Wireless ad-hoc networks are infrastructureless and they consist of nodes that come together and start communicating dynamically without requiring any backbone support. The nodes can enter and leave the network at will and can move about in the network at will. Ad-hoc networks present the perfect test-beds for bio-inspired computing algorithms. Both ad-hoc networks and bio-inspired computing approaches are characterized by self-organization, feedback and structural and functional complexity (Toh, 2002) (deCastro & Von Zuben, 2005). Hence, bio-inspired algorithms often provide us an opportunity to solve the most complex problems of ad-hoc networks in a satisfactory manner. In this chapter, we present the works done in the field of ad-hoc networks using bio-inspired Swarm Intelligence (SI). In particular, we look at how we can use Ant Colony Optimization (ACO) technique, a SI technique, for optimal routing in ad-hoc networks.
Social insects such as ants, bees, wasps and termites and organisms such as fishes and birds rely on local communication to achieve distributed control. While insects such as ants, bees and termites rely on indirect communication through environment (also often referred to in the literature as Stigmergy), birds are dependent on direct but localised communication.
Key Terms in this Chapter
Swarm Intelligence: Group of bio-inspired algorithms which is modelled on the collective behaviour of a group of social organisms such as ants, termites, bees, fishes and birds.
Reactive Forward Ant: Ant agents responsible for discovering paths to the destination nodes.
Stigmergy: Method of indirect communication between simple agents by altering their environment. Ants use a chemical called Pheromone to communicate with each other, which is an example of stigmergy.
Proactive Forward Ant: Control ant agents that are unicast to destination node and are responsible for finding fresh information about existing routes or to find fresh nodes to the destinations.
Pheromone: Chemical secreted by natural ants, the presence of which is an indicative of the number of ants that have followed a particular path. This chemical is modelled to represent the historical preference that is associated with a path in Ant Colony Optimization.
Heuristic Information: Static value associated with a solution that represents the relative suitability of a solution among its peers based on intuition, previous experience or common sense.
AntHocNet: An Ant Colony Optimization-based algorithm for routing in ad-hoc networks using reactive route set up combined with proactive route probing, maintenance and improvement.
Ant Colony Optimization: Ant Colony Optimization involves a set of algorithms modelled on the foraging behaviour of a colony of natural ants.