Edge Computing is a type of network in which it mainly focuses on bringing a centralized data center as near as possible to the user or originated place for data processing. If you are interested in learning more on this topic, you can go through this paper.
- Define Edge Computing
The definition of Edge Computing goes like, the applications which are required by the user, will be brought near to user, directly to the user’s device or to the edge network.
- What is Edge Computing?
The architecture of edge computing has distributed IT model, so they it can bring the computing resources like data centers and cloud, closer to the originating point. Minimizing the latency requirements for data processing and to reduce the network cost is being the main motive for edge computing. Edge can be used in multiple ways such as multiplexers, routers, routing switches, ISP and “Integrated access devices” (IAD). One important thing which should be noticed about this edge network is that, this network should be located near to the user device.
- Where Edge Computing is used?
In this section we are going to discuss about the uses of Edge Computing. The different uses of Edge Computing include Agriculture, Farming, Clear Energy, Autonomous vehicles and Health care, Security, Public Transit System, Sustainable Technology and Traffic Management.
- Why Edge Computing is proposed? Previous Technology Issues
Moving on to the next section, here we are going to discuss about the challenges faced by this Edge Computing technology and the reason for which it was proposed. This technique came into existence for processing data close to user, to increase the speed and volume of processing, in order to produce a better real time result.
The issues faced by this technology previously are: Cost, Latency, Governance, Real-time data and Security.
- Algorithms / Protocols
After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for Edge Computing to overcome the previous issues faced by it are: “k-means clustering Algorithm”, Sequential particle swarm optimization, “Density-Based Spatial Clustering of Applications with Noise” (DBSCAN) and “Best-First-Search-based Spanning Tree” (BFST).
- Comparative study / Analysis
Here in this section we are going to compare different algorithms related to this study in order to find the best one. They are: “AK-means algorithm”, “Scalable two-layer routing architecture”, “Delay-bounded routing” (DBR), “Distance-based Back-Pressure Routing” (DBPR), “Modified bandwidth constrained minimum delay path” (M-BMDP) routing algorithm, “Delay-aware routing”, “Two stable routing algorithms” and “Maximum-Flow-Minimum-Cost (MCMF) routing algorithm consisting of two Linear Programs (LPs)”.
- Simulation results / Parameters
The approaches which were proposed to overcome the issues faced by Edge Computing in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Sum utility (bps), Packet error rate (%), Bit error rate and Energy efficiency (bit/J).
- Dataset LINKS / Important URL
Here are some of the links provided for you below to gain more knowledge about Edge Computing which can be useful for you:
- https://link.springer.com/article/10.1007/s11036-019-01421-5
- https://www.sciencedirect.com/science/article/pii/S0140366422000585
- Edge Computing Applications
In this next section we are going to discuss about the applications of Edge Computing technology. This technology has been employed in many fields, from which some of them are listed here: Autonomous Vehicles, Disaster Response, Internet of Things (IoT), Remote Sensing and Telemedicine.
- Topology
Here you are going to learn about the different choices of topologies which can be used in Edge Computing method. They are: Data Routing, Storage of Distributed Data, Nodes in Edge Computing, Edge-to-Cloud Communication, Load Balancer, Gateway Stations of LEO Satellite, LEO Satellite Constellation, Controller for Resource Management and User Devices.
- Environment
The environment in which the operation of Edge Computing is functioning includes Energy Efficiency, Edge Computing Infrastructure, Analytics, Monitoring, Regulatory Compliance, Flexibility, Scalability and Security Measures.
- Simulation Tools
Here we provide some simulation software for Edge Computing system, which is established with the usage of Python, version 3.11.4, to increase its performance.
- Results
When you complete reading this research paper on Edge Computing, you will now have a clear idea on this technology, main goal of it and some other useful information’s like the topology, algorithm and also about the drawbacks of it.
