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Network Simulation Python

 

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Network Simulation Python is the process of creating network simulations is examined as complicated as well as fascinating. Expand your network simulation projects with our Python support! We have all the needed tools and in touch with leading libraries to get your work started. Connect with us for expert guidance and help.

Through the utilization of tools and libraries with its specific characteristics and application areas, the way of developing network simulation in Python can be accomplished. We provide some prevalent choices:

  1. SimPy

SimPy library is often examined as a process-oriented discrete-event simulation model. For simulating the characteristics of different kinds of models and networks, it is utilized.

Installation:

pip install simpy

Instance:

import simpy

def network(env, num_servers, service_time):

for i in range(num_servers):

env.process(server(env, i, service_time))

def server(env, server_id, service_time):

while True:

print(f’Server {server_id} is ready to receive a request at {env.now}’)

yield env.timeout(service_time)

print(f’Server {server_id} has processed a request at {env.now}’)

# Simulation setup

env = simpy.Environment()

network(env, num_servers=3, service_time=5)

env.run(until=20)

  1. ns-3 with Python Bindings

For internet models, ns-3 is considered as a discrete-event network simulator. Typically, Python buildings are encompassed in this library.

Installation: It is intricate to adhere to the instruction of ns-3 installation which is required. The following is a simple outline:

  1. Install dependencies:

sudo apt-get update

sudo apt-get install gcc g++ python3 python3-dev

  1. Download and build ns-3:

wget https://www.nsnam.org/release/ns-allinone-3.xx.tar.bz2

tar xjf ns-allinone-3.xx.tar.bz2

cd ns-allinone-3.xx

./build.py –enable-examples –enable-tests

  1. Enable Python bindings:

./waf configure –enable-examples –enable-tests –with-python

./waf

Instance: The following is a simple instance of configuring a basic network simulation:

import ns.core

import ns.network

import ns.internet

import ns.point_to_point

import ns.applications

# Create nodes

nodes = ns.network.NodeContainer()

nodes.Create(2)

# Install internet stack

stack = ns.internet.InternetStackHelper()

stack.Install(nodes)

# Create point-to-point link

pointToPoint = ns.point_to_point.PointToPointHelper()

pointToPoint.SetDeviceAttribute(“DataRate”, ns.core.StringValue(“5Mbps”))

pointToPoint.SetChannelAttribute(“Delay”, ns.core.StringValue(“2ms”))

devices = pointToPoint.Install(nodes)

# Assign IP addresses

address = ns.internet.Ipv4AddressHelper()

address.SetBase(ns.network.Ipv4Address(“10.1.1.0”), ns.network.Ipv4Mask(“255.255.255.0”))

interfaces = address.Assign(devices)

# Create and configure applications

echoServer = ns.applications.UdpEchoServerHelper(9)

serverApps = echoServer.Install(nodes.Get(1))

serverApps.Start(ns.core.Seconds(1.0))

serverApps.Stop(ns.core.Seconds(10.0))

echoClient = ns.applications.UdpEchoClientHelper(interfaces.GetAddress(1), 9)

echoClient.SetAttribute(“MaxPackets”, ns.core.UintegerValue(1))

echoClient.SetAttribute(“Interval”, ns.core.TimeValue(ns.core.Seconds(1.0)))

echoClient.SetAttribute(“PacketSize”, ns.core.UintegerValue(1024))

clientApps = echoClient.Install(nodes.Get(0))

clientApps.Start(ns.core.Seconds(2.0))

clientApps.Stop(ns.core.Seconds(10.0))

# Run the simulation

ns.core.Simulator.Run()

ns.core.Simulator.Destroy()

  1. Mininet

A virtual network of switches, links, hosts, and controllers are developed by a Mininet which is a network emulator. As a means to assess the effectiveness of network and protocol characteristics, it is employed.

