IOT Simulator Python

Several frameworks and tools are there in this current technological era to deal with IoT simulators by employing Python language. According to specific requirements and considerations, appropriate frameworks and tools have to be selected. To assist you in this process, we suggest some major and efficient frameworks and tools: 

  1. NS-3 with Python Bindings
  • Outline:
    • NS-3 simulator assists Python scripting with the aid of PyViz. It is specifically referred to as a discrete-event network simulator.
    • Arrangement of network models based on Python is enabled by this simulator.

sudo apt update

sudo apt install -y python3-pip

pip3 install –user pybindgen

  • Utilization:
    • For simulating a basic network, consider the following instance of script:

import ns.applications

import ns.core

import ns.internet


import ns.point_to_point

def main(argv):


    node1 =

    node2 =

    nodes =, node2)

    pointToPoint = ns.point_to_point.PointToPointHelper()

    pointToPoint.SetDeviceAttribute(“DataRate”, ns.core.StringValue(“5Mbps”))

    pointToPoint.SetChannelAttribute(“Delay”, ns.core.StringValue(“2ms”))

    devices = pointToPoint.Install(nodes)

    stack = ns.internet.InternetStackHelper()


    address = ns.internet.Ipv4AddressHelper()


    interfaces = address.Assign(devices)

    echoServer = ns.applications.UdpEchoServerHelper(9)

    serverApps = echoServer.Install(node2)



    echoClient = ns.applications.UdpEchoClientHelper(interfaces.GetAddress(1), 9)

    echoClient.SetAttribute(“MaxPackets”, ns.core.UintegerValue(1))

    echoClient.SetAttribute(“Interval”, ns.core.TimeValue(ns.core.Seconds(1.0)))

    echoClient.SetAttribute(“PacketSize”, ns.core.UintegerValue(1024))

    clientApps = echoClient.Install(node1)






if __name__ == “__main__”:

    import sys


  1. Cooja (Contiki OS) with Python Scripting
  • Outline:
    • Cooja assists scripting efficiently through Python. It is considered as an IoT-based network simulator.
    • The process of automating network tests and examining outcomes is also supported by this simulator.
  • Installation Process:
    • In the Contiki-NG repository, adhere to the configuration guidelines for Cooja.
  • Utilization:
    • To automate node deployment, examine the following instance of Python script:

import java.util.ArrayList as ArrayList

import javax.swing as swing

from coojatest import *

def setup_experiment(simulation):

    nodes = ArrayList()



    nodes.get(0).setPosition(100, 200)

    nodes.get(1).setPosition(400, 200)


    print(“Simulation running…”)


  1. IoTSim-Edge with Python
  • Outline:
    • For simulation, IoTSim-Edge offers Python bindings, and it is developed on CloudSim.
    • This simulator is majorly modeled for IoT-cloud-edge computing contexts.
  • Installation Process:
    • The guidelines offered in the IoTSim-Edge repository have to be followed for installation.
  • Utilization:
    • For IoT simulation, focus on the below specified Python script instance:

from py4j.java_gateway import JavaGateway, GatewayParameters, CallbackServerParameters

gateway = JavaGateway(




IoTSim = gateway.entry_point.getIoTSimEdge()

# Initialize the simulation



print(“Simulation completed!”)


  1. SCoRE with Python
  • Outline:
    • SCoRE stands for Smart City Resource Emulator generally modeled for smart city-based applications. It is known as a Python-related simulator.
    • It combines IoT devices along with the contexts of cloud and edge computing.

pip install score-emulator

  • Utilization:
    • For the simulation of an IoT network, consider the following Python script example:

from score import IoTDevice, EdgeNode, CloudNode, Network, Simulation

# Create IoT Devices

temp_sensor = IoTDevice(“TempSensor”, “sensor”, 0.1, 0.05)

motion_sensor = IoTDevice(“MotionSensor”, “sensor”, 0.1, 0.05)

# Create Edge and Cloud Nodes

edge_node = EdgeNode(“EdgeNode”, 1, 0.1, 0.1)

cloud_node = CloudNode(“CloudNode”, 10, 1, 0.5)

# Create Network and Simulation

network = Network()





simulation = Simulation(network)

# Retrieve Results

results = simulation.get_results()


  1. SimPy (Discrete Event Simulation Framework)
  • Outline:
    • To develop conventional IoT network simulators, SimPy can be employed effectively.
    • It is majorly termed as a Python-related discrete-event framework used for simulation processes.

pip install simpy

  • Utilization:
    • Python script instance for the simulation of conventional IoT:

import simpy

def sensor(env, name, interval, queue):

    while True:

        yield env.timeout(interval)

        queue.put_nowait(f”{name} – Time: {}”)

def cloud(env, queue):

    while True:

        msg = yield queue.get()

        print(f”Processing: {msg} at Time {}”)

        yield env.timeout(1)

env = simpy.Environment()

queue = simpy.Store(env)

env.process(sensor(env, “TempSensor”, 5, queue))

env.process(sensor(env, “HumiditySensor”, 3, queue))

env.process(cloud(env, queue))

Should I learn Python C or C to work in IoT projects and what is the difference between Python and any other language to learn for IoT?

Should You Learn C, C++, or Python for IoT-based Projects?

In terms of various considerations, the appropriate programming language like C, C++, or Python has to be selected. While choosing the language, it is important to examine the particular hardware which you plan to deal with and the kind of IoT projects that you are passionate about. 

