iFogSim Simulator

iFogSim Simulator

For the purpose of designing and simulating fog computing platforms, iFogSim is widely employed and is considered as a prominent toolkit. This toolkit was constructed in order to help experts and researchers in assessing different factors of fog computing frameworks like task planning, resource management, energy absorption, and performance analysis:

The following are few major characteristics and elements of iFogSim:

  1. Fog Computing Model: Encompassing fog devices such as gateways, routers, smart sensors, fog nodes, cloud data centers and IoT devices, iFogSim offers an extensive system for fog computing platforms.
  2. Task Offloading: Generally, the toolkit assists various task offloading policies, and according to aspects such as latency, resource accessibility, and energy absorption, it permits the users to simulate in what way tasks are disseminated among fog nodes and cloud data centers.
  3. Resource Management: Users are facilitated by means of iFogSim in order to design and examine resource management strategies within the fog platforms, like allotment of computational sources, storage sources, and interaction bandwidth.
  4. Simulation Environment: Typically, a simulation platform is offered by this toolkit where the user has the ability to specify settings of fog computing such as the description of fog devices, network topologies, workload features, and performance parameters.
  5. Energy Consumption Modeling: By examining aspects such as CPU utility, network interaction, and device inactive states, researchers can assess the energy absorption of fog computing frameworks by employing iFogSim.
  6. Task Scheduling Algorithms: For enhancing resource utility and decreasing delay in fog platforms, the toolkit encompasses different task scheduling methods. By simulation experimentations, users can contrast the effectiveness of various scheduling methods.
  7. Integration with CloudSim: Normally, iFogSim is constructed at the top of CloudSim that is considered as a prominent cloud computing based simulation toolkit. Usually, the users are permitted to simulate hybrid fog cloud platforms and investigate communications among fog nodes and cloud data centers by the means of this incorporation.

How do I create a topology in iFogSim?

The process of creating a topology in iFogSim is examined as challenging as well as fascinating. Below is a stepwise instruction that assist you to develop a simple and efficient topology in iFogSim:

Step 1: Setting Up Your Environment

  • On your computer, make sure that you have Java and any essential IDE such as IntelliJ IDEA or Eclipse installed.
  • It is advisable to download the iFogSim library from its authorized GitHub directories or the project blog.
  • The iFogSim project has to be imported into your IDE.

Step 2: Define Your Fog Devices

By employing the FogDevice class, fog devices in iFogSim are developed. Normally, features like processing power, storage, memory, and network bandwidth must be mentioned in an explicit manner. An instance for developing a fog device is given below:

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FogDevice device = createFogDevice(“deviceName”, mips, ram, upBw, downBw, level, ratePerMips, busyPower, idlePower);

A method such as createFogDevice can be developed, where you represent FogDevice objects with determined configurations. The power usage is generally assessed by this method and it also establishes other device-related parameters.

Step 3: Define Sensors and Actuators

Utilizing the Sensor and Actuator classes, sensors and actuators are specified. Typically, they are connected to fog devices and applications. For instance:

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Sensor tempSensor = new Sensor(“sensorName”, “SENSOR_TYPE”, gwDeviceId, userId, new DeterministicDistribution(5));

// Creates a sensor with a deterministic distribution of sensor data generation

 interval. Actuator display = new Actuator(“actuatorName”, userId, appId, “ACTUATOR_TYPE”);

Step 4: Define Applications

The iFogSim based applications are designed as Directed Acyclic Graph (DAG) where edges denote tuples to be exchanged among modules and each node denotes a module/task. For this goal, utilize AppModule and AppEdge classes. For example:

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Application application = Application.createApplication(“appId”, userId);

// Create application

application.addAppModule(“module1”, 10);

// Add a module with a name and its resource demands

// Define communication from sensor to module

application.addAppEdge(“SENSOR_TYPE”, “module1”, 1000, 2000, “SENSOR_TYPE”, Tuple.UP, AppEdge.SENSOR);

 // Define communication from module to actuator

 application.addAppEdge(“module1”, “ACTUATOR_TYPE”, 1000, 200, “ACTUATOR_TYPE”, Tuple.DOWN, AppEdge.ACTUATOR);

Step 5: Create a Physical Topology

Once, sensors, actuators, and applications are specified, you can construct a topology that specifies in what way these elements are linked. To depict the network topology, this stage encompasses establishing parent-child relationships among fog devices.

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device.setParentId(parentId); // Set the parent of the device

device.setChildrenIds(childrenIds); // Optionally, set children of the device

Step 6: Initialize and Start Simulation

You can initialize and begin the simulation after your topology is specified.

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Controller controller = new Controller(“controllerName”, fogDevices, sensors, actuators);

// Start the simulation


Further Steps

  • To assess your arrangement, aim to research various configurations, application systems, and topologies.
  • In order to comprehend the effectiveness of your fog computing platforms, it is appreciable to examine the output of the simulation.

For the most advanced information and approaches, refer to the modern documentation of iFogSim. Generally, the certain code and configurations rely upon the necessities of your project and the version of iFogSim that you are employing.

What is a fog computing research paper?

