RETRACTED ARTICLE: A hybrid swarm intelligent framework to support efficient military communication in MANET

Autor: K. Sudhakar, S. Anbukaruppusamy, N. Sengottaiyan
Rok vydání: 2020
Předmět:
Zdroj: Journal of Ambient Intelligence and Humanized Computing. 12:5215-5223
ISSN: 1868-5145
1868-5137
DOI: 10.1007/s12652-020-01999-9
Popis: Military communication involves sharing of secret information which needs to be done with more concern to assure secured transmission of data. In the war field communication would be more difficult task where the soldiers are scattered in different location. In this case route path establishment and routing would be more difficult task due to presence of scattered soldiers in multiple locations. There is various research works has been focused to provide the efficient and better route path establishment in MANET. In the previous work, load and energy aware micro–macro density clustering approach is introduced which focus on performing clustering in two levels micro clustering and macro clustering. However this research work might degrades its clustering performance with respect to energy and time in case of more cluster count generation and also optimal route path construction is also not performed correctly. This is resolved in proposed research work by introducing new framework called multi-objective aware micro–macro clustering using hybridized additive weight based genetic algorithm (MO–MMC–HAWGA). This proposed research work leads to optimal clustering of mobile node under multiple objectives consideration like distance, energy, bandwidth, and stability. Initially it first chooses the optimal cluster head based on which optimal clustering would be done. This work improves the performance by avoiding the more number of clusters which are generated at run time by performing macro clustering. The research work/s experimental evaluation is conducted in NS2 simulation environment and it is proved that proposed research work namely MO–MMC–HAWGA provides optimal result than existing research methods.
Databáze: OpenAIRE
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