AntMeshNet: An Ant Colony Optimization Based Routing Approach to Wireless Mesh Networks

Sharad Sharma, Shakti Kumar and Brahmjit Singh
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Sharad Sharma: Department of Electronics & Communications Engineering, National Institute of Technology, Kurukshetra, Haryana, India
Shakti Kumar: Computational Intelligence (CI) Lab, IST Klawad, Yamunanagar, Haryana, India
Brahmjit Singh: Department of Electronics & Communications Engineering, National Institute of Technology, Kurukshetra, Haryana, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2014, vol. 5, issue 1, 20-45

Abstract: Wireless Mesh Networks (WMNs) are emerging as evolutionary self organizing networks to provide connectivity to end users. Efficient Routing in WMNs is a highly challenging problem due to existence of stochastically changing network environments. Routing strategies must be dynamically adaptive and evolve in a decentralized, self organizing and fault tolerant way to meet the needs of this changing environment inherent in WMNs. Conventional routing paradigms establishing exact shortest path between a source-terminal node pair perform poorly under the constraints imposed by dynamic network conditions. In this paper, the authors propose an optimal routing approach inspired by the foraging behavior of ants to maximize the network performance while optimizing the network resource utilization. The proposed AntMeshNet algorithm is based upon Ant Colony Optimization (ACO) algorithm; exploiting the foraging behavior of simple biological ants. The paper proposes an Integrated Link Cost (ILC) measure used as link distance between two adjacent nodes. ILC takes into account throughput, delay, jitter of the link and residual energy of the node. Since the relationship between input and output parameters is highly non-linear, fuzzy logic was used to evaluate ILC based upon four inputs. This fuzzy system consists of 81 rules. Routing tables are continuously updated after a predefined interval or after a change in network architecture is detected. This takes care of dynamic environment of WMNs. A large number of trials were conducted for each model. The results have been compared with Adhoc On-demand Distance Vector (AODV) algorithm. The results are found to be far superior to those obtained by AODV algorithm for the same WMN.

Date: 2014
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