A Particle Swarm and Clonal Selection Algorithm based Channel Assignment Algorithm in MRMC Wireless Mesh Network
Nandini Balusu1, Narsimha G2, Suresh Pabboju3 

1Nandini Balusu, Assistant Professor, Department of Computer Science and Eng, Telangana University, Nizamabad, Telangana, India.
2Narsimha G, Professor, Department of Computer Science and Eng, JNTUH College of Engineering, Hyderabad, Telangana, India.
3Suresh Pabboju, Professor, Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana State, India.

Manuscript received on 03 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 3763-3769 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3403078219/19©BEIESP | DOI: 10.35940/ijrte.B3403.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Channel Assignment (CA) in MRMC Wireless Mesh Network (WMN) is an efficient device that exploits numerous non-intersecting channels to reduce the interference and augment the capability of WMN. However, CA could diminish the complete network interference; its consequences might cause certain design issues that impact the efficiency of the network. A good CA in wireless mesh networks could minimize numerous interference co-channels and enhance the throughput. The clonal selection approach is simulated using the rudimentary notion of adaptive immune reply to virus stimulus. The PSO is inspired using the social behaviors of swarms. Motivated by these two optimization methodologies, in this paper, a hybridized particle swarm aided clonal selection approach is introduced for resolving channel assignment issue, i.e., evaluating a lower-interference channel assignment depending on gathered data. Conflict Vector in Conflict Graph known as antigens in clonal selection algorithm having fewer affinity values are improved using the particle swarm optimization update operation. Then the clonal selection and mutation operation is employed iteratively to generate the optimum conflict vectors. The experimental results are carried out using NS2. From the outcomes, it is clearly shown that the suggested approach has better energy efficiency, packet delivery ratio, and less network delay, packet drop compared to existing algorithms.
Keywords: Multi-Radio Multi-Channel, Wireless Mesh Network, Channel Assignment, Particle Swarm Optimization, Clonal Selection Approach, Artificial Immune System.

Scope of the Article: Web Algorithms