LIST OF IOT SIMULATOR

In the process of applying an efficient research methodology, choosing an appropriate IoT simulator is considered as more important. Explore a variety of IoT simulator topics with the help of cutting-edge simulation tools and techniques outlined in your paper. Our team of experts is dedicated to offering comprehensive thesis writing assistance to researchers at every stage of their academic journey. Share your requirements with us, and we’ll be more than happy to assist you. For assisting you to select the suitable as well as effective simulator for your research, we offer extensive collections of IoT-based simulators along with their major characteristics:

1. NS-3 (Network Simulator 3)

• Explanation: It is referred to as a flexible and modular network simulator. It offers extensive assistance for IoT protocols.
• Major Characteristics:
  • Protocols: LTE, Wi-Fi, Zigbee, 6LoWPAN, and LoRaWAN.
  • Scalability: It has high scalability (offers support to extensive IoT networks).
  • Programming Language: C++.
  • Environments: Linux, macOS, and Windows (through Cygwin) are appropriate platforms.
• Effective For:
  • It is more suitable for assessing IoT network protocols, network topologies, and routing methods.
• Documentation: NS-3 Documentation.
• Instances:
  • Major instance is LoRaWAN Module for NS-3.

2. OMNeT++

• Explanation: OMNeT++ is an open-source and modular network simulator.
• Major Characteristics:
  • Protocols: CoAP, LTE (through INET, MIXIM modules), 6LoWPAN, Wi-Fi, LoRaWAN, and Zigbee.
  • Scalability: OMNeT++ has high scalability (It assists extensive networks).
  • Programming Language: C++
  • Environments: Linux, macOS, and Windows are suitable.
• Effective For:
  • It is efficient for evaluating routing methods, IoT network frameworks, and creating custom protocols.
• Documentation: OMNeT++ Documentation.
• Instances:
  • INET Framework is the best instance.

3. Cooja (Contiki OS Simulator)

• Explanation: It is generally considered as a wireless sensor and IoT network simulator.
• Major Characteristics:
  • Protocols: BLE, TSCH, CoAP, RPL, and 6LoWPAN.
  • Scalability: It provides assistance up to hundred nodes and its scalability is always medium.
  • Programming Language: C.
  • Environments: Potential platforms are macOS, Windows, and Linux.
• Effective For:
  • Cooja is highly appropriate for assessing IoT network protocols and Contiki OS applications.
• Documentation: Cooja Wiki.
• Instances:
  • General instance is the Cooja Tutorial.

4. CupCarbon

• Explanation: CupCarbon is known as an IoT and smart city network simulator.
• Major Characteristics:
  • Protocols: Sigfox, Lora, 6LoWPAN, Wi-Fi and Zigbee.
  • Scalability: CupCarbon has medium scalability and provides assistance for thousands of nodes.
  • Programming Language: Python, Java.
  • Environments: Linux, macOS, and Windows are more ideal.
• Effective For:
  • For the process of simulating practical urban IoT contexts and energy utilization, CupCarbon is very helpful and effective.
• Documentation: CupCarbon Documentation.
• Instances:
  • Smart City Simulation Video is a significant example.

5. iFogSim

• Explanation: Resource management is the major concentration of an IoT and fog computing simulator.
• Major Characteristics:
  • Protocols: It includes some common IoT protocols such as MQTT and HTTP.
  • Scalability: iFogSim aids thousands of nodes and has medium scalability.
  • Programming Language: Java.
  • Environments: Suitable environments are Linux, macOS, and Windows.
• Effective For:
  • In fog computing, the process of assessing resources handling strategies can be done by iFogSim.
• Documentation: iFogSim GitHub
• Instances:
  • Consider general examples like iFogSim Introduction Paper.

6. Matlab/Simulink

• Explanation: For IoT simulations, it is specified as a high-level platform.
• Major Characteristics:
  • Protocols: Wi-Fi (through toolboxes), Bluetooth, LoRa, and Zigbee.
  • Scalability: It has high scalability (particularly based on computing resources).
  • Programming Language: MATLAB.
  • Environments: Potential platforms are macOS, Windows, and Linux.
• Effective For:
  • MATLAB/Simulink is effective in IoT applications, for signal processing and rapid modeling.
• Documentation: Matlab Documentation.
• Instances:
  • Some major Simulink IoT Examples.

7. NetSim

• Explanation: NetSim is referred to as an industrial network simulator and has particular IoT-based modules.
• Major Characteristics:
  • Protocols: CoAP, Wi-SUN, 6LoWPAN, MQTT, LoRaWAN, and Zigbee.
  • Scalability: NetSim provides assistance for thousands of nodes and has medium scalability.
  • Programming Language: C/C++.
  • Environments: Windows is the ideal platform.
• Effective For:
  • It is highly better for the study of extensive IoT protocol with visualization.
• Documentation: NetSim Documentation.

