Simulation and Analysis Tools for conducting better research Projects

Suppose you have gathered customer data from social media on “how to maintain a proper diet”. Now you are thinking about writing content on it so that people read and review your content. Hence, you have to analyze the customer data first and then you have to make a decision on it. Well, this is how the simulation and analysis tool can help you in a research project. In this blog, we will know the simulation and analysis tools that can be implemented to conduct better research projects that are beneficial for a PhD or master’s students in writing a better research paper.

Types of simulation tools to use in research projects

There are various simulation tools that are being implemented in the research projects which are described below:

NS2 simulator

Network Simulator Version 2 is referred to as NS2. This open-source, event-driven simulator was created with computer communication network research in mind. Object-oriented Tool Command Language and C++ are the two main languages used in NS2 (OTcl). While the core mechanism (i.e., backend) of the simulation objects is defined in C++, the simulation is set up in OTcl by putting the pieces together, customizing them, and scheduling discrete events. 

TclCL is used to link the OTcl and C++ together. It is regarded as a discrete event simulator focused on network research that offers important assistance in simulating TCP routing and multicast protocols over wired and wireless networks (local and satellite).

NS3 simulator 

The Internet protocol and computer networks are modeled using two separate simulators NS3 and NS2, which are incompatible with one another. It can, however, be applied to systems that are not Internet-based and are not computer network systems. It can be read by the Wireshark software, but Network Animator must be installed to display the visual mode (NAM). With the aid of numerous Helper classes, NS3 enables us to deploy devices, internet stacks, applications, etc. to our virtual nodes, which are analogous to actual machines.

OPNET IT Guru simulator

It is a tool that aids in simulating the operation and behavior of any network type. In comparison to other simulators, it is quite powerful and adaptable. OPNET IT Guru offers models of devices and protocols that have already been developed, enabling the creation and simulation of various network topologies. Its set of devices and protocols is fixed; neither new protocols nor changes to the behavior of current ones are permitted.

OMNET++ Simulator

OMNeT++ is a C++ simulation library and framework that is modular and component-based and is mostly used to create network simulators. For non-commercial simulations like those used in educational settings and for training, OMNeT++ is freely available. For commercial use, OMNEST is an expanded version of OMNeT++.

GNS3 simulator

With the help of the graphical network simulator GNS3, you can quickly create network topologies and carry out simulations. It supports PIX firewalls, which serve as decorative hardware, ATM, Frame Relays, Ethernet Switches, and IOS routers. enables the configuration of hardware through the IOS, including switches and routers, among other active network devices. allows for the execution of simulators while also making network topology planning easier.

MATLAB simulator  

Engineers and scientists can use the programming environment MATLAB® to analyze, create, and test systems and technologies that will change the world. The basis of MATLAB is the MATLAB language, which is a language based on a matrix that permits the most organic expression of computer mathematics.

Types of analysis tools to implement in research projects

The analysis tools not only help in implementing research projects but also helps in effectively analyzing data. The types of analysis tools have been described below:

Microsoft Excel

The most well-known spreadsheet programme worldwide is Excel. Additionally, it includes strong computing and graphing features for data analysis. Excel is a standard in the market, regardless of your area of expertise or the supplementary software you might want. Its helpful built-in features include pivot tables and form design tools (for tallying or sorting data). Additionally, it offers a vast array of extra capabilities that make manipulating data easier.

Python 

Every data analyst should have access to Python since it has so many useful applications. It prioritizes readability more than more complex languages, and many programmers are already familiar with it because of its broad use in the computer industry. Additionally, Python has a huge variety of resource libraries that are appropriate for a wide range of different data analytics workloads, making it very flexible.

R

Like Python, R is a popular open-source computer program. It is commonly used to create statistical and data analysis software. Python has a simpler syntax than R, but R has a more difficult learning curve. Nevertheless, it is frequently employed for data visualization and was designed specifically to deal with challenging statistical computing problems.

Microsoft Power BI

Microsoft Power BI, one of the greatest business intelligence systems, offers a wide range of data sources. Using this application, users may create and distribute dashboards, visuals, and reports. For simple deployment, users can combine multiple dashboards and reports into a Power BI app. Users of Power BI may create automated machine learning models and the software connects with Azure Machine Learning.

Sissence

The data analytics platform Sisense can be used by both technical developers and business analysts to process and visualize all of their company’s data. It offers a wide range of drag-and-drop functionality as well as dynamic dashboards for teamwork. The Sisense platform’s unique In-Chip technology, which optimizes computation to utilize CPU cache rather than slower RAM, is a defining characteristic.

Conclusion

The PhD research projects greatly need the simulations and analysis tools that can help in the successful completion of the project. However, we, at Chanakya Research, have been able to get the trust of more than 1000+ students by using these simulation tools.

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