Some might say innovation wins, speaks for itself... That might be true in a perfect world, however in a more realistic scenario,  innovation needs to be timed well, and backed by a strong business drive! The Nevo family, one of the most innovative platforms that I had the pleasure of working on, from user interface to technology. Good old times with late night coding sessions and Chili's take-outs! 

I thought it's worth capturing this just for historical value as I have to admit much has happened in the two years since we published this paper and "very soon" a major update will be coming out. This is an overview & comparison of Zigbee, Z-Wave, RF4CE, Bluetooth and Wi-Fi in the home. 

This is a study of insecurities in IEEE 802.11 Wireless Local Area Network (WLAN) standard due to the poor implementation of RC4 in Wired Equivalent Privacy (WEP) encryption algorithm used in this standard and other implementation specific problems passed on to wireless networks from the traditional wired Local Area Networks (LANs). In addition to the attacks already in use by many I have proposed a new approach based on Genetic Algorithm to improve the speed of cracking the secret key used to encrypt the packets. I have also provided a step-by-step approach to hacking WLANs with a brief description of some of the tools available for each step. For completeness I have included a brief overview of the security provisions to improve the issues described in this paper. To the is a diagram that shows some of the covered topics in this work:


Thesis (attached) was presented at San Francisco State University on 10 May 2004.
Committee:

Prof. H. Shahnasser (Chair)
Prof. T. Cooklev

The objective in this project is to optimize a given circuit using Genetic Algorithm. The parameter which will be optimized can be chosen by the user and the program will change the specified parameters to reach the desired accuracy. The first step is to be able to fit a function to desired values, this has been done for a specified function as an example in the Fit_Function program. The next step is to find a Simulator which simulates the specified circuit and returns the symbolic transfer function of the circuit, or else we would have to run a numerical simulator like PSPICE in each iteration and that is going to make the optimization process take a much longer time to complete.And the final step is to link the main program to this simulator.


About Optimization using Genetic Algorithm:


The rapidly expanding field of Genetic Algorithms ( GAs ) has given rise to many new applications in a variety of disciplines. One of the major applications in which GA is used is
Optimization. Genetic Algorithm is a subset of evolutionary algorithms that model biological processes to optimize highly complex cost functions.

Some of the advantages of a genetic algorithm include that it :

• Optimizes with continuous or discrete parameters
• Doesn't require derivative information
• Simultaneously searches from a wide sampling of the cost surface
• Deals with a large number of parameters
• Is well suited for parallel computers
• Optimizes parameters with extremely complex cost surfaces; they can jump out of a local minimum
• Provides a list of optimum parameters, not just a single solution
• May encode the parameters so that the optimization is done with the encoded parameters
• Works with numerically generated data, experimental data, or any analytical functions.

About The Project:

Project supervisor, Dr Naser Sadati, has been a great mentor and has actually thought us everything we know about Fuzzy Logic and Genetic algorithm.


The project group is consisted of :

• Arsham Hatambeiki , BS student, Electronics Group, Sharif University of Technology
• Saeed Karimi Fard , BS student , Control Group , Sharif University of TechnologyThis application is written in Microsoft Visual C++ 6.0 - 09 November, 2001

Attached is a complete introduction to Genetic Algorithm and Otimization and the special ( in Farsi ) changes made for our optimizer. The last section of the pdf is a complete tutorial for the ACO software and some screen shots ( in English ) .

This is an example of Object Oriented programming. This program has been written using VC++ 6.0 as a Win32 Console application. This program uses Genetic Algorithm to fit a function to as many points as you desire. it's objective is to minimize the some of square errors in all the points.

We choose the function as follows:

it has 12 coefficients, so we choose the chromosome as an array of 12 Real numeric quantities and we generate each generation (population) of 20 chromosomes as follows:

1.Ten best chromosomes of the previous generation are included in this generation automatically.

2.Next ten chromosomes are generated using Genetic laws of inheritance ( Cross Over, Mutation ) Using the best 10 results of the previous generation.



The first population is generated using Random Real numbers and then they are sorted from the one resulting in minimum Sum of Square Errors to the maximum. And the top ten on the list are used to generate the next population and so on. The formula used to calculate Sum of Square Error is as follows:



There are many different rules to be used for Cross Over and Mutation, as an example you can see the results for a couple of them after 10,000 iteration.

As an example after 10,000 iterations for fitting the function to 6 points using the rules seen in the Fit_Function program we have the following results:

desired and current point and values :