A Multicast Genetic Routing Protocol Neural Network Approach

Multicast Zone Routing Protocol (GMZRP) is the most promising and widely accepted and well proved hybrid routing protocol in Mobile Ad-hoc Networks (MANETs) for its excellent results when compared in order to load balance the network. with table-driven demand protocols. Improvement of DPMRP is considered using GA and NN approach is considered in this paper. These enhanced protocols are compared with DPMRP and also with each other for same metrics. From the results it is concluded that MGRP (DPMRP), enhanced using GA approach, provides best results. This study is aimed to provide a set of available paths to the destination using the concept of genetic algorithm This gives us the reduction in the overhead, less jitter and better delivery of packets. We call this new routing protocol as Genetic Routing Protocol (GMZRP). Finally, the implementation of proposed genetic GMZRP is compared with Genetic GMZRP, i.e., GMZRP and the result demonstrates better performance from the proposed protocol. Since the method provides a set of paths from nodes to the destination, it results in load balance to the network. DOI: 10.17762/ijritcc2321-8169.15035

Ant-based Routing Schemes for Mobile Ad hoc Networks

An ad-hoc network is a collection of mobile nodes, which communicate over radio. These networks have an important advantage; they do not require any existing infrastructure or central administration. Therefore, mobile ad-hoc networks are suitable for temporary communication links. This flexibility, however, comes at a price: communication is difficult to organize due to frequent topology changes. Routing in such networks can be viewed as a distributed optimization problem. A new class of algorithms, inspired by swarm intelligence, is currently being developed that can potentially solve numerous problems of modern communications networks. These algorithms rely on the interaction of a multitude of simultaneously interacting agents. A survey of few such algorithms for ad hoc networks is presented here

ESAHR: Energy Efficient Swarm Adaptive Hybrid Routing Topology for Mobile Ad hoc Networks

Ad hoc networks consist of independent self structured nodes. Nodes use a wireless medium for exchange their message or data, therefore two nodes can converse directly if and only if they are within each other2019;s broadcast range. Swarm intelligence submits to complex behaviors that occur from very effortless individual activities and exchanges, which is frequently experienced in nature, especially amongst social insects such as ants. Although each individual (an ant) has little intelligence and simply follows basic rules using local information gained from the surroundings, for instance ant2019;s pheromone track arranging and following activities, globally optimized activities, such as discovering a shortest route, appear when they work together as a group. In this regard in our earlier work we proposed a biologically inspired metaphor based routing in mobile ad hoc networks that referred as Swarm Adaptive Hybrid Routing (SAHR). . With the motivation gained from SAHR, here in this paper we propose a energy efficient swarm adaptive hybrid routing topology (ESAHR). The goal is to improve transmission performance along with energy conservation that used for packet transmission In this paper we use our earlier proposed algorithm that inspired from Swarm Intelligence to obtain these characteristics. In an extensive set of simulation tests, we evaluate our routing algorithm with state-of-the-art algorithm, and demonstrate that it gets better performance over a wide range of diverse scenarios and for a number of different assessment measures. In particular, we show that it scales better in energy conservation with the number of nodes in the network

Mitigating Routing Misbehavior Using Ant-Tabu-Based Routing Algorithm for Wireless Ad-Hoc

Summary Routing is a key factor in the design of modern communication networks, especially in wireless ad-hoc networks (WANs). In WANs, both selfish and malicious nodes are misbehaving nodes and cause severely routing and security problems. Selfish nodes may drop routing and data packets and malicious nodes may redirect the packets to another routing path or launch denial-of-service (DoS) attacks. In this paper, an efficient routing algorithm is proposed, Ant-Tabu-Based Routing Algorithm (ATBRA), to mitigate selfish problem and reduce routing overheads. In ATBRA, both the concepts of ant-based routing algorithm and Tabu search are applied. We compare the performance of the proposed scheme with that of DSR in terms of two performance metrics: successful delivery rate (SDR) and routing overhead (RO). By comparisons, we notice that the proposed algorithm outperforms DSR in all two categories. The simulation results also indicate that the proposed algorithm is more efficient than DSR

Swarm intelligence for network routing optimization, Journal of Telecommunications and Information Technology, 2005, nr 3

This paper presents the results of a comparative study of network routing approaches. Recent advances in the field suggest that swarm intelligence may offer a robust, high quality solution. The overall aim of the study was to develop a framework to facilitate the empirical evaluation of a swarm intelligence routing approach compared to a conventional static and dynamic routing approach. This paper presents a framework for the simulation of computer networks, collection of performance statistics, generation and reuse of network topologies and traffic patterns