Fog computing is said to be the best known for producing data in a decentralized computing system placed between the devices and the cloud. It is a user-friendly model which has a flexible structure. To enhance the performance, it allows the users to fix resources even in logical locations, including the applications and services. The goal of this framework is to reach the needed location from basic analytic services to the end of the network. This kind of goal setup will reduce the gap between the user’s network to enhance performance, overall network efficiency, and to transmit data. In this article, we start with the basic idea framed on fog computing, further comes the detailed and advanced data about fog computing thesis.
Here, we will discuss some of the main features of Fog computing.
- Lowering the costs of operation
- Latency minimization
- Improved security system
- Conservation of network bandwidth
- Fastening up the agility
- Improvisation of reliability
- Privacy with In-depth insights
As we discussed the main features of fog computing. Let’s now go through the layers of fog computing that helps us give a deepened knowledge about the topic.
Layers of Fog Computing Architecture
- Physical and virtualization layer
- Wireless sensor network lookout by things and physical sensors
- Virtual sensor network and virtual sensors
- Preprocessing layer
- Trimming, Reconstruction, Data analysis, and data filtering
- Temporary storage layer
- Storage devices
- Storage space virtualization
- Replication, De-duplication, and data distribution
- Monitoring layer
- Monitoring of service
- Monitoring of response
- Monitoring of power
- Monitoring of resource
- Monitoring of activities
- Security layer
- Privacy and Integrity measures
- Transport layer
- Secured and pre-processed data uploading in the cloud.
Here, we discussed the layers of fog computing architecture in detail. Low latency network links between devices and their analytics endpoints can be created using fog computing. Using this setup, compared to the cloud, the amount of bandwidth needed will be decreased. Next, we see how the fog nodes allocate the resources to the tasks.
How does the fog node allocate resources to tasks?
Fog node allocates its resources to tasks in a phased manner. These phases can be divided into 4 stages. They are as follows,
- Fog usually supports the enormous size of IoT tasks, and it is evaluated.
- The infrastructure of fog can begin from the initial stage or it can be further altered in the existing infrastructure based on the design and dimension.
- In the process of IoT use, the techniques for fog resource provisioning and in granting the fog resource need to be fixed prior.
- Installation of fog framework, if there’s a necessary for allocation, migration of data and provisioning of resource
- To estimate the capacity of resource management and the capability of fog infrastructure, assessment can be used.
We are now able to get the technical understanding by overseeing the phases included in fog computing, practical explanations with a massive amount of resources help us come up with novel ideas for fog computing thesis writing. Our team provides you with such kind of research projects with reliable sources based on the latest trends in that research field. For any further information, you can surely make contact our expert’s team. Now let’s move on to the latest issues in fog computing.
Research Issues in Fog computing
- Management of resources
- Insufficiency of due diligence
- Loss of data
- Service denial
- Privacy and security
- Insufficient data processing
- Service failure and overloading
- Malicious insider
- Breaching of data
- Hijacking of accounts
The latest issues discussed above are framed by our experts that, they are aware of the scope of the project that makes your fog computing thesis more valuable. Further, discuss the real-time applications of fog computing
Real-Time Applications of Fog Computing
- Multimedia – Augmented reality, Streaming of videos, gaming are some of the multimedia processing that can be proposed and delivered by fog. In case of recognition of the face, to diminish the response time of appropriate authorities in an incident, video surveillance applications can be used. Data explosion can happen with surveillance cameras on the incident spot. To have rapid emergency response, a decentralized fog infrastructure can process various data in various fog nodes.
- Health care – In real life, body sensors can be used to diagnose heart disease. To provide medical aid instantaneously to patients in health care centers, fog with sensors and wearable’s are proposed for usage.
- Autonomous vehicle – control signals are given by the fog server to navigate the drone, and it provides a sightline between a drone and the fog server. It enhances route navigation, coupled with smart traffic lights, reduces traffic accidents by serving information from smart vehicles, sharing instantaneous traffic scenes, and creating a wide area. A fog server serves its best.
