The performance of a hybrid routing intelligent algorithm in a mobile ad hoc network

Highlights

• Hybrid routing intelligent algorithm is proposed in this paper.

• The hybrid routing intelligent algorithm improves the end-to-end delay and power consumption.

• The proposed hybrid routing intelligent algorithm approach has a tendency to find the optimal route.

Abstract

End-to-end delay, power consumption, and communication cost are some of the most important metrics in a mobile ad hoc network (MANET) when routing from a source to a destination. Recent approaches using the swarm intelligence (SI) technique proved that the local interaction of several simple agents to meet a global goal has a significant impact on MANET routing. In this work, a hybrid routing intelligent algorithm that has an ant colony optimisation (ACO) algorithm and particle swarm optimisation (PSO) is used to improve the various metrics in MANET routing. The ACO algorithm uses mobile agents as ants to identify the most feasible and best path in a network. Additionally, the ACO algorithm helps to locate paths between two nodes in a network and provides input to the PSO technique, which is a metaheuristic approach in SI. The PSO finds the best solution for a particle’s position and velocity and minimises cost, power, and end-to-end delay. This hybrid routing intelligent algorithm has an improved performance when compared with the simple ACO algorithm in terms of delay, power consumption, and communication cost.

Introduction

A mobile ad hoc network (MANET) is a self-configuring infrastructureless network. Communication in MANETs is typically performed with the aid of temporary multi-hop relays, i.e., a source node uses its neighbouring node as a relay router. Routing is the act of moving information from a source to a destination across an inter-network. Routing protocols employ several important metrics to determine the best path for a packet to travel through. The process of path selection and directing packets from a source node to a destination node in a network is called routing and is an active area of research in ad hoc networks [1]. A metric is a standard for measuring parameters, such as distance, bandwidth, delay, and load for a path, and is used by routing algorithms to determine the optimal path to the destination [2].

An ad hoc network is a collection of nodes that are dynamically and arbitrarily located in such a manner that the interconnections between the nodes can change on a continual basis [3]. Currently, a major problem exists for routing protocols in ad hoc networks with wireless hosts. Numerous routing protocols are presently proposed for ad hoc networks [4].

In ad hoc networks, each node forwards its data to other nodes willingly. Each node communicates with other nodes within its transmission range [5], [6]. To send a packet to a destination, the node forwards the packet to its neighbouring node, which, in turn, forwards the received packet to its neighbour until the packet reaches the destination [6], [7]. Therefore, a wireless ad hoc network does not have a clear line of defence, and every node must be prepared for encounters with an adversary directly or indirectly [8].

Most destination/next hop relations inform the router that a particular destination can be reached optimally by sending the packet to a specific node on behalf of the “next hop” end route to the final destination [9]. When the router receives a packet, it checks the address of the destination node on its routing table. Based on the information in the routing table, the router forwards the packet to the next hop or destination. These networks include a combination of fixed wireless services and mobile networking [2]. In community networks lacking in hierarchically organised networks, several challenges arise [10]. The sender uses this route to transmit the packet if a route is identified. Additionally, the sender may attempt to find a route using the route discovery protocol if no route is found.

This paper is organised as follows: Section 2 presents a brief overview of other MANET routing strategies proposed in the literature. The basic idea of particle swarm optimisation is introduced in Section 3. Ant colony optimisation is discussed in Section 4. Section 5 explains the proposed hybrid routing intelligent algorithm model. Section 6 offers the performance evaluation and simulation results. Finally, conclusions are summarised in Section 7.