PSO based route lifetime prediction algorithm for maximizing network lifetime in MANET

In MANET, this exhaustion of energy will be more due to its infrastructureless nature and mobility. Due to this, the topology get varied. This may drastically affect the performance of routing protocol and also affect the network lifetime. Several researches have gone so far for predicting node lifetime and link lifetime. To address this problem a new algorithm has been developed which utilizes the network parameters relating to dynamic nature of nodes viz. energy drain rate, relative mobility estimation to predict the node lifetime and link lifetime. Then implement this algorithm in the DYMO protocol environment. This will maximize the network lifetime and scalability. Further improve the performance, we have implemented a new algorithm by integrating route lifetime prediction algorithm along with the particle swarm optimization (PSO) algorithm. Since PSO uses for network centric localization purpose, this approach generates in-network navigational decisions by obviating centralized control thereby reducing both the congestion and delay.

[1]  Satish K. Tripathi,et al.  Signal stability-based adaptive routing (SSA) for ad hoc mobile networks , 1997, IEEE Wirel. Commun..

[2]  A. Massa,et al.  An innovative computational approach based on a particle swarm strategy for adaptive phased-arrays control , 2006, IEEE Transactions on Antennas and Propagation.

[3]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[4]  Christos G. Cassandras,et al.  Sensor Networks and Cooperative Control , 2005, CDC 2005.

[5]  Chai-Keong Toh,et al.  Associativity-Based Routing for Ad Hoc Mobile Networks , 1997, Wirel. Pers. Commun..

[6]  Bernard Mans,et al.  Exploiting Overhearing: Flow-Aware Routing for Improved Lifetime in Ad Hoc Networks , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[7]  Russell C. Eberhart,et al.  Recent advances in particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  M. Salazar-Palma,et al.  A survey of various propagation models for mobile communication , 2003 .

[9]  J. J. Garcia-Luna-Aceves.  An Analytical Framework for The Characterization of Link Dynamics in MANETs - eScholarship , 2006 .

[10]  Vijay Kumar,et al.  Robot and sensor networks for first responders , 2004, IEEE Pervasive Computing.

[11]  H.R. Sadjadpour,et al.  An Analytical Framework for The Charaterization of Link Dynamics in MANETs Invited Paper , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[12]  Chen Guoliang,et al.  Longest Lifetime Path in Mobile Ad Hoc Networks , 2006 .

[13]  Chai-Keong Toh.  Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc networks , 2001 .

[14]  Karthik Dantu,et al.  Lifetime prediction routing in mobile ad hoc networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[15]  Archan Misra,et al.  MRPC: maximizing network lifetime for reliable routing in wireless environments , 2002, 2002 IEEE Wireless Communications and Networking Conference Record. WCNC 2002 (Cat. No.02TH8609).