Installation:

sudo apt-get install mininet

Instance:

from mininet.net import Mininet

from mininet.node import Controller, OVSKernelSwitch

from mininet.cli import CLI

from mininet.log import setLogLevel

def simpleTest():

net = Mininet(controller=Controller, switch=OVSKernelSwitch)

net.addController(‘c0’)

h1 = net.addHost(‘h1’)

h2 = net.addHost(‘h2’)

s1 = net.addSwitch(‘s1’)

net.addLink(h1, s1)

net.addLink(h2, s1)

net.start()

h1.cmd(‘ping -c 4 %s’ % h2.IP())

CLI(net)

net.stop()

if __name__ == ‘__main__’:

setLogLevel(‘info’)

simpleTest()

Based on the necessities and complications of our project, these libraries and tools offer an efficient initiating phase for network simulation in Python.

Network simulation python Projects

In the contemporary years, numerous project topics based on network simulation are progressing continuously. Encompassing different factors of network simulation and analysis, we recommend 50 extensive project topics for network simulation with Python:

  1. Performance Analysis of TCP Variants
  • On the basis of packet loss, throughput, and delay in a simulated network, we intend to contrast various kinds of TCP such as TCP Vegas, TECP Reno.
  1. Network Congestion Control Mechanisms
  • In a network simulation, our team plans to apply and contrast various congestion control methods.
  1. Wireless Sensor Network (WSN) Simulation
  • Concentrating on network lifespan and energy utilization with various routing protocols, a WSN ought to be simulated.
  1. Dynamic Routing Protocols in MANETs
  • In Mobile Ad-hoc Networks, we focus on assessing the effectiveness of dynamic routing protocols such as OLSR, AODV, and DSR.
  1. Vehicular Ad-hoc Networks (VANETs) Simulation
  • As a means to investigate the influence of mobility trends on network effectiveness, it is significant to simulate VANETs.
  1. IoT Network Simulation
  • In order to explore the effectiveness of communication protocols such as CoAP and MQTT, we aim to simulate an IoT network.
  1. 5G Network Simulation
  • In various settings, assess capability, latency, and throughput through simulating 5G networks.
  1. Software-Defined Networking (SDN) Simulation
  • An SDN controller has to be executed. Generally, to estimate the adaptability and effectiveness, it is appreciable to simulate a network.
  1. Network Security Attacks Simulation
  • Various network security assaults like MITM, DDoS should be simulated. On network effectiveness, our team intends to examine their influence.
  1. Quality of Service (QoS) in Multimedia Networks
  • To assess QoS parameters such as packet loss, jitter, and delay, we focus on simulating multimedia networks.
  1. Fault Tolerance in Networks
  • For examining the influence of node faults, our team simulates a network. Typically, fault-tolerant methods have to be applied.
  1. Load Balancing in Cloud Networks
  • As a means to assess various load-balancing methods, it is appreciable to simulate a cloud network.
  1. Energy-Efficient Routing in Wireless Networks
  • To apply and assess energy-effective routing protocols, our team plans to simulate wireless networks.
  1. Cognitive Radio Networks Simulation
  • Typically, cognitive radio networks must be simulated to explore spectrum allocation and sharing approaches.
  1. Network Virtualization
  • In order to estimate the effectiveness of virtual network processes, it is advisable to simulate network visualization.
  1. Delay Tolerant Networks (DTNs) Simulation
  • In irregular connectivity scenarios, assess routing protocols by simulating DTNs.
  1. Network Traffic Analysis and Visualization
  • Mainly, network traffic has to be simulated. For exploration and visualization, we aim to construct effective tools.
  1. Edge Computing Networks
  • As a means to assess latency and bandwidth utilization in distributed platforms, our team focuses on simulating edge computing networks.
  1. Mobile Edge Computing (MEC) Simulation
  • Concentrating on latency and resource consumption, it is approachable to investigate the influence of MEC on network effectiveness.
  1. Fog Computing Networks
  • To estimate the effectiveness of applications that are implemented nearer to end-users, we intend to simulate fog computing networks.
  1. Network-on-Chip (NoC) Simulation
  • Mainly, NoC infrastructures should be simulated to examine performance parameters and communication trends.
  1. Underwater Acoustic Networks
  • As a means to assess network effectiveness and communication protocols, our team plans to simulate underwater acoustic networks.
  1. Satellite Communication Networks
