ectivise's repositories
411
An Alert Management Web Application
ansible-librenms-1
Ansible role for setting up librenms
ansible-librenms-3
Ansible role for deployment of LibreNMS
ansible-mail-server
Production grade email server setup with only one command...
ansible-python
Ansible role for managing python installation
ansible-role-certbot
Ansible Role - Certbot (for Let's Encrypt)
ansible-role-memcached
Ansible Role - Memcached
bitnami-docker-mariadb
Bitnami MariaDB Docker Image
calendar
calendat for ADMIN LTE
charts
Helm Charts
docker-librenms
LibreNMS Docker based on Phusion Baseimage
docker-librenms-1
Docker image for LibreNMS
elastiflow
Network flow analytics (Netflow, sFlow and IPFIX) with the Elastic Stack
eNMS
An enterprise-grade vendor-agnostic network automation platform.
grafolean-netflow-bot
NetFlow collector and bot for Grafolean
node-smpp
SMPP client and server implementation in node.js
openfortigui
VPN-GUI to connect to Fortigate-Hardware, based on openfortivpn
react-component-depot
Collection of various react components with youtube tutorials
School_Monitor_System
Network Device Monitor System, Base on Librenms & Grafana.
SMPP-Server-1
SMPP Server on Node JS
SMPPClient
SMPP Client in C#
WiFi-Positioning-and-analysis-system
It is very difficult to think of an aspect of life that has not been affected by the Internet. It does more than just connecting computers. It connects people, lives, stories, and businesses. Wireless networks are present in all the large buildings or sites, and they are anticipated to provide high-speed Internet for the connected users. This can be attained by connecting wireless routers to the Internet backbone through fast connection cables (e.g. fiber optics), or as well finding the optimal position of the router along with the location, so that the targeted area is covered with Internet access as much as possible, provided that the cost constraints of routers and the cost of their mutual interconnection are satisfied. As the placement of WI-FI routers in the network is a very intensive problem concerning connectivity and coverage. It directly affects the transmission loss, installation cost, operational complexity, wi-fi network coverage, etc. However, optimizing the location of the routers can resolve these issues and increase network performance. Therefore, using major deep-learning models this problem can be resolved. The proposed model concentrates on the optimization of the objective function in terms of the empty spaces in the location, hindrances such as concrete walls, metallic objects, etc. in the area, client coverage in the location, and the network connectivity. It is an initial step to ensure the desired network performance such as throughput, connectivity, and coverage of the network. The model also additionally bifurcates the areas into divisions based on the network coverage in each region for particular chores like messaging, streaming, gaming, etc. Furthermore, an advanced Wi-Fi analyzing system for generating different results based on the observations of the Wi-Fi router and the network it is placed in is implemented. It gives an analysis report of the Wi-Fi router. It dictates the number of users presently connected to the system with their description like IP Address, Physical Address, etc and also determines the information regarding the devices in the network range of the router. It executes signal strength testing that demonstrates the strength of the signal in the network and also performs a speed testing module that determines the upload speed and the download speed of the system using real-time graph plotting. The computational experiment, performed over a dataset of sample house maps, to indicate the optimal position of the Wi-Fi proposes that the approach can obtain great results. Consequently, the results indicate that the approach can be easily adapted for application in practice for determining the network areas based on the signal strengths in the region, in terms of the Wi-Fi router placement and analyze the wireless network, devices in the network, and the connected users. The application can be extended to provide co-ordinates for a 3D map. The model can also be paired with some hardware to increase portability.
wireless_website_v2
website to moniter wireless speed