An Improved-Cross Layer Scheduling model for intelligent home networks

Abstract

We daily add more devices and services into existing intelligent home networks. Consequently, various networking standards evolutions being experienced world over have not left home networks behind. These evolutions results in competition and depletion of the available limited resources. Consumers therefore, experience unavailable, unreliable and poor performing network both locally and remotely. In this paper, we present an Improved-Cross Layer Scheduling (CLS) model that optimizes performance of intelligent home networks. We have used Particle Swam Optimization (PSO) algorithm to dynamically schedule, align and prioritize network subnets, devices and services in the model. We have used Virtual Local Area Network (VLAN) protocol to classify home network into six subnets. Consequently, we have used weighing factor numbers to classify subnet devices. Further, we have used Differentiated Service Code Points (DSCP) to classify supported home network services into six classes. Moreover, we have increased number of packets transmitted per Class of Service (CoS) iteratively. We have optimized each subnet and used the output of the preceding subnet as input to subsequent subnet. Equally, we have reduced delay between consecutive transmitting CoSs in the media. We have simulated our model and realized average network throughput of 99.735%, packet loss of 1.59% and delay of 1.82 milliseconds.