Mutated k-means algorithm for dynamic clustering to perform effective and intelligent broadcasting in medical surveillance using selective reliable broadcast protocol in VANET

Abstract

Vehicular ad-hoc Network (VANET) is an emerging type of Mobile ad-hoc Networks (MANETs) with excellent applications in the intelligent traffic system. Applications in VANETs are life critical since human lives are at stake and therefore, interaction among nodes (vehicles) must be established in the most secure manner. To give effective information transmission between vehicles, Cluster based topology bunches vehicle hubs in geographic region together can be connected to help coordinate correspondence inside and outside group should be possible through group heads. Different variations of K-implies calculation are ordinarily utilized for bunching yet these calculations cannot be specifically connected to VANET due to dynamic evolving topology. In this paper, we propose of dynamic grouping through K-implies which suits well for dynamic topology qualities of VANET. The proposed technique functions admirably with referred to number of bunches ahead of time and also the obscure number of groups. In our approach, the client has the adaptability to settle the quantity of bunches or the base number of groups required. This calculation figures the new bunch focus by expanding the group counter by one in every emphasis until the point that target work is fulfilled. The same can be defined and the ideal group heads and the relationship amongst CMs and CHs can be resolved.