SIMULATION SOFTWARE FOR IOT

Several simulation tools are there for the IoT-based projects. According to the project requirements, a suitable tool has to be selected. To assist you in this selection process, we recommend various prominent simulation tools that are appropriate for IoT projects:

  1. NS-3 (Network Simulator 3):
  • Explanation: NS-3 assists different protocols such as 6LoWPAN and LoRaWAN. It is referred to as a discrete-event network simulator.
  • Characteristics:
    • It has various accessible modules for IoT protocols and wireless networks (like LTE, Wi-Fi).
    • Offers assistance for the simulation of extensive networks.
  • Application Areas:
    • Testing of IoT protocols like Zigbee and LoRaWAN.
    • Performance assessment of extensive IoT networks.
  • Major Resources:
    • NS-3 Documentation
  1. Cooja (Contiki OS Network Simulator):
  • Explanation: Cooja is efficient in the process of simulating IoT networks and wireless sensor networks (WSNs). It is a phase of the Contiki OS platform.
  • Characteristics:
    • Cooja simulates RPL, 6LoWPAN, and other major IoT-based protocols.
    • It also assists cross-level simulation, including network and hardware.
  • Application Areas:
    • Creation and debugging of Contiki OS-based applications.
    • Assessing the network performance of RPL and 6LoWPAN.
  • Major Resources:
    • Cooja Documentation.
  1. CupCarbon:
  • Explanation: CupCarbon enables the practical visualization and evaluation of wireless sensor networks. It is considered as a smart city and IoT network simulator.
  • Characteristics:
    • Provides assistance for various protocols like Sigfox, 6LoWPAN, Zigbee, and LoRa.
    • Another feature is the practical 2D and 3D simulation platforms.
  • Application Areas:
    • Analysis of energy utilization in WSNs.
    • Designing and simulation of smart city network.
  • Major Resources:
    • CupCarbon Documentation
  1. OMNeT++:
  • Explanation: OMNeT++ is an open-source, modular simulation architecture. For the simulation of the IoT network, it has different extensions.
  • Characteristics:
    • Through various models such as MiXiM and INET, it assists protocol and network simulation.
    • By means of extensions, this simulator enables different IoT-based protocols such as 6LoWPAN, LoRaWAN, and Zigbee.
  • Application Areas:
    • Simulations of extensive IoT networks.
    • Performance assessment of IoT protocol.
  • Major Resources:
    • OMNeT++ Documentation
  1. ONE (Opportunistic Network Environment):
  • Explanation: ONE is an effective simulator. For opportunistic mobile networks, it is more appropriate.
  • Characteristics:
    • For mobility simulation, it combines with SUMO.
    • This simulator enables routing protocols, data transmission methods, and mobility frameworks.
  • Application Areas:
    • Major use cases encompass simulations of IoT networks in delay-tolerant and mobile networks.
  • Major Resources:
    • ONE Documentation
  1. iFogSim:
  • Explanation: iFogSim simulator supports designing of IoT and edge applications, and it is majorly modeled for fog computing platforms.
  • Characteristics:
    • In fog platforms, it designs network and computing resources.
    • This simulator assists the analysis of network cost, latency, and power usage.
  • Application Areas:
    • Simulation of IoT data analytics plan.
    • Assessing resource management strategies of fog computing.
  • Major Resources:
    • iFogSim Documentation
  1. SimIoT:
  • Explanation: SimIoT is a robust simulation tool that employs CloudSim as the foundation. It mainly concentrates on cloud-related IoT applications.
  • Characteristics:
    • It is efficient in designing resource management strategies of IoT, cloud, and edge.
    • To assist edge computing nodes and IoT devices, this tool includes CloudSim.
  • Application Areas:
    • Assessment of various resource management tactics.
    • Simulating the performance of cloud-related IoT applications.
  • Major Resources:
    • SimIoT Documentation.
  1. Matlab/Simulink:
  • Explanation: MATLAB/Simulink is an extensive platform. Using particular toolboxes, it assists the simulations of WSN and IoT.
  • Characteristics:
    • Model-related design functionalities are offered by Simulink.
    • For the creation of IoT applications and network simulation, it offers pre-developed modules.
  • Application Areas:
    • Sensor data analysis and signal processing are important use cases.
    • Modeling and simulation of IoT protocols in a quick manner.
  • Major Resources:
    • Matlab Documentation
  1. NetSim:
  • Explanation: NetSim is known as an industrial network simulator. IoT applications are the major concentration of this simulator.
  • Characteristics:
    • For the simulation of IoT applications in healthcare and smart cities, it offers an effective visualizer.
    • This simulator enables protocols like Zigbee, 6LoWPAN, and WSNs.
  • Application Areas:
    • Various IoT-based protocols’ performance investigation.
    • Modeling and assessment of IoT network frameworks.
  • Major Resources:
    • NetSim Documentation
  1. GNS3 (Graphical Network Simulator):
  • Explanation: For the simulation of practical network activity, GNS3 is employed along with virtual appliances. It is an open-source network simulator.
  • Characteristics:
    • Through the utilization of virtual appliances like Arduino or Raspberry Pi, IoT devices can be simulated efficiently.
    • It assists various virtual devices such as firewalls, switches, and routers.
  • Application Areas:
    • Employing virtual appliances for the simulation of realistic IoT device activity.
    • For IoT placements, assessment of network topology.
  • Major Resources:
    • GNS3 Documentation.

