Optimal Scheduling using Multi-Objective Artificial Bee Colony based Intuitionistic Fuzzy Possibilistic C Means Clustering over Mobile Ad Hoc Networks

Abstract

Mobile Adhoc networks are defined as the group of mobiles nodes that distributed in various locations which can connect with each other through multi-hop wireless path with the ability of dynamic path changing during node movement. This dynamic mobility behavior and its small size need to be provided with required resources, so that MANET growth can be assured. In the existing work, efficient resource allocation is guaranteed using the mechanism namely a lightweight dynamic channel allocation mechanism which can tolerate the non-uniform load distribution issues effectively. With this, dynamic requirement of users are satisfied using Centralized Dynamic Channel Allocation (CDCA) for Fuzzy Covariance Possibilistic C-Means (FCPCM) clustering algorithm. The existing work cannot achieve the local optimal result and it cannot satisfy the multi objective parameter values that are cannot satisfy the parameter values of energy, bandwidth, delay and optimal scheduling. This is resolved in the proposed research work by introducing the novel mechanism namely, lightweight Centralized Dynamic Channel Allocation (CDCA) mechanism for Intuitionistic Fuzzy Possibilistic C Means (IFPCM) Clustering Based MANETs and a cooperative load balancing strategy. The optimal scheduling is done by using the Multi-Objective Artificial Bee Colony (MOABC) approach. IFPCM is used to select the efficient cluster head node by generating the membership values. It is more useful for load balancing through calculating the multi objective parameters using MOABC algorithm. MOABC is focused to increase the local and global optimization by selecting the best channel condition. Thus, it is used to improve the service levels in the scheduling process in terms of higher packet delivery ratio, throughput, bandwidth and lower energy consumption. Combine both algorithms such as Cluster Heads Multi hop time (CMH)-TRACE to provide support for non-uniform load distributions and propose CDCA-TRACE. The proposed research is achieved optimal load balancing in the given network using CDCA with MOABC TRACE (CDCMOABC-TRACE) method.