Table 1.
Existing IoT Simulators.
| Tool | Focus | Key Features | Limitations |
|---|---|---|---|
| IoTNetSim | End-to-end IoT services | Detailed modeling of IoT nodes, sensors, and mobility. Supports various protocols. Modular and extendable architecture. | limitations in supporting certain sensor types due to the complexity of modeling their mobility. |
| EdgeMiningSim | IoT data mining in edge computing | Multi-layered architecture. Supports task offloading and edge server management. High scalability. | Requires substantial computational resources. |
| Large-scale NB-IoT Simulator | IoT in smart cities | Integrates real geographical data. Tailored for NB-IoT and LTE devices. Discrete-event simulation approach. | Limited to NB-IoT and LTE devices. |
| ASSIST | Social IoT environments | Models social interactions among IoT devices. Supports common IoT protocols. Scalable for extensive networks. | Primarily supports SIoT environments. |
| Co-simulator for Smart Grids | Smart grids | Integrates Gridlab-D and CORE. GUI for efficiency and software emulation for fidelity. | Focus on smart grids, limited IoT applicability. |
| GVSoC | RISC-V-based IoT processors | Event-driven, balances accuracy and speed. Highly configurable for DSE. | Focuses on RISC-V, lacks support for other architectures. |
| Large-Scale IoT Simulator | IoT systems in urban settings | Simulates thousands of devices. High level of generality. | Limited to application-layer perspective, limits its suitability for testing low-level networking aspects |
| IoT simulator in [22] | Energy management in city districts | Integrates diverse data sources. Leverages LinkSmart Middleware. |
Requires expansion for more IoT devices such as weather and traffic sensors. |
| LoRa-MAB | Resource allocation in LoRaWAN | Event-driven framework. Provides insights into network performance. |
Focus on LoRaWAN, may not cover all IoT scenarios. |
| Dynamic Co-simulation with Multi-Agent System | Modular IoT system simulation | Enables separate simulation of IoT components. Adaptable and modular. | Complex setup with multiple simulation tools, limitation for adding intelligence to the models |
| MobIoTSim | Mobile IoT device simulation | Emulates devices, generates real-time data. Connects to cloud gateways. | May not fully replicate real device behavior. |
| RelIoT | Reliability in IoT networks | Integrates modules for power, performance, and temperature. Estimates device reliability. | Needs support for more complex reliability models. |
| MoSIoT | IoT healthcare monitoring | MDE for scenario creation. Supports commercial IoT hubs. |
Focus on healthcare, may not cover other IoT areas. |
| Hybrid Simulation-Based in [23] | Large-scale IoT applications | Combines simulation and real-life testing. Utilizes PADS methodology. |
Focused on system level, may not address detailed IoT protocols. |
| SimulateIoT | IoT system design and simulation | DSL for scalable IoT systems. Model-Driven Development. |
Limited node mobility and hardware simulation. |
| SimulateIoT-FIWARE | IoT simulation on FIWARE | Extends SimulateIoT for FIWARE. Generates code for specific FIWARE technology. | Tailored to FIWARE, limited other platform applicability. |
| MyiFogSim | VM migration in fog computing | Supports VM migration policies. Models mobile users and wireless access points. | Needs improvement in scalability. |
| EdgeCloudSim | IoT services over Edge and Cloud | Detailed analysis of service time, and energy consumption. Accommodates mobile devices. | Missing nuances of diverse hardware features. |
| Mercury | Real-time fog computing scenarios | Focuses on low latency, high throughput, and 5G. Data stream analytics and federated computation offloading. | Cloud computing not included in initial approach. |
| IoTSim-Edge | IoT and edge computing challenges | Models device diversity, protocols, mobility. Supports mobile IoT devices. | Does not consider energy consumption of infrastructure. |
| SimIoT | Cloud computing and IoT | Models users, data centers, virtual machines. Optimizes message exchanging. Supports heterogeneity. | Lacks real-world IoT implementation and explicit energy efficiency measures. |
| SimulateIoT-Mobile | IoT environments with mobile nodes | Extends SimulateIoT for mobile scenarios. Utilizes MQTT for mobility management. | Assumes guaranteed connectivity, which may not reflect real-world conditions. |
| PIoT | Network performance of IoT in cities | Front-end for simulation configuration. Models millions of IoT devices using cellular infrastructure. | Focuses on network performance, less on IoT device energy sources. |
| Contiki-Cooja | Network simulation for Contiki OS | Enables specification of Contiki motes. Provides crucial network data post-simulation. | Emphasizes hardware and network challenges, not IoT communication models. |
| ABS-SmartComAgri | Precision agriculture | Manages pesticide usage. Implements smart communication protocols. | Specifically for precision agriculture, not general IoT applications. |
| FS-IIoTSim | Industrial IoT systems | Supports communication protocols. Scenario modeling and performance evaluation. | Tailored for industrial environments, may not cover broader IoT applications. |
| IoTSim-Osmosis | Integrated edge-cloud IoT applications | Models dynamic workload transfer. Unified modeling for IoT in edge-cloud environments. | Limited wireless communication layer, fixed IoT device locations. |
| IoTSim-Osmosis-RES | Sustainable IoT ecosystems | Incorporates renewable energy sources. Models energy management and network infrastructure. | Does not support direct communication between IoT devices. |
| SimulatorBridger | VANETs in urban mobility | Bridges IoT simulation with traffic simulation. Manages mobility and communications of IoT devices. | Does not support direct communication between IoT devices, lack of supporting security model. |