In the above part, we discussed the applications of fog computing in real-time, particularly in the fields of Multimedia, Autonomous vehicles, and Healthcare. It will give you a clear picture of such aspects. Every project must be tested for certain fog computing applications and it is not application-specific and the proposed methods are useful for various real-time applications.
As such our project work provides you with more updates in technology by our experienced team of world-class certified engineers, additionally making the research more reliable and trusted. For any kind of queries, you can contact our team members at any time. Further, we can move on to,
Communicating Technologies in Fog computing
- IEEE 802.11aq: Frequency Range, Pre-association Discovery.
- IEEE 802.11aj: China Millimeter Wave
- IEEE 802.11ah: Sub-1 GHz license exempt operation (e.g., Smart metering, sensor network)
- IEEE 802.11af: TV Whitespace
- IEEE 802.11ad: Very High Throughput 60 GHz
- IEEE 802.11aa: Robust streaming of Video Audio Transport Streams
- WiFi Technologies
- IEEE 802.11ac: Multi user MIMO; Wider channels (estimate in future time 80 to 160 MHz), Very High Throughput <6 GHz; potential improvements over 802.11n: better modulation scheme (expected ~10% throughput increase)
- IEEE 802.11ae: Management Frames Prioritization
- IEEE 802.11-2016: A new release of the standard that includes amendments aa, ac, af, ae, and ad.
- IEEE 802.11ai: Fast Initial Link Setup
- IEEE 802.11ak: Bridged Networks in Transit Links
Innovative Fog Computing Thesis Ideas
- Allocation of resources
- Provisioning of resources
- Balancing the load
- Offloading the task
- Placement of application
- Scheduling the resource
Therefore, fog computing is emerging as one of the important and growing fields of research. By providing reliable research data from trustworthy sources and benchmark references we help our customers in presenting the best papers.
Current Trends in Fog Computing
- Optimization of offloading process
- Deployment Mobile Crowdsensing application into a sensing Cloud
- Reducing the consumption of energy
- Billing cost in visual/ audio recognition
- Retrieval environment
- Modeling a typical health care monitoring system.
- Fog computing resources are being allocated under QoS and SLA constraints
- Management of data in mobile crowdsensing environment
- Distribution of processing load at optimal load between the cloud and the fog
- Migration of services from the edge to the cloud
- Fog robotics and control management
The above points are the current trends in fog computing thesis. We are best at intriguing into every aspect of the research project and that helped us serve you with your needs. Our technical team has worked and provided the discussion for ‘Fog Robotics’. So here we can move on to Fog computing in the Robotics field.
Overview of Fog Robotics
Fog robot server and the cloud are the two elements in Fog robotics. With the help of a local server, Fog robotics with cloud shows the nearby data to the user. These servers are adaptable in nature that include the capability of the network processing power of computation, and secured by sharing the outcomes to other robots for performance with high rates along with the lowest possible latency.
Research Ideas in Cloud Robotics
- Latency Of Issues
- Service Quality
- Bandwidth Limitations
- Security and Privacy
Fog robotics emerged as an advanced option for future robotic systems. Since robots need greater brainpower for processing billions of computations while doing their work, Fog robotics is considered as a distributed robot system of the next generation. For example in the case that to instruct the robot to take a spray bottle, there comes the role of fog robotics.
Research Ideas in Fog Robotics
Fog robotics in a structural way works closer with robots that include of roles of functioning of networks, storage, and control over fog computing. This encourages the robots to make interact with human life. Fog robotics utilizes the cloud, fog robot servers, and evaluating robots for fog robotics architecture.
- Fog computing for Industrial automation and robotics – This project highlights the models of novel designing programming for both operating system and hardware mechanisms including the fog node’s protocols of communication.
- Adding security to fog robotics using the global data plane – This work enquires about the usage of data capsules. It is a process to enhance the performance of the machine learning applications and the security system that was operating in edge computing environments. Additionally, it protects the security and privacy of the data by examining the fog robot system.
- 5G coral: A 5G Convergent Virtualized Radio Access Network Living at the Edge – In this project, fog-assisted robotics is examined with real-time applications. The area of radio access network at the edge is been targeted in this project and investigated the automation of robots and formation of the fleet for coordination movement.
