An Intelligent Agent Based Optimal Scheduling & Congestion Free Routing Scheme to Enhance QoS of NFV Enabled Heterogeneous Wireless Sensor Network Framework

In wireless sensor network (WSN), congestion is regarded as an unavoidable issue as of today’s scenario at which the data traffic is increased to their aggregated channel capacity. This results in the consequence of buffer overflowing which leads to packets drop thereby reducing packet delivery ratio and degrades network throughput. Also, routing is the most favored approaches for the minimization of nodes energy consumption and the enhancement of Heterogeneous WSNs throughput. Since, the problem of routing has been proved to be an NP-hard, it has been realized that optimization-based technique offers better performance. For solving these issues, this paper proposes a congestion free optimal routing scheme based on Genetic algorithm-based distance aware-Low Energy Adaptive Clustering Hierarchy (GADA-LEACH) protocol to predict intelligent agent path. The major intention of this work is to enhance the efficiency of energy and QoS (throughput and delay) in edge-cloud based IoT environment of data transmission. Initially, the system model is initialized and the graph construction is made for the process of clustering using Spectral graph theory (SGT) at which the cluster head (CH) selection is made. Then, the scheduling process is carried by means of Packet weighted based ensemble optimization scheduling (PW-EOS) scheme. The NFV enabled optimal edge node is selected using Reconfigured Cuckoo Search optimizer (RCSO) after which the intelligent agent moving path in the Heterogeneous Network (HetNet) are predicted by GADA-LEACH technique. From this, an effective routing path is determined among CH and an optimized Edge node. Eventually, the performance assessment is carried in terms of throughput, energy consumption, survival rate, PDR, End-to-End delay and the outcomes conquered are compared with existing methods to attest the proficiency of proposed scheme.