Performance Analysis of Swarm Intelligence-Based Routing Protocol for Mobile Ad Hoc Network and Wireless Mesh Networks

Ant colonies reside in social insect societies and maintain distributed systems that present a highly structured social organization despite of the simplicity of their individuals. Ants’ algorithm belongs to the Swarm Intelligence (SI), which is proposed to find the shortest path. Among various works inspired by ant colonies, the Ant Colony Optimization (ACO) metaheuristic algorithms are the most successful and popular, e.g., AntNet, Multiple Ant Colony Optimization (MACO) and AntHocNet. But there are several shortcomings including the freezing problem of the optimum path, traffic engineering, and to link failure due to nodes mobility in wireless mobile networks. The metaheuristic and distributed route discovery for data load management in Wireless Mesh Networks (WMNs) and Mobile Ad-hoc Network (MANET) are fundamental targets of this study. Also the main aim of this research is to solve the freezing problem during optimum as well as sub-optimum path discovery process. In this research, Intelligent AntNet based Routing Algorithm (IANRA) is presented for routing in WMNs and MANET to find optimum and near-optimum paths for data packet routing. In IANRA, a source node reactively sets up a path to a destination node at the beginning of each communication. This procedure uses ant-like agents to discover optimum and alternative paths. The fundamental point in IANRA is to find optimum and sub-optimum routes by the capability of breeding of ants. This ability is continuation of route that was produced by the parent ants. The new generations of ants inherit identifier of their family, the generation number, and the routing information that their parents get during their routing procedure. By this procedure, IANRA is able to prevent some of the existing difficulties in AntNet, MACO and Ad hoc On Demand Distance Vector (AODV) routing algorithms. OMNeT++ was used to simulate the IARNA algorithm for WMNs and MANET. The results show that the IANRA routing algorithm improved the data packet delivery ratio for both WMNs and MANET. Besides, it is able to decrease average end-to-end packet delay compared to other algorithms by showing its efficiency. IANRA has decreased average end-to-end packet delay by 31.16%, 58.20% and 48.40% in MANET scenario 52.86%, 64.52% and 62.86% by increasing packet generation rate in WMNs compared to AntHocNet, AODV and B-AntNet routing algorithms respectively with increased network load. On the other hand, IANRA shows the packet delivery ratio of 91.96% and 82.77% in MANET, 97.31% and 92.25% in WMNs for low (1 packet/s) and high (20 packet/s) data load respectively

Ant colony optimization routing mechanisms with bandwidth sensing

The study and understanding of the social behavior of insects has contributed to the definition of some algorithms that are capable of solving several types of optimization problems. In 1997 Di Caro and Dorigo developed the first routing algorithm for wired networks, called AntNet, using an approach which was inspired in the behavior of ant colonies. At each node, AntNet, similar to others Ant Colony Optimization (ACO) based algorithms, forward ants based in the amount of pheromones present in the links and in response to the node's queue lengths. In this paper, an adaptation of the e-DANTE algorithm for discrete problems, as an IP based routing mechanism, was implemented. We also propose the inclusion of a new parameter for the computation of paths for both the AntNet and the newly proposed algorithm: the available bandwith. Those methods were tested in ns-2 using two dense network architectures and their efficiency is compared with the original AntNet and a Link-State routing algorithm, when considering the transmission of competing traffic flows between distinct nodes. © 2011 IEEE

Prediction-based Decentralized Routing Algorithm

We introduce a new efficient routing algorithm called Prediction-based Decentralized Routing algorithm (PDR), which is based on the Ant Colony Optimization (ACO) meta-heuristics. In our approach, an ant uses a combination of the link state information and the predicted link load instead of the ant's trip time to determine the amount of pheromone to deposit. A Feed Forward Neural Network (FFNN) is used to build adaptive traffic predictors which capture the actual traffic behaviour. We study two performance parameters: the rejection ratio and the percentage of accepted bandwidth under two different network load conditions. We show that our algorithm reduces the rejection ratio of requests and achieves a higher throughput when compared to Shortest Path First and Widest Shortest Path algorithms