  1. Python for IoT
  • Advantages:
  • User-friendliness: Python is considered as an easy language for learners because of having a basic and explicit syntax.
  • Wide range of Libraries: It has several IoT-based libraries such as MQTT, GPIOZero, and Adafruit_DHT.
  • Environment Support: Various environments such as ESP32, Linux-related IoT devices, and Raspberry Pi offer great assistance for Python.
  • Modeling: Python is highly suitable for modeling IoT applications in a rapid manner.
  • General Application Areas:
  • Data processing and edge computing with the aid of Node-RED, EdgeX Foundry.
  • Interaction protocols by employing requests, paho-mqtt, etc.
  • Analysis and visualization of data using matplotlib and pandas.
  • Limitations:
  • Speed: On account of the understandable nature of Python language, it is slower than C or C++.
  • Memory Utilization: When compared to compiled languages, it utilizes more memory.
  1. C for IoT
  • Advantages:
  • Low-Level Control: C is more appropriate for microcontrollers and offers permissions to low-level system functions.
  • Effectiveness: It requires very less memory and the compiled codes can be executed rapidly.
  • Actual-time Systems: For the actual-time systems that are with rigid timing needs, C is considered as highly ideal.
  • General Application Areas:
  • Interaction protocols applying with Zigbee and MQTT.
  • Programming resource-limited microcontrollers (for instance: ESP8266, Arduino)
  • Integrated systems such as RTOS-related systems with actual-time conditions.
  • Limitations:
  • Complicated Syntax: In-depth interpretation of memory handling is needed.
  • Extensive Creation Time: In contrast to Python, C is more complicated to write and debug.
  1. C++ for IoT
  • Advantages:
  • Object-Oriented: Various latest programming characteristics are provided by C++, including templates and classes.
  • Performance: Based on the compiled nature of C++, it is examined as effective as well as rapid.
  • Ideal Libraries: Valuable methods, utilities, and containers are offered by STL.
  • General Application Areas:
  • For complicated IoT applications, utilization of frameworks such as ESP-IDF and Mbed OS.
  • Integrated systems which specifically need object-oriented programming.
  • Other common use cases include IoT devices that are with high processing power (for example: Raspberry Pi).
  • Limitations:
  • Learning Curve: C++ has the latest characteristics, so it is highly difficult when compared to Python.
  • Memory Handling: For the security purpose, it needs meticulous memory management.

Comparison: Python vs. C/C++ for IoT





Ease of Use

High (Beginner-friendly)

Moderate (Complex syntax)

Moderate (Complex syntax)


Moderate (Interpreted)

High (Compiled)

High (Compiled)

Development Speed

High (Quick Prototyping)



Memory Usage




Low-Level Access




Real-Time Systems

Not Suitable



Platform Support

Excellent (Linux-based)

Excellent (Microcontrollers)

Excellent (Microcontrollers & Linux)


Extensive (IoT-specific)

Standard Libraries

Standard Libraries







  • Python:
    • For various objectives like edge computing projects, data processing, and quick modeling, Python is a more appropriate language.
    • To construct IoT applications rapidly or for Linux-related environments such as Raspberry Pi, it is considered as highly suitable.
  • C:
    • C is more efficient for integrated applications that need rigid timing control and for actual-time systems.
    • It is majorly optimal for low-level programming on microcontrollers which have limited resources.
  • C++:
    • C++ language is widely selected for various frameworks such as ESP-IDF and Mbed OS.
    • For the difficult applications which need object-oriented programming, it is a more perfect language.


  1. Learners and Quick Prototyping:
  • To develop and examine IoT projects in a rapid manner, gain knowledge based on Python.
  • Some of the important as well as excellent environments are Linux-related IoT settings, NodeMCU, and Raspberry Pi.
  1. Embedded Systems and Microcontrollers:
  • For programming microcontrollers such as ESP32 and Arduino, acquire expertise relevant to C/C++.
  • In order to facilitate the creation process, employ major frameworks including PlatformIO and Arduino IDE.
  1. Latest and Complicated Projects:
  • It is approachable to investigate various significant frameworks such as FreeRTOS, ESP-IDF, and Mbed OS.
  • Specifically for intricate IoT-based projects that include object-oriented planning, utilize C++ language.
IOT Simulator Python Topics

IOT Simulator Python Projects

Elevate your grades with as your research partner for IOT Simulator Projects in Python. Our team of experts offers top-notch simulation support that goes beyond just guidance. We provide continuous checkpoints to keep you on the right path and help you meet important deadlines.

  1. LDA-2IoT: A level dependent authentication using two factor for IoT paradigm
  2. VLSI implementation of lightweight cryptography technique for FPGA-IOT application
  3. IoT and health monitoring wearable devices as enabling technologies for sustainable enhancement of life quality in smart environments
  4. Blockchain-based Intrusion Detection System of IoT urban data with device authentication against DDoS attacks
  5. An overview of IoT architectures, technologies, and existing open-source projects
  6. SI4IoT: A methodology based on models and services for the integration of IoT systems
  7. Anomaly based network intrusion detection for IoT attacks using deep learning technique
  8. BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices
  9. A voice spoofing detection framework for IoT systems with feature pyramid and online knowledge distillation
  10. A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation
  11. Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm
  12. Maximizing data gathering and energy efficiency in UAV-assisted IoT: A multi-objective optimization approach
  13. SPMAC: Secure and privacy-preserving multi-authority access control for fog-enabled IoT cloud storage
  14. Development of a distributed MR-IoT method for operations and maintenance of underground pipeline network
  15. ZT-Access: A combining zero trust access control with attribute-based encryption scheme against compromised devices in power IoT environments
  16. A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks
  17. Hybrid Bayesian optimization hypertuned catboost approach for malicious access and anomaly detection in IoT nomalyframework
  18. Recognition of IoT-based fire-detection system fire-signal patterns applying fuzzy logic
  19. Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems
  20. Distributed robust channel allocation for clustered cognitive radio-based IoT networks using graph theory


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