As the result of doing this, it intends to improve protection, decrease delay, handle bandwidth in more effective manner, and enhance data locality for actual-time analytics and decision-making procedures in Internet of Things (IoT) and other applications:

Major Components of a Fog Computing Research Paper

  1. Introduction
  • Overview: It is advisable to offer a common introduction about fog computing, such as its description, main features, and in what way it varies from cultural cloud computing.
  • Motivation: Concentrating on certain usage scenarios such as healthcare, IoT, smart cities, industrial IoT (IIoT), and mobile application, describe the requirement for fog computing.
  • Challenges: This section defines the limitations that fog computing intends to solve like safety, latency, scalability, network bandwidth.
  1. Literature Review
  • Incorporating initial theories, recent mechanisms, and current developments, provide an extensive analysis of previous studies in the domain of fog computing.
  • For offering a setting for the novel exploration, investigate existing research based on challenges, methodologies, and outcomes.
  1. Research Problem and Objectives
  • This section describes an explicit description of the certain issues of the paper that intends to solve within the setting of fog computing.
  • Focus on offering elaborated aims or queries that the research aims to respond to.
  1. Methodology
  • To explore the research issue, the technique employed has to be mentioned. Typically, empirical setups, simulations employing tools such as YAFS, iFogSim, EdgeClouSim, etc, conceptual analysis, or case studies are encompassed.
  • It is approachable to offer explanation of the datasets, tools, and analytical approaches that are utilized in the study.
  1. System Model and Design
  • Provide an elaborated explanation of the suggested structure or framework by considering papers suggesting novel infrastructures, methods, or models.
  • Normally, conceptual foundations, structural figures, methods, and communication flows are involved in this section.
  1. Implementation and Evaluation
  • In what way the suggested framework or system was deployed and examined have to be explained in an explicit manner. Generally, simulation outcomes, empirical setup, performance metrics, and exploration are encompassed.
  • Concentrate on describing the assessment against specified aims, comparison with previous approaches, and discourse of the outcomes.
  1. Discussion
  • It is appreciable to elucidate the immersive exploration of the outcomes, involving their impacts for fog computing domain, possible applications, and challenges of the research.
  • In what way the work that innovates the latest in fog computing has to be described.
  1. Conclusion and Future Work
  • Finally, in the conclusion section, aim to outline the major results, dedications to the fog computing discipline, and the wider influence of research.
  • According to the experimental findings and experienced challenges, offer recommendations for upcoming research possibilities.

Submitting a Research Paper

If you are prepared, submit your research paper to conferences, educational journals, or workshops which concentrate on fog computing, IoT, cloud computing, and other relevant regions. It is significant to choose an appropriate venue for your work, because each venue has its own viewers, submission instructions, and peer-analysis procedure.

iFogSim Simulator Projects

Fog Computing Project Topics

Fog computing aims to enhance the performance of cloud systems, such as edge computing, rather than replacing cloud computing. Some of the latest Fog Computing Project Topics that we accompanied for scholars are listed below.networksimulationtools.com will provide wonderful dissertation ideas for all areas of fog computing. Share with us all your details we will guide to the next level.

  1. Multilevel scheduling mechanism for a stochastic fog computing environment using the HIRO model and RNN
  2. AI-based fog and edge computing: A systematic review, taxonomy and future directions
  3. Fog-DeepStream: A new approach combining LSTM and Concept Drift for data stream analytics on Fog computing
  4. Cache in fog computing design, concepts, contributions, and security issues in machine learning prospective
  5. Optimum scheduling in fog computing using the Divisible Load Theory (DLT) with linear and nonlinear loads
  6. An integrating computing framework based on edge-fog-cloud for internet of healthcare things applications
  7. An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing
  8. Defense scheme against advanced persistent threats in mobile fog computing security
  9. FedDOVe: A Federated Deep Q-learning-based Offloading for Vehicular fog computing
  10. Data reduction in fog computing and internet of things: A systematic literature survey
  11. Locality-aware deployment of application microservices for multi-domain fog computing
  12. Optimal cross-layer resource allocation in fog computing: A market-based framework
  13. The trade-offs between Fog Processing and Communications in latency-sensitive Vehicular Fog Computing
  14. Monitoring fog computing: A review, taxonomy and open challenges
  15. Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm
  16. FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments
  17. Evaluating NiFi and MQTT based serverless data pipelines in fog computing environments
  18. A Real-Time Deep Learning-based Smart Surveillance Using Fog Computing: A Complete Architecture
  19. S-HIDRA: A blockchain and SDN domain-based architecture to orchestrate fog computing environments
  20. Multiple linear regression-based energy-aware resource allocation in the Fog computing environment
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OMNET++ 103 95 87
OPNET 36 64 89
QULANET 30 76 60
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MATLAB 96 185 180
LTESIM 38 32 16
CONTIKI OS 42 36 29
GNS3 35 89 14
NETSIM 35 11 21
EVE-NG 4 8 9
TRANS 9 5 4
PEERSIM 8 8 12
RTOOL 13 15 8
VNX and VNUML 8 7 8
WISTAR 9 9 8
CNET 6 8 4
ESCAPE 8 7 9
VIRL 9 9 8
SWAN 9 19 5
JAVASIM 40 68 69
SSFNET 7 9 8
TOSSIM 5 7 4
PSIM 7 8 6
ONESIM 5 10 5
DIVERT 4 9 8
TINY OS 19 27 17
TRANS 7 8 6
CONSELF 7 19 6
ARENA 5 12 9
VENSIM 8 10 7
NETKIT 6 8 7
GEOIP 9 17 8
REAL 7 5 5
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