Mobile Ad hoc Network (MANET) is an
autonomous, self-configuring and infrastructure-less
system in which various mobile nodes are connected by
wireless links. In MANETs hello messages are periodically
exchanged to maintain the connectivity of neighbour nodes.
While discovering neighbour nodes, an unnecessary hello
message causes the problem of battery drainage in
MANET routing protocols like AODV and DYMO. These
ad-hoc types of networks are mainly used in the smart
phones and origin the problem of energy utilization when
neighbour nodes are discovered to maintain the
connectivity. For the MANET scenario we take Random
Waypoint Model and also the relationship between hello
interval and event interval is considered. In this paper both
the protocols are made adaptive and then Particle Swarm
Optimization algorithm (PSO) is employed to give better
results by reducing energy consumption and network
overhead.
Kamaldeep Kaur : Student, Chandigarh University
Department of Computer Science and Engineering, Gharuan, India
Lokesh Pawar : Assistant Professor, Chandigarh University
Department of Computer Science and Engineering, Gharuan, India
Routing
Hello Messaging
MANET
Optimization Algorithms
So here in this paper, we optimize an adaptive Hello
messaging scheme with Particle swarm optimization
technique to practically suppress the unnecessary Hello
messages and to reduce the battery drainage problem. By
this optimization scheme the difficulties related to
battery utilization and network overhead are solved.
These are the significant problems that influence the
MANETs performance. For the future work, the
proposed scheme should be deployed in various
scenarios and also in the large scale networks. The value
of hello interval should be optimized using more
different optimization techniques.
[1] Chalamtac, M. Conti, and J. J. Liu. Mobile ad hoc
networking: Imperatives and challenges. Ad Hoc
Networks, 1(1):13–64, 2003. Cited By (since
1996):376.
[2] S. Corson, J. Macker., “Mobile Ad hoc Networking
(MANET): Routing Protocol Performance Issues and
Evaluation Considerations,” IETF RFC2501, 1999.
[3] Seon Yeong Han, and Dongman Lee “An Adaptive
Hello Messaging Scheme for Neighbor Discovery in
On-Demand MANET Routing Protocols” IEEE
Communications Letters, VOL. 17, NO. 5, MAY
2013.
[4] T. Clausen, C. Dearlove, and J. Dean, “Mobile ad
hoc network (MANET) neighborhood discovery
protocol (NHDP),” 2010.
[5] David B. Johnson, David A. Maltz, Yih-Chun Hu
and Jorjeta G. Jetcheva, “The Dynamic Source
Routing for Mobile Ad Hoc Wireless Networks, “
July 2004.
[6] Dimitri Marandin, “Performance Evaluation of
Failed Link Detection in Mobile Ad Hoc Networks,”
3rd Annual Med-Hoc-Net, 2004.
[7] V. C. Giruka and M. Singhal, “Hello protocols for
ad-hoc networks: overhead and accuracy tradeoffs,”
in Proc.Sixth IEEE International Symposium on a
World of Wireless Mobile and Multimedia
Networks, pp. 354–361.
[8] Ian D. Chakeres, Elizabeth M. Belding-Royer, “The
Utility of Hello Messages for Determining
LinkConnectivity” in Proc. 2002 Wireless Personal
Multimedia Communications, 2002. The 5th
International Symposium.
[9] R. Oliveira, M. Luis, L. Bernardo, R. Dinis, and P.
Pinto, “The impact of node’s mobility on linkdetection
based on routing hello messages,” in Proc.
2010 IEEE Wireless Communications and
Networpsking Conference, pp. 1–6.
[10] Ehsan Mostajerani, Rafidah Md Noor and Hassan
Keshavarz, “A Novel Improved Neighbor Discovery
Method for an Intelligent-AODV in Mobile Ad hoc
Networks,” International Conference of Information
and Communication Technology, 2013.
[11] V. D. Tracy Camp, Jeff Boleng, “A survey of
mobility models for ad hoc network research,”
Wireless Communications and Mobile Computing,
2:483–502, 2002.
[12] E. Belding-Royer and S. D. C. Perkins, “Ad hoc ondemand
distance vector (AODV) routing,” July
2003.
[13] R.C. Eberhart and Y. Shi. Particle swarm
optimization: developments, applications and
resources. In Proceedings of the 2001 conference on
evolutionary computation, volume 1, pages 81-86.
Piscataway, NJ, USA: IEEE, 2001.
[14] J. Kennedy, R.C. Eberhart, et al. Particle swarm
optimization. In Proceedings of IEEE international
conference on neural networks, volume 4, pages
1942-1948. Perth, Australia, 1995.
[15] Y. Shi and R. Eberhart, “A modi_ed particle swarm
optimizer” In Evolutionary Computation
Proceedings, 1998. IEEE World Congress on
Computational Intelligence., The 1998 IEEE
International Conference on, pages 69-73. IEEE,
2002.