8. GNS3 (Graphical Network Simulator)

• Explanation: GNS3 is a prominent network simulator. For IoT devices, it offers assistance.
• Major Characteristics:
  • Protocols: Bluetooth (through virtual appliances), Wi-Fi, Zigbee, and LoRaWAN.
  • Scalability: Like MATLAB/Simulink, it is also based on computing resources, but generally has medium scalability.
  • Programming Language: Python.
  • Environments: Linux, macOS, and Windows are highly suitable.
• Effective For:
  • GNS3 is best for the simulation of secure IoT networks with appliances, firewalls, and virtual routers.
• Documentation: GNS3 Documentation.

9. TOSSIM (TinyOS Simulator)

• Explanation: For the TinyOS-related wireless sensor networks, TOSSIM is examined as an appropriate simulator.
• Major Characteristics:
  • Protocols: Zigbee, CoAP, RPL, and 6LoWPAN.
  • Scalability: TOSSIM assists hundreds of nodes and has medium scalability.
  • Programming Language: C++, Python.
  • Environments: Ideal platforms are Linux, macOS, and Windows.
• Effective For:
  • It is highly useful and efficient for assessing IoT network protocols and TinyOS applications.
• Documentation: TOSSIM Documentation.

10. OneM2M Simulator

• Explanation: This simulator is essential for simulating OneM2M-compliant IoT frameworks.
• Major Characteristics:
  • Protocols: MQTT, CoAP, and OneM2M.
  • Scalability: OneM2M simulator has medium scalability.
  • Programming Language: Java
  • Environments: Some appropriate platforms are Linux, macOS, and Windows.
• Effective For:
  • This simulator is more helpful and robust for the evaluation of OneM2M-compliant IoT solutions and application services.
• Documentation: OneM2M Simulator GitHub.

Is it possible to use MATLAB for simulation of IoT?

Yes, it is possible to employ MATLAB for the simulation of IoT. Specifically, it is considered as an efficient software tool for the simulation process. The following are general guidelines that assist you to use MATLAB for the process of IoT simulation:

Procedures to Simulate IoT in MATLAB

1. Describe the IoT System Necessities:

• The particular needs of your IoT systems have to be decided clearly.
• Network Scale: It denotes the count of devices.
• Communication Protocols: Wi-Fi, Bluetooth, LoRaWAN, and Zigbee.
• Use cases: Agriculture, healthcare, and smart city are some of the possible use cases.

2. Arrange the Development Platform:

• Install MATLAB:
• It is important to ensure that you have all the essential toolboxes for the simulation process, such as ThingSpeak, Communication Toolbox, Simulink, etc.

3. Employ MATLAB Toolboxes for IoT Simulation:
4. Simulink:

• It is considered as a graphical platform specifically used to design and simulate IoT systems efficiently.
• Instance of Application: Zigbee-Based IoT Network Simulation.
  • For the simulation of a Zigbee network, utilize the Zigbee Protocol block.
  • Through the use of Simulink blocks, append devices or custom sensors.

% Simulate a simple Zigbee IoT Network in Simulink

open_system(‘zigbeeIoTNetworkModel’)

sim(‘zigbeeIoTNetworkModel’)

2. SimEvents:

• In IoT networks, simulate discrete-event systems appropriately.
• Instance of Application: Traffic Generation in an IoT Network.

% Create a simple traffic generator using SimEvents

open_system(‘trafficGeneratorModel’)

sim(‘trafficGeneratorModel’)

3. Communication Toolbox:

• Various communication protocols such as Wi-Fi, LoRaWAN, and Zigbee have to be simulated.
• Instance of Application: LoRaWAN Communication Simulation.

% Define LoRa parameters

sf = 7; % Spreading Factor

bw = 125e3; % Bandwidth

cr = 4/5; % Coding Rate

snr = 5; % Signal-to-Noise Ratio

% Generate LoRa waveform

loraWaveform = loraWaveformGenerator(sf, bw, cr, snr);

% Visualize the waveform

loraSpectrumAnalyzer = dsp.SpectrumAnalyzer(‘SampleRate’, bw);

loraSpectrumAnalyzer(loraWaveform)

4. ThingSpeak IoT Analytics:

• For transferring and obtaining data from IoT devices, employ the ThingSpeak API.
• Instance of Application: Visualize Real-Time IoT Data.

% Load ThingSpeak channel data

channelID = 123456;

readAPIKey = ‘ABCDEFG’;

data = thingSpeakRead(channelID, ‘Fields’, [1,2], ‘ReadKey’, readAPIKey);

% Plot the data

plot(data.Time, data.Field1)

title(‘Temperature Over Time’)

xlabel(‘Time’)

ylabel(‘Temperature (°C)’)

5. MATLAB-based Packages:

• In your simulation, combine actual-world devices.
• Arduino Support Package: This package is generally employed to control Arduino boards.
• Raspberry Pi Support Package: It helps to communicate with Raspberry Pi.
• ESP8266/ESP32 Support Package: Easier to link ESP boards to MATLAB with the aid of this package.

Instance of Application: Monitor Arduino Sensors

% Create an Arduino object

a = arduino(‘COM4’, ‘Uno’, ‘Libraries’, ‘I2C’);

% Read data from a temperature sensor

tempSensor = addon(a, ‘Adafruit/MCP9808’, ‘I2CAddress’, ‘0x18’);

temperature = readTemperature(tempSensor);

fprintf(‘Temperature: %.2f °C\n’, temperature)