/10.1007/s10586-009-0090-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-009-0090-2

Keywords

Search

Navigation