In the following, we can see stimulator, implementation technologies, metrics, and its objectives of fog computing thesis. Tools are different based on the tasks running in the cloud or fog. Based on that, tools are selected with their specifications.
Simulation Tools for Fog Computing
- Simulator: MobFogSim
Technology implementation: Extends iFogSim
Metrics: Same as iFogSim
Objectives: Evaluate application behavior and performance
- Simulator: Fogbus
Metrics: Energy, network, Latency, and CPU usage
- Simulator: FogDirMime
Technology implementation: Python
Objectives: Support FogDirector
- Simulator: YAFS
Technology implementation: Python, JSON
Metrics: Response time, network utilization, network delay, Energy models
Objectives: Analyze the design and deployment of applications
- Simulator: OPNET
Technology implementation: based on OPNET, Visual studio
Metrics: Processing time
Objectives: Cope with the massive amount of confidential and security-sensitive data
- Simulator: FogWorkFlowSim
Technology implementation: Java
Metrics: Performance (time, energy, and processing cost)
Objectives: Evaluate resource and task management strategies
- Simulator: FogNetSim++
Technology implementation: based on OMNeT++
Metrics: Pricing model, Energy module, and scheduling algorithms
Objectives: General simulation of fog environments
- Simulator: FogDirSim
Technology implementation: RESTful API, Python
Metrics: Energy consumption, Performance in terms of uptime, and resource usage
Objectives: Compare application management and infrastructure management policies
- Simulator: MyiFogSim
Technology implementation: Extension of iFogSim
Objectives: Resource allocation
- Simulator: iFogSim
Technology implementation: Extension of CloudSim, Java, JSON
Metrics: Energy consumption, network congestion, and operational costs
Objectives: Performance of resource management policies
- Simulator: FogTorchΠ
Technology implementation: Extension of FogTorch
Metrics: Resource utilization and QoS accuracy
Objectives: Same as FogTorch and many QoS profiles according to a probability distribution
- Simulator: FogTorch
Technology implementation: Java
Metrics: Reliability of links and nodes, QoS, power consumption, monetary costs, and security.
Objectives: Find eligible deployments of an application over a fog infrastructure
- Simulator: Edge-Fog
Technology implementation: Python
Objectives: Distribute task processing on the participating cloud resources
The performance evaluation of fog computing is tested and explained because it analysis the real-time working efficiency of the proposed methods and also it helps to develop further by comparing with the existing methods and such metrics are discussed in the following.
Performance Evaluation Metrics in Fog computing
- Response time, Transmission costs, processing cost per unit time
- Number of messages, System loss rate, System throughput, CPI utilization, System response time
- Reconfiguration costs, transmission costs, operational costs, routing costs
- Storage and processing costs
- Energy consumption, Latency
- Job losses, Monetary costs
- Costs for maintenance
- Processing delay, Propagation and transmission delay, Service delay
- Network latency, Computation latency, and communication latency
Experiences, massive resources, internal review, work based on technology updates are some of our specialized functions. This kind of support helps the customers to trust us and to serve them better. And we are now at the final part of our work about how we are best at fog computing thesis writing.
How we are best in thesis writing?
- Objectives – In our process of thesis writing, we usually come up with several objectives by specifically narrowing them down. Otherwise, the thesis may unable to find the focus on points.
- Literature – In this section, we basically include the study area, and other various sources of information that we’re going to use throughout our study, particularly in every stage of research.
- Research – It is considered as the crucial section where we’ll frame the research area and further detail the ideology used in the research question.
- Methodology – In this methodology section, we elaborate on the methods used in the process of data collection for the thesis, and the project work can be done using both Empirical and Non-Empirical data.
- Potential outcomes – In this section of potential outcomes, we used to mention the expected results out of the project. And it discusses the results of analysis and research.
- Reference list – A reference list can be added based on the preferences instructed by the mentor. And we’ll help you do that.
Our service to you is a unique one with in-depth research proposal, delivered on time at a low cost. Fog computing thesis was explained in a detailed manner. Further, we give you the best research papers that are free from scams and Plagiarism. We provide you with excellent team support and work to fulfill your needs. For any further information, contact our expert team providing 24/7 customer support.