How can I get the default code of the LoRa ADR algorithm in the LORASIM and LoRaEnergySim simulators?

LoRaEnergySim and LoRaSim are specifically modeled to assist researchers for simulating LoRa-based networks and interpreting their activities. These are referred to as open-source simulators. To analyze the performance of LoRa network as well as Adaptive Data Rate (ADR), they provide an approachable way and generally, they are drafted in Python.

  1. LoRaSim Default Code for LoRa ADR Algorithm

Initial Procedures

git clone https://github.com/lora-net/LoRaSim.git

cd LoRaSim

Interpreting ADR in LoRaSim

  • The characteristic of Adaptive Data Rate (ADR) in LoRaSim is majorly applied for the enhancement of power utilization, time on air, and data rate.
  • Significant code relevant to ADR can be identified in loraDir.py and simulator.py.

Instance of Code Snippets

  • Across the LoRa class (loraDir.py), the functionality of ADR is primarily managed.

Consider the following general instance of ADR logic that is utilized in LoRaSim simulator:

# Excerpt from loraDir.py or simulator.py (LoRaSim)

class LoRa(object):

def __init__(self, …):

# Initialize other parameters

self.data_rate = 5  # Example starting Data Rate

self.tx_power = 14  # Example starting TX Power

self.adr = True  # Enable ADR

def apply_adr(self):

if self.adr:

# Example ADR logic: adjust the data rate and TX power

if some_condition:

self.data_rate = new_data_rate

self.tx_power = new_tx_power

else:

self.data_rate = another_data_rate

self.tx_power = another_tx_power

In the LoRa class itself, this ADR logic can be examined and altered directly.

  1. LoRaEnergySim Default Code for LoRa ADR Algorithm

Another major simulator is LoRaEnergySim that mainly concentrates on the energy utilization in LoRa networks. It encompasses information related to energy usage and generally constructs on the LoRaSim architecture.

Initial Procedures

In the beginning, clone the LoRaEnergySim repository:

git clone https://github.com/RenyuanLiu/LoRaEnergySim.git

cd LoRaEnergySim

Interpreting ADR in LoRaEnergySim

  • In files such as LoRaSim_Energy.py, you can detect ADR. The functionality of ADR in LoRaEnergySim is always the same as LoRaSim’s ADR functionality.

Instance of Code Snippets

  • Place the LoRa class in LoRaSim_Energy.py. Within this, identify the ADR logic.

class LoRa(object):

def __init__(self, …):

# Initialize parameters for LoRaEnergySim

self.data_rate = 5  # Example starting Data Rate

self.tx_power = 14  # Example starting TX Power

self.adr = True  # Enable ADR

def apply_adr(self):

if self.adr:

# Example ADR logic for LoRaEnergySim

if some_condition:

self.data_rate = new_data_rate

self.tx_power = new_tx_power

else:

self.data_rate = another_data_rate

self.tx_power = another_tx_power

Implementing and Altering ADR

  • It is important to alter the apply_adr technique or equivalent, if you intend to change the ADR algorithm in both LoRaSim and LoRaEnergySim simulators.
  • To achieve best outcomes, make sure that your simulation metrics perfectly align with your application area.

Supplementary Details

  • It is advisable to utilize an IDE with global search ability or tools such as grep, if you are facing any difficulty in the process of placing the particular files.

grep -rnw ‘.’ -e ‘adr’

Simulation Software Assistance For IOT

Simulation Project Topics For IOT

Discover the most cutting-edge Simulation Project Topics for IOT, where we provide top-notch simulation assistance for scholars at all levels. You can find essential Simulators for IoT-based simulation projects on phdprojects.org. Our team of expert guides has proven to be incredibly valuable for our scholars. By adhering to our professional advice and instructions, there will be no obstacle preventing you from attaining your doctorate. Don’t hesitate to seek expert help in implementing your IoT Simulation Projects, accompanied by a concise explanation. Read some of the ideas that we have shared on IOT and have provided best simulation support.

  1. Secure biometric-based access control scheme for future IoT-enabled cloud-assisted video surveillance system
  2. Terminal independent security token derivation scheme for ultra-dense IoT networks
  3. An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning
  4. Content-centric data and computation offloading in AI-supported fog networks for next generation IoT
  5. Remote Registration and Group Authentication of IoT Devices in 5G Cellular Network
  6. Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas
  7. A trusted IoT data sharing and secure oracle based access for agricultural production risk management
  8. Bidirectional converter based on G2V and V2G operation with time of usage-based tariff analysis and monitoring of charging parameters using IoT
  9. Development of an IoT assessment method: An interdisciplinary framework for energy efficient buildings
  10. Intelligent user-collaborative edge device APC-based MEC 5G IoT for computational offloading and resource allocation
  11. An Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT networks
  12. Smart mattress integrated with pressure sensor and IoT functions for sleep apnea detection
  13. Microservices for Data Analytics in IoT Applications: Current Solutions, Open Challenges, and Future Research Directions
  14. An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform
  15. An integrated energy efficiency evaluation method for forging workshop based on IoT and data-driven
  16. Energy-efficient data collection for UAV-assisted IoT: Joint trajectory and resource optimization
  17. BC driven IoT-based food quality traceability system for dairy product using deep learning model
  18. Extraction of statistical features for type-2 fuzzy NILM with IoT enabled control in a smart home
  19. A comparative study of low and high resolution infrared cameras for IoT smart city applications
  20. MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications