For different applications such as smart cities, healthcare, ecological tracking, and business procedures, Wireless Sensor Networks (WSNs) are determined as significant. The process of simulating networks is examined as vital for developers and researchers to formulate, examine, and enhance WSN protocols and infrastructures before real implementations. Each with its own collection of characteristics, advantages, and challenges, several simulation tools have been constructed for this usage. The following is a summary of few popular wireless sensor network simulation tools:

1. NS-2 / NS-3

• Overview: A open-source simulation tools are NS-2 (Network Simulator 2) and its enhancements NS-3, which are extensively employed for study in networking. Encompassing WSNs, they assist a broad scope of protocols and mechanisms.
• Strengths: Wide protocol assistance, a wider user committee, and extremely extensive and precise simulations.
• Limitations: NS-3 is more advanced and employs C++/Python but sustains a complicated infrastructure, while NS-2 has a steep learning curve and employs TCL scripting which might be restrictive for some.

2. OMNeT++

• Overview: OMNeT ++ is mainly for networking study, which is a modular, extensible simulation platform. It is not constrained to WSNs, it is extensively efficient for such simulations when incorporated with systems such as Castalia or INET.
• Strengths: Along with a graphical interface for network design and visualization, it is extremely adaptable. By means of external tools, it assists co-simulation.
• Limitations: Specifically, for immersive customization and simulation logic deployment, it needs C++ skills.

3. Castalia

• Overview: Typically, Castalia is formulated for simulating WSNs, Body Area Networks (BANs), and networks of less-power integrated devices. It is constructed on the top of OMNeT++.
• Strengths: Encompassing sensor node hardware, node energy usage, and wireless channel frameworks, concentrates on practical simulation of sensor network activities.
• Limitations: It might not comprise every business application situation, but especially designed to study.

4. Cooja

• Overview: Cooja is determined as the default simulator for Contiki OS. Encompassing microcontroller nodes running Contiki as well as simulated radio messages within the similar simulations, it permits simulation of networks.
• Strengths: Assisting the thorough simulations of the hardware and software stack, this is perfect for IoT and WSN simulations including Contiki OS.
• Limitations: It restricts its feasibility for non-Contiki platforms, whereas it is mainly formulated for Contiki-related frameworks.

5. TOSSIM

• Overview: Permitting extensive exploration of interaction at the bit level, TOSSIM simulates the implementation of TinyOs applications with high exactness. It is considered as a simulator for TinyOS-related networks.
• Strengths: Helpful for correcting and examining the applications of TinyOS, extreme precision in simulating TinyOS networks.
• Limitations: It is particular to TinyOS, therefore it is less appropriate for projects outside this environment.

6. Matlab’s Simulink

• Overview: By means of its in-depth library of mathematical and signal processing blocks, MATLAB’s Simulink platform can be employed to simulate WSNs. Typically, it is not solely a network simulator.
• Strengths: Incorporating complicated signal processing and mathematical modelling, it is robust for simulations. Also, it contains effective visualization abilities.
• Limitations: Needs experience of MATLAB for efficient usage. Proprietary software with a licensing expense.

7. CupCarbon

• Overview: Particularly, for tracking and ecological data gathering, a Smart City and IoT WSN simulator formulated for modelling, visualizing, and verifying distributed methods.
• Strengths: Including sensor networks efficient for smart city simulations. It is defined as a user-friendly interface.
• Limitations: In other WSN applications, constraint its usage. It mainly concentrates on urban sensor networks and IoT settings.

What types of software can you use to simulate your wireless sensor networks design?

     There are numerous types of software, but it is significant to choose an appropriate software according to the necessities of simulation.  Together with a few certain instances, the following is a summary based on the kinds of software we can employ to simulate our WSN models.

Network Simulator

Encompassing interaction protocols, routing methods, and network topology, these tools are formulated to simulate the networking factors of WSNs.

• NS-2/NS-3 (Network Simulator): In educational study it is extensively utilized for simulating different kinds of networks such as WSNs. The latest version is NS-3 that has the ability to provide effective performance and additional characteristics.
• OMNeT++: For constructing network simulators, a modular simulation model that offers suitable elements. Generally, OMNeT++ alone is not certain to WSNs, it can be employed for simulations when incorporated with the suitable modules.
• Cooja: Specifically, helpful for simulating IoT and WSN situations that include Contiki-enabled devices. It is the default simulator for Contiki OS.

IoT and WSN-Specific Simulators

For simulating the novel features of WSNs and IoT platforms like sensor activities and energy absorption, these simulations provide personalized capabilities.

• Castalia: It is designed for simulating Body Area Networks (BANs), WSNs, and networks of less-power integrated devices. Castalia is developed on OMNeT++.
• CupCarbon: Concentrating on WSNs for ecological tracking and urban scheduling, a simulator formulated for Smart City and IoT applications.

Integrated Development Environments (IDEs) with Simulation Capabilities

Permitting developers to code, simulate, and debug their WSN applications within the similar platform, few IDEs offer simulation tools as phases of their suite.

• Contiki OS and Cooja Simulator: For IoT, Contiki is an operating system. It works along with the Cooja simulator that permits simulation of Contiki-related networks.
• TinyOS: It offers simulation tools such as TOSSIM for examining TinyOS applications. TinyOS is an event-based operating framework that is formulated for less-power wireless devices.

Mathematical and Computational Modeling Tools

Enabling for complicated simulations such as physical layer simulation, signal processing, and many more, these tools are employed for developing thorough mathematical frameworks of WSNs.

• MATLAB and Simulink: Encompassing personalized toolboxes for wireless interaction, provide extensive tools for mathematical modelling and simulation.
• Octave: Specifically, for numerical calculating and designing, an open-source substitute to MATLAB that offers most of the similar characteristics.

Custom Simulation Scripts and Libraries

Custom scripts employing programming languages and libraries formulated for technical computing can be utilized for certain requirements or progressive simulations.

• Python with Libraries (NumPy, SciPy, Matplotlib): To construct custom WSN simulations, mainly for data processing and analysis, Python together with its technical and numerical libraries can be employed.
• SciLab: Equivalent to MATLAB, Scilab can be employed for custom WSN simulations. It is determined as an open-source software for numerical calculations.