Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks
ABSTRACT Due to mobility of wireless hosts, routing in mobile ad-hoc networks (MANETs) is a challenging task. Multipath routing is
employed to provide reliable communication, load balancing, and improving quality of service of MANETs. Multiple paths are
selected to be node-disjoint or link-disjoint to improve transmission reliability. However, selecting an optimal disjoint
multipath set is an NP-complete problem. Neural networks are powerful tools for a wide variety of combinatorial optimization
problems. In this study, a transient chaotic neural network (TCNN) is presented as multipath routing algorithm in MANETs.
Each node in the network can be equipped with a neural network, and all the network nodes can be trained and used to obtain
optimal or sub-optimal high reliable disjoint paths. This algorithm can find both node-disjoint and link-disjoint paths with
no extra overhead. The simulation results show that the proposed method can find the high reliable disjoint path set in MANETs.
In this paper, the performance of the proposed algorithm is compared to the shortest path algorithm, disjoint path set selection
protocol algorithm, and Hopfield neural network (HNN)-based model. Experimental results show that the disjoint path set reliability
of the proposed algorithm is up to 4.5times more than the shortest path reliability. Also, the proposed algorithm has better
performance in both reliability and the number of paths and shows up to 56% improvement in path set reliability and up to
20% improvement in the number of paths in the path set. The proposed TCNN-based algorithm also selects more reliable paths
as compared to HNN-based algorithm in less number of iterations.
KeywordsTransient chaotic neural network–Mobile ad-hoc network–Disjoint multipath routing–Reliability
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ABSTRACT: The main contribution of this paper is to propose a nonlinear robust controller to synchronize general chaotic systems, such that the controller does not need the information of the chaotic system’s model. Following this purpose, in this paper, two methods are proposed to synchronize general forms of chaotic systems with application in secure communication. The first method uses radial basis function neural network (RBFNN) as a controller. All the parameters of the RBFNN are derived and optimized via particle swarm optimization (PSO) algorithm and genetic algorithm (GA). In order to increase the robustness of the controller, in the second method, an integral term is added to the RBF neural network gives an integral RBFNN (IRBFNN). The coefficients of the integral term and the parameters of IRBFNN are also derived and optimized via PSO and GA. The proposed methods are applied to the famous Lorenz chaotic system for secure communication. The performance and control effort of the proposed methods are compared with the recently proposed PID controller optimized via GA. Simulation results show the superiority of the proposed methods in comparison to the recent one in improving synchronization while using smaller control effort. KeywordsSecure communication–Synchronization–Chaotic systems–Neural controllerNeural Computing and Applications 01/2011; · 1.76 Impact Factor