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adaptive_cloud_community

Public repository for hosting the Azure Adaptive Cloud Community content

angularshoppingapp

angularstore

basicflinkpattern

ckad_tips

Tips for CKAD exam

cloudguru

cp_2021

edgecomputinggateway_android

Android TV gateway agent for wso2 iot server by embedding siddhi 4 as dependencies from maven nexus repositories

ejabberd-contrib

Growing and curated ejabberd contributions repository - PR or ask to join !

find-shortest-path-using-generic-algorithm-in-matlab

Objective of this project was to select minimum cost path for sending packets from router A to router B such that all routers are traversed, hence this problem is different to Travelling Salesmen Problem (TSP), where Intermediate nodes can be left off. Initial location for all routers was randomly generated in 3-D space. Hinged upon initially generated locations, distance amidst them is computed using Euclidian formula which serves as Fitness function. Initial Population was selected using Roulette wheel selection using aforementioned Fitness function. Then Crossover was computed if, Probability of crossover. Pc > (Randomly generated probability) using two-point crossover. After this initial population was updated and mutation was done if Pm > (Randomly generated probability). Best chromosome was computed using max fitness function and Inversion / Swapping / Sliding was done on 2nd,3rd,4th chromosome, while 1st chromosome was passed as such using Elite Selection method to preserve best chromosome (Solution in this case). User have laxity to enter number of initial routers, size of initial population and number of iterations for Genetic algorithm to simulate. This method was named as MGA (Modified Genetic Algorithm) and it’s performance was juxtaposed with SGA (Simple Genetic Algorithm) where Initial Selection / Fitness function / Crossover / Mutation method deployed were computed differently using same set of routers co-ordinates used for SGA. Results were shown using six simulation Graphs, three for each case.

finding-shortest-path-using-genetic-algorithm-

This code generates the random locations in 3-d space and then finds the shortest path among the first and last node. User has leverage to enter total number of router , total population size and number of generations . with that selected , the genetic algorithm performs genetic operations like single point crossover , mutation( inversion , swapping , sliding ) etc. Also the roulette wheel type of selection is done by selecting the best individual on basis of cost/ distance competition . each time the new individuals/ paths are selected, the distance between the source to destination node gets shortened. At end of simulation , three graphs are generated that shows the initial location of routers , final path to send packets from source to destination node and generation graph showing the shortest distance at each generation

flink-training-exercises

free-programming-books

:books: Freely available programming books

googlemapdirectionsimple

Create a simple app that find a path between two places

maps_application

this is my cep work -- started in may 2017

messaging

Resource collection for messaging and eventing

performance-analysis-of-tcp-variants-and-routing-protocols-using-ns2-simulations

Routing in an Ad-hoc network is a challenging task because source and destination nodes are mobile and thus routing decisions are to be changed dynamically when link failure or packet delay is encountered. As TCP protocols were initially designed for wired networks, so they are not able to deliver optimized performance, in the case of ad-hoc networks. For ensuring a reliable transfer, various variants of TCP must be used such as TCP-Reno, TCP-Vegas, TCP-Westwood, TCP-New Reno, TCP-Tahoe, TCP-Sack etc. Mobile ad-hoc network is a decentralized network consisting of various mobile nodes. Challenges in MANETS routing includes lack of apriori knowledge of underlying topology, which requires using the adaptive protocol to tackle route failures and packet loss scenarios.

product-iots

Welcome to the WSO2 IoT Server source code! For info on working with the WSO2 IoT Server repository and contributing code, click the link below.

py_cisco

This is the private repo for takehome exam by Cisco Calgary, Canada

site_public

springwebapp

testingjunit5