# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
#### 4.1.5 Conclusions  
In summary, NS-3, Cooja, and OMNeT++ serve as powerful tools for simulating network diffusion in IoT and Space IoT environments, each offering distinct advantages and trade-offs. As illustrated in Figure 2, NS-3 emerged as the most widely adopted simulator, followed by Cooja and OMNeT++. NS-3 is particularly well suited for large-scale simulations, offering high realism and extensibility. Cooja excels in low-power IoT simulations, making it ideal for resource-constrained environments, while OMNeT++ stands out with its modular architecture, enabling the simulation of complex network scenarios. Choosing the appropriate simulator depends on specific research and application needs, with scalability, extensibility, and ease of use being key factors to consider. We summarize the simulators as follows:  
* NS-3 is recommended for detailed protocol analysis and large-scale network scenarios.
* Cooja is ideal for low-power IoT applications and hardware emulation.
* OMNeT++ is suitable for complex simulations involving diverse network protocols and configurations.  
The selection of a simulator plays a crucial role in shaping the outcomes of IoT research, making it essential to evaluate the specific requirements of the study before choosing an appropriate simulation tool [116, 117].  
Beyond widely used simulators such as NS-3, Cooja, and OMNeT++, several JAVA-based network simulators have played a significant role in past research. GRUBIX and SHOX, in particular, were frequently utilized from 2010 to 2020 for simulating wireless sensor networks and large-scale IoT deployments [118]. Although these simulators were not retrieved through the keyword-based search methodology applied in this review, their  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **NS-3** & **Cooja** & **OMNeT++** \\ \hline Scalability & High (supports \(>\) 100,000 nodes) but requires high computational resources & Moderate (scalability limited due to memory constraints) & High (supports large-scale networks but has performance bottlenecks) \\ \hline Visualization Tools & Supports NetAnim (2D) and NetSimulyzer (3D) & Provides GUI-based visualization for WSN interactions & Includes advanced graphical tools for visualization \\ \hline Energy Modeling & Supports energy consumption analysis for battery-operated IoT devices & Specialized in low-power consumption analysis for WSNs & Supports power consumption modeling but requires additional modules \\ \hline Suitability for IoT & Well-suited for large-scale, high-bandwidth IoT simulations & Best for resource-constrained, low-power IoT networks & Suitable for large-scale, heterogeneous IoT networks \\ \hline Fault Tolerance and Noise Modeling & Includes noise models, packet drop analysis, and fault tolerance testing & Supports configurable noise models and basic fault tolerance mechanisms & Provides high configurability for noise and fault tolerance studies \\ \hline Ease of Use & Steep learning curve due to extensive configuration options & User-friendly GUI; easier setup for WSN simulations & Complex setup but highly modular and flexible for advanced users \\ \hline Common Use Cases & IoT, Space IoT, large-scale networks, 5G/6G studies & Low-power IoT, WSNs, sensor-based networks & Complex network scenarios, heterogeneous IoT, satellite-based networks \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._relevance in earlier studies justifies their mention as noteworthy alternatives for diffusion algorithm analysis.