Abstract
Application of mobile ad hoc networks (MANETs) has gained significant popularity among researchers in the field of data communication networks. However, a MANET operating in a wireless environment imposes a number of challenges for the implementers so far as routing of packets across it is concerned. There is a wide range of research contributions are available in the literature wherein authors propose various solutions to overcome the problems and bottleneck related to routing in MANET. Especially soft computing techniques and Ant Colony Optimization (ACO) in particular has been significantly popular among the researchers to resolve MANET routing issues. This technique plays a vital role in route discovery in particular. In this paper, we have conducted a comprehensive review of this technique applied to routing in MANET with respect to various criteria. Hopefully this paper serves to a perfect document for researchers in this field.
Highlights
Mobile Ad Hoc Networks (MANETs) have found wide implementation in real world applications that present a series of challenges to successful implementation of it
During the route discovery process, first of all, source sends the Message Digest (MD) of request ant (RQANT) that is obtained as a one way hash function, and it is attached to a Digital Signature (DS) which is generated making use of a pseudonymous certificate obtained from the Certification Authority (CA) along with a secret signing key of the node
The main focus is given in this work at the research contributions of authors that use Ant Colony Optimization (ACO) technique in order for resolution of routing issues in mobile ad hoc networks (MANETs) and in particular the route discovery process at the beginning of communication between two nodes in the network
Summary
Mobile Ad Hoc Networks (MANETs) have found wide implementation in real world applications that present a series of challenges to successful implementation of it. Ant Colony Optimization ACO) based routing protocols appear to be more efficient in comparison with other approaches. ACO as an Artificial Intelligence (AI) based search technique mostly relies on the past search results in order to find a path between the desired source and destination nodes in a MANET. Artificial ants being used for this purpose may lead to reinforcement that may result in a premature convergence of the path finding procedure which can be overcome using randomization techniques in order for decision making during route discovery [4, 5]. Out of multiple paths available between the source and destination nodes, the optimal one can be chosen with respect to the pheromone value
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