Numerous frameworks and libraries are provided by Python, which are considered as more efficient and useful in the processes of emulating IoT devices and developing IoT simulators. Feel free to reach out to us, as we are committed to providing prompt assistance. We guarantee swift responses to all inquiries and concerns. Upon receiving your request, we will promptly begin working on your paper, starting with a well-crafted title and abstract. Our team will ensure a clear explanation of the research problem and its objectives.  By examining the simulation of IoT in Python, we list out various fascinating project topics and plans:

1. Smart Home Simulator

• Outline:
  • Along with MQTT-based devices, develop a smart home simulator.
  • Plan to simulate various devices such as safety sensors, thermostats, and smart bulbs.
• Goals:
  • A network of MQTT devices have to be created. Through a central hub, regulate them.
  • For the purpose of energy preserving, apply a recommendation system.
• Major Tools:
  • IoT Framework: Node-RED
  • MQTT Library: paho-mqtt
  • MQTT Broker: Eclipse Mosquito

import paho.mqtt.client as mqtt

import random

import time

# MQTT Broker settings

BROKER = “mqtt.eclipseprojects.io”

TOPIC = “smart_home/livingroom/light”

def on_connect(client, userdata, flags, rc):

    print(“Connected to broker with result code ” + str(rc))

def simulate_smart_bulb(client):

    while True:

        state = random.choice([“ON”, “OFF”])

        client.publish(TOPIC, state, qos=1)

        print(f”Published {state} to {TOPIC}”)

        time.sleep(5)

# MQTT Client setup

client = mqtt.Client(“smart_bulb_simulator”)

client.on_connect = on_connect

client.connect(BROKER, 1883, 60)

client.loop_start()

simulate_smart_bulb(client)

2. Industrial IoT Network Simulator

• Outline:
  • With MQTT-based sensors, an industrial IoT network must be simulated.
  • It is also important to simulate power sensors, vibration, and temperature.
• Goals:
  • To track machine wellness, a network of simulated sensors has to be created.
  • Aim to identify abnormalities by analyzing network traffic.
• Major Tools:
  • Data Analysis: numpy, pandas
  • MQTT Library: paho-mqtt
  • Visualization: Node-RED, Grafana.

import paho.mqtt.client as mqtt

import random

import time

# MQTT Broker settings

BROKER = “mqtt.eclipseprojects.io”

TOPIC_TEMP = “factory/machine1/temp”

TOPIC_VIBRATION = “factory/machine1/vibration”

def on_connect(client, userdata, flags, rc):

    print(“Connected to broker with result code ” + str(rc))

def simulate_industrial_sensors(client):

    while True:

        temp = round(random.uniform(20.0, 80.0), 2)

        vibration = round(random.uniform(0.1, 10.0), 2)

        client.publish(TOPIC_TEMP, temp, qos=1)

        client.publish(TOPIC_VIBRATION, vibration, qos=1)

        print(f”Published Temp: {temp}C, Vibration: {vibration}g”)

        time.sleep(5)

# MQTT Client setup

client = mqtt.Client(“industrial_sensors_simulator”)

client.on_connect = on_connect

client.connect(BROKER, 1883, 60)

client.loop_start()

simulate_industrial_sensors(client)

3. IoT Network Traffic Anomaly Detection Simulator

• Outline:
  • Using MQTT-based devices, simulate an IoT network.
  • Specifically for the training of IDS, create abnormal and common network traffic.
• Goals:
  • In order to train intrusion detection systems (IDS), create a dataset.
  • For anomaly identification, machine learning models must be applied and assessed.
• Major Tools:
  • Network Protocols: CoAP, MQTT
  • Machine Learning: TensorFlow, Scikit-Learn
  • Data Analysis: numpy, pandas

import paho.mqtt.client as mqtt

import random

import time

import pandas as pd

# MQTT Broker settings

BROKER = “mqtt.eclipseprojects.io”

TOPICS = [“iot/device1/temp”, “iot/device1/motion”, “iot/device2/light”]

# Initialize dataset

dataset = []

def on_connect(client, userdata, flags, rc):

    print(“Connected to broker with result code ” + str(rc))

def simulate_traffic(client):

    for topic in TOPICS:

        normal_values = [“normal”, “anomaly”]

        while True:

            value = random.choice(normal_values)

            dataset.append([topic, value])

            client.publish(topic, value, qos=1)

            print(f”Published {value} to {topic}”)

            time.sleep(2)

def save_dataset():

    df = pd.DataFrame(dataset, columns=[“Topic”, “Traffic”])

    df.to_csv(“iot_network_traffic.csv”, index=False)

    print(“Dataset saved to iot_network_traffic.csv”)

# MQTT Client setup

client = mqtt.Client(“network_traffic_simulator”)

client.on_connect = on_connect

client.connect(BROKER, 1883, 60)

client.loop_start()

simulate_traffic(client)

save_dataset()