  • For exploring error rates, latency, and throughput, satellite networks have to be simulated.
  1. Ad-hoc Network Security Protocols
  • For ad-hoc networks, we aim to apply and assess security protocols.
  1. Cross-Layer Design in Wireless Networks
  • To enhance network effectiveness, it is appreciable to simulate cross-layer optimization approaches.
  1. Network Slicing in 5G Networks
  • For estimating the segregation and effectiveness of various network slices, our team focuses on simulating network slicing.
  1. Content Delivery Networks (CDNs)
  • On content delivery effectiveness, investigate the influence of caching policies through simulating CDNs.
  1. Blockchain-Based Network Security
  • In a network, we plan to apply and simulate blockchain-related security technologies.
  1. Smart Grid Communication Networks
  • In order to assess latency and credibility, it is significant to simulate communication networks in smart grids.
  1. Machine Learning in Network Traffic Prediction
  • For forecasting network traffic and reinforcing resource allocation, our team intends to employ machine learning approaches.
  1. IoT-Based Smart Home Networks
  • As a means to examine the protection and effectiveness of IoT devices, smart home networks ought to be simulated.
  1. Routing in Flying Ad-hoc Networks (FANETs)
  • In extremely dynamic platforms, estimate routing protocols through simulating FANETs.
  1. Inter-Planetary Network Simulation
  • To investigate communication delays and data transmission protocols, we aim to simulate inter-planetary networks.
  1. Cyber-Physical Systems (CPS) Network Simulation
  • Generally, the CPS network has to be simulated to explore the incorporation of physical procedures and networked control.
  1. Multicast Routing Protocols in Wireless Networks
  • As a means to assess the adaptability and effectiveness, our team focuses on simulating multicast routing protocols.
  1. Network Coding Techniques
  • To enhance credibility and throughput, it is advisable to utilize and simulate network coding approaches.
  1. SDN-Based Network Security Solutions
  • The SDN-related security solutions must be simulated to estimate their performance in reducing assaults.
  1. Performance Analysis of Hybrid Networks
  • As a means to investigate the effectiveness, we plan to simulate hybrid networks integrating wireless and wired mechanisms.
  1. Virtual Reality (VR) Network Simulation
  • Typically, VR networks have to be simulated to examine user expertise, latency, and bandwidth necessities.
  1. Augmented Reality (AR) Network Simulation
  • In various network scenarios, assess the effectiveness of AR applications through simulating AR networks.
  1. IoT-Based Healthcare Networks
  • To explore data transfer and safety problems, our team focuses on simulating healthcare networks with IoT devices.
  1. Big Data Transfer in High-Speed Networks
  • As a means to estimate the big data transmission and its influence on network effectiveness, we aim to simulate high-speed networks.
  1. Collaborative Edge and Cloud Computing
  • Mainly, to assess the effectiveness and collaboration, networks must be simulated which integrates cloud and edge computing.
  1. Quantum Communication Networks
  • In order to investigate error correction and quantum key distribution, we plan to simulate quantum communication networks.
  1. Vehicular Cloud Networks
  • In connected cars, assess data transmission and computation offloading by simulating vehicular cloud networks.
  1. Network Function Virtualization (NFV)
  • To estimate the implementation and effectiveness of virtual network procedures, our team aims to simulate NFV.
  1. Autonomous Vehicle Networks
  • Generally, networks for automated vehicles ought to be simulated to explore communication latency and credibility.
  1. Smart City Network Infrastructure
  • Among urban architecture, assess connectivity and data sharing through simulating smart city networks.
  1. IoT-Based Industrial Networks
  • In manufacturing procedures, investigate actual time tracking and management by simulating industrial IoT networks.
  1. Multi-Access Edge Computing (MEC)
  • To estimate the effectiveness of applications which are implemented at the network edge, we focus on simulating MEC networks.

We have offered few efficient libraries and tools which are utilized for constructing network simulations in Python. Also, 50 thorough project topics for network simulation with Python involving several factors of network simulation and investigation are suggested by us in this article.

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