An Open Flow Controller Based on the Intercerebral Neural Network for Media Independent Handover

Qiong Wu Jun S Huang

doi:10.4172/2090-4908.1000112

Abstract

This paper presents a novel open flow controller based on the intercerebral neural network for the media independent handover over wireless operation. This controller makes a binary decision based on the link control parameters obtained instantaneously from the trained neural network, which in turn depends on the instantaneous mobile speed interaction with the wireless link performance parameters. These parameters of packet loss, round trip time, radio signal strength, bandwidth, data cost and vehicle speed are typically different from network to network. We use a mutated particle swarm optimization to train the fundamental controller equation for the media independent handover that are mixed with the radial and sigmoid activation functions of the intercerebral neural network. Simulations based on the field experiment test data show that the system using the swarm intelligence algorithm is practical for the improvement of the overall secure networking experience for the fast delivery.

Highlights

A transportation company for fast delivery service usually has to maintain a team of service trucks on the road, each of which is equipped with a mobile scanner that keeps track of the goods during its upload and download operations as well as reporting the GPS position of the truck
Typical wireless handover is determined by looking at the Link Going Down (LGD) parameter [8], LGD can be a function of a number of measured parameters, such as packet loss, round trip time, radio signal strength, bandwidth, data cost and vehicle speed, etc
The intercerebral approach comes from studying of actual neuron structure between man and woman, where we found that there are only two types of neurons in our brain; we call it either Radial Based Function (RBF) [12,13] or Sigmoid Based Function (SBF): a) A Radial Basis Function (RBF) network [14] is a neural network that uses radial basis functions as activation functions

Summary

Introduction

A transportation company for fast delivery service usually has to maintain a team of service trucks on the road, each of which is equipped with a mobile scanner that keeps track of the goods during its upload and download operations as well as reporting the GPS position of the truck. Typical wireless handover is determined by looking at the Link Going Down (LGD) parameter [8], LGD can be a function of a number of measured parameters, such as packet loss, round trip time, radio signal strength, bandwidth, data cost and vehicle speed, etc Each of these parameters has a relationship to the location environment relative to the base station or access point. The only easy way to simplify these relationships is to use the intercelebral neural network that is meant to capture the multiple nonlinear relationships in order for us to make a quick handover decision from the measured field data By using this method, we are able to capture both WiFi and GPRS characteristics at the same time. The non-linear relationships are first summarized below before we present our PSO (Particle Swarm Optimization) algorithm

A Mathematical Model of Radio Signal Strength

Findings

Conclusions