4. Smart Agriculture Simulator

• Outline:
  • A smart farming tracking system has to be simulated.
  • Then, simulate some important aspects like weather sensors, temperature, and soil moisture.
• Goals:
  • To track weather and soil states, create an agriculture network.
  • For crop wellness and irrigation, apply predictive analytics.
• Major Tools:
  • Data Analysis: numpy, pandas
  • MQTT Library: paho-mqtt

import paho.mqtt.client as mqtt

import random

import time

# MQTT Broker settings

BROKER = “mqtt.eclipseprojects.io”

TOPIC_SOIL = “farm/field1/soilmoisture”

TOPIC_WEATHER = “farm/field1/weather”

def on_connect(client, userdata, flags, rc):

    print(“Connected to broker with result code ” + str(rc))

def simulate_agriculture_sensors(client):

    while True:

        soil_moisture = round(random.uniform(10.0, 40.0), 2)

        weather = random.choice([“sunny”, “rainy”, “cloudy”])

        client.publish(TOPIC_SOIL, soil_moisture, qos=1)

        client.publish(TOPIC_WEATHER, weather, qos=1)

        print(f”Published Soil Moisture: {soil_moisture}%, Weather: {weather}”)

        time.sleep(10)

# MQTT Client setup

client = mqtt.Client(“agriculture_sensors_simulator”)

client.on_connect = on_connect

client.connect(BROKER, 1883, 60)

client.loop_start()

simulate_agriculture_sensors(client)

5. IoT Device Energy Consumption Simulator

• Outline:
  • Initially, simulate IoT devices appropriately. Then, their energy utilization has to be assessed.
  • The power usage of various devices should be tracked and enhanced.
• Goals:
  • For IoT devices, aim to create an energy utilization model.
  • Different protocols such as LoRaWAN, CoAP, and MQTT must be applied and improved.
• Major Tools:
  • Network Protocols: CoAP, MQTT
  • Energy Tracking Libraries: pyRAPL, psutil
  • Data Analysis: numpy, pandas

import paho.mqtt.client as mqtt

import random

import time

import psutil

# MQTT Broker settings

BROKER = “mqtt.eclipseprojects.io”

TOPIC = “energy/iot/device1/temp”

def on_connect(client, userdata, flags, rc):

    print(“Connected to broker with result code ” + str(rc))

def simulate_device_energy(client):

    while True:

        temp = round(random.uniform(20.0, 30.0), 2)

        energy = psutil.cpu_percent(interval=1)

        client.publish(TOPIC, f”{temp},{energy}”, qos=1)

        print(f”Published Temp: {temp}C, Energy: {energy}%”)

        time.sleep(5)

# MQTT Client setup

client = mqtt.Client(“device_energy_simulator”)

client.on_connect = on_connect

client.connect(BROKER, 1883, 60)

client.loop_start()

simulate_device_energy(client)

Does the LoRa and LoRaWAN module in the NS3 support Python Is there a Python binding for them?

Specifically, Python bindings are provided in NS-3 with the aid of a pybindgen tool. To interact with C++ modules in NS-3, this tool effectively supports Python. For dealing with Python, we provide some major procedures to consider, in the case of LoRaWAN and LoRa modules:

1. Install NS-3 with Python Bindings: First, make sure that you have installed NS-3 along with Python bindings, which are functioning properly in your system. Consider the following procedures, if you have not installed NS-3 previously:

git clone https://gitlab.com/nsnam/ns-3-dev.git

cd ns-3-dev

./ns3 configure –enable-examples –enable-tests –enable-python-bindings

./ns3 build

2. Copy and Append LoRa/LoRaWAN Modules: The LoRaWAN extension has to be copied. Then, append this to your NS-3 modules in a proper way.

git clone https://github.com/signetlabdei/lorawan.git

cd lorawan

./waf configure

./waf build

To the NS-3 contrib directory, copy the lorawan directory.

cp -r lorawan $NS3_HOME/contrib

cd $NS3_HOME

./ns3 configure –enable-python-bindings

./ns3 build

3. Validate Python Bindings for LoRa/LoRaWAN: For the LoRaWAN/LoRa module, the Python bindings must be created, so make sure this properly.

Below build/bindings/, you must have files such as _ns3_module_lorawan.so.   

4. Access LoRa/LoRaWAN Modules in Python: Utilizing import ns.lorawan, you can import Python bindings into Python only after ensuring that the Python bindings are created in proper location. Based on drafting a basic Python script through the utilization of LoRaWAN/LoRa module and NS-3, a general instance is specified below:

import ns.core

import ns.network

import ns.mobility

import ns.internet

import ns.lorawan

# Create a LoRaWAN helper

lorawanHelper = ns.lorawan.LorawanHelper()

# Set up a LoRaWAN network as per your needs