Published on Nov 23, 2015
Over the last two decades there has been such tremendous growth in the field of networks that it has paved a way for a wireless era from a wired one. Wireless networks find their applications in many fields such as in military, radio satellites, emergency operations, wireless mesh networks, wireless sensor network among a few. Ad-hoc networks keep changing dynamically, which results in disturbance of the network.
Hence a need arises to have seamless communication. Mobile Ad-hoc Network (MANET) is a type of Ad-hoc network with a self-organizing capability. They basically consist of mobile nodes which are connected to each other by wireless links. They do not have any fixed infrastructure or a centralized administration. During communication, nodes within the transmission range can have direct communication, but if that isn’t the case they have to communicate through intermediate nodes.
The term routing refers to the process of selecting paths in a computer network along which data is sent. This process is carried out by a routing protocol, used to exchange information about topology and link weights, and a routing algorithm, that computes paths between nodes. The routing protocols are divided into three categories.
Firstly the proactive protocols like DSDV, OLSR, reactive protocols like AODV and hybrid protocols like TORA, ZRP, and MPOLSR. Another most important type of protocols in recent times is the Bio-inspired protocols. Bio-inspired protocols are found to be capable of demonstrating self organizing behavior due to their robustness and efficiency; examples of such protocols are AntHocNet, BeeAdHoc, and ANSI.
This paper discusses the results of the experiments conducted on AntHocNet algorithm, whose design is based on a self-organizing behavior of ants, shortest path discovery and on Ant Colony Optimization. AntHocNet follows hybrid approach unlike other bio-inspired algorithms. While most of the previous bio-inspired algorithms were adopting a proactive scheme by periodically generating ant-like agents for all possible destinations, AntHocNet generates ants according to both proactive and reactive schemes.
The paper is organized as follows: In Chapter 2, we discuss the related works carried out in the area. Chapter 3 briefly describes the literature survey relevant to our work. Chapter 4 gives a brief discussion on system design and Implementation followed by Simulation results in Section V. Section V1 gives the conclusion and future enhancements possible.
It is based on the application of social behavior of insects and other animals to solve the problems of routing. Some of the Bio-inspired routing protocols are: AntHocNet, ARA (Ant-colony based Routing Algorithm), BeeAdHoc, ANSI (Ad hoc Networking with Swarm Intelligence), etc. Swarm Intelligence (SI) is an artificial bio-inspired intelligence technique based on the study of collective behavior in decentralized, self-organized systems. Since 1999, there is a great interest in applying swarm intelligence to solve hard static and dynamic optimization problems.
These problems are solved using cooperative agents that communicate with each other modifying their environment, like ant colonies or others insects do. This is the reason why these agents are commonly called ants.
Key characteristics of these models are:
Large numbers of simple agents.
Agents may communicate with each other directly.
Agents may communicate indirectly by affecting their environment, a process known as stigmergy.
Intelligence contained in the networks and communications between agents.
Local behavior of agents causes some emergent global behavior.
Ant routing is the result of using swarm-intelligence in systems for routing within communications networks. Ant Colony Optimization (ACO) is popular among other Swarm Intelligent Techniques.
The main idea behind ACO routing algorithms is that they gather routing information through repeated sampling of full paths using small control packets, which are called ants. This is in line with the behavior of ants in nature, where a large number of ants continuously move between their nest and the food source, and with the working of ACO algorithms for combinatorial optimization, where multiple artificial ants repeatedly and in parallel construct sample solutions for the problem at hand.
The ants are generated concurrently and independently by the nodes, with the task to test a path to an assigned destination. An ant going from source node ‘s’ to destination node ‘d’ collects information about the quality of the path and uses this on its way back from ‘d’ to ‘s’ to update the routing information at the intermediate nodes. Ants always sample complete paths, so that routing information can be updated.
The routing tables contain for each destination a vector of real-valued entries, one for each known neighbor node. These entries are a measure of the goodness of going over that neighbor on the way to the destination. They are termed pheromone variables, and are constantly updated according to path quality values calculated by the ants. The repeated and concurrent generation of path-sampling ants results in the availability at each node, a bundle of paths, each with an estimated measure of quality. In turn, the ants use the routing tables to define which path to their destination they sample: at each node they stochastically choose a next hop, giving higher probability to those links which are associated with higher pheromone values.
This pheromone information is used for routing data packets, more or less in the same way as for the routing of ants: all packets are routed stochastically, choosing with a higher probability those links associated with higher pheromone values. There are also some initiatives for ant-routing algorithms in ad hoc networks other than AntHocNet, ARA, and PERA among a few.
In case of wireless networks, AntHocNet is more efficient among all the considered ant based algorithms. This is because it has greater chance of exploring new paths based on probability. But it is costlier as more resources are required for implementing it. This is due to the fact that there is lot of ant traffic generated during the routing process.
AntHocNet is a multipath routing algorithm for mobile ad-hoc networks that combines both proactive and reactive components. It is based on AntNet, designed for wired networks, with some modifications to be used on ad-hoc networks. AntHocNet emerges as a reactive, adaptive, multipath and proactive algorithm (hybrid).
It is reactive because it has agents operating on-demand to set up routes to destinations. It does not maintain paths to all destinations at all times, but sets up paths when they are needed at the start of a session. This is done in a reactive path setup phase, where ant agents called reactive forward ants are launched by the source in order to find multiple paths to the destination, and backward ants return to set up the paths.
The paths are represented in pheromone tables indicating their respective quality. After path setup, data packets are routed stochastically as datagrams over the different paths using these pheromone tables. While the data session is open, paths are monitored, maintained and improved proactively using different agents, called proactive forward ants. The algorithm reacts to link failures with either a local route repair or by warning preceding nodes on the paths.
Simulator chosen: The simulator chosen to evaluate the two protocols is QualNet 5.0 as it offers a number of important advantages when compared to other simulators. Some of the features of QualNet are: it includes an extensive documentation and technical support, user-friendly tools, tools for building scenarios and analyzing simulation output. It offers large set of modules and protocols for both wired and wireless networks (local, Ad hoc, satellite and cellular).
The key to successful deployment of wireless networks in QualNet is its speed, scalability, accuracy and portability. QualNet offers highly detailed models of all aspects of networking. This ensures accurate modeling results. Scalability in QualNet is necessary for prediction of large network behavior of thousands of nodes. QualNet runs on all common platforms (Linux, Windows, and Solaris).
A feature-rich visual development environment offered by QualNet allows users to set up models quickly, efficiently code protocols and then run models that present real-time statistics. It also provides packet-level debugging insight.
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