Edge Computing Research Ideas
- Design of Long-Term Evolution Based Mobile Edge Computing Systems to Improve 5G Systems
- on the Placement of Edge Servers in Mobile Edge Computing
- A Distributed Deep Learning Approach with Mobile Edge Computing for Next Generation IoT Networks Security
- MEC Bench: A Framework for Benchmarking Multi-Access Edge Computing Platforms
- Service Management and Energy Scheduling Toward Low-Carbon Edge Computing
- Workload Orchestration in Multi-Access Edge Computing Using Belief Rule-Based Approach
- A Complete virtual Edge Computing Extrapolation Architectural style, Uses, and Implications
- Computation Offloading and Resource Allocation in IoT-Based Mobile Edge Computing Systems
- ATOM: A Decentralized Task Offloading Framework for Mobile Edge Computing through Blockchain and Smart Contracts
- Performance Analysis of IoT Mobile Edge Computing Networks Using a DF/AF UAV-Enabled Relay with Downlink NOMA
- Offloading Techniques in Mobile Edge Computing (MEC) for Future Wireless Networks
- Optimal Resource Allocation for 6G UAV-enabled Mobile Edge Computing with Mission-Critical Applications
- Combined computation interference and offloading control for mobile edge computing in wireless cellular networks
- CoopEdge+: Enabling Decentralized, Secure and Cooperative Multi-Access Edge Computing Based on Blockchain
- Research on Intelligent Mobile Edge Computing and Task Unloading Method of UAV
- Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mm Wave Mobile Edge Computing
- Enabling Balanced Data DE duplication in Mobile Edge Computing
- Edge Computing: Architecture, Implications and Future with Latest Trends
- Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing
- A Survey of Faults and Fault-Injection Techniques in Edge Computing Systems
- DQN for Smart Transportation Supporting V2V Mobile Edge Computing
- Proportional Fairness in Wireless Powered Mobile Edge Computing Networks
- Realizing the Power of Edge Intelligence: Addressing the Challenges in AI and tiny ML Applications for Edge Computing
- Computer Data Security and Encryption Algorithm Based on Mobile Edge Computing
- Offloading Strategy for UAV-Assisted Mobile Edge Computing with Computation Rate Maximization
- Privacy-Preserving Task Offloading in Vehicular Edge Computing
- Overview of Task Offloading of Wireless Sensor Network in Edge Computing Environment
- Behavior Tree-based Workflow Modeling and Scheduling for Server less Edge Computing
- EdgeSL: Edge-Computing Architecture on Smart Lighting Control with Distilled KNN for Optimum Processing Time
- OL-MEDC: An Online Approach for Cost-Effective Data Caching in Mobile Edge Computing Systems
- Deep Reinforcement Learning for Task Offloading in a Multi-Access Edge Computing Environment
- Efficient Computation Offloading for Mobile Edge Computing by Using Success-History based Adaptive DE
- Performance Analysis of Task Offloading in Mobile Edge Computing using Multi-Objective Optimization Algorithm
- A Survey on Mobile Edge Computing for Deep Learning
- Task Scheduling for Mobile Edge Computing Leveraging Deep Reinforcement Learning
- Intrusion Detection in IoT leveraged by Multi-Access Edge Computing using Machine Learning
- Edge Computing Tasks Orchestration: An Energy-Aware Approach
- AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges
- Research on Collaborative Computational Offload Strategy Based on Improved Ant Colony Algorithm in Edge Computing
- A Prediction Based Resource Reservation Algorithm for Service Handover in Edge Computing
- User-Oriented Edge Node Grouping in Mobile Edge Computing
- Mobile Edge Computing Tasking Offloading Strategy in Cell-Free Massive MIMO with Graph Neural Network
- Task Offloading in Multi-Hop Relay-Aided Multi-Access Edge Computing
- Server Capacity Planning Based Task Computation Offloading in Vehicular Edge Computing Networks
- Adaptive and Priority-Based Resource Allocation for Efficient Resources Utilization in Mobile-Edge Computing
- An Automatic Malaria Disease Diagnosis Framework Integrating Blockchain-Enabled Cloud–Edge Computing and Deep Learning
- FPGA-Accelerated HEVC Encoder for Energy-Efficient Multi-Access Edge Computing
- Computation Resource Offloading in Mobile Edge Computing: A Deep Reinforcement Approach
- Radio and Computing Resource Allocation in Co-Located Edge Computing: A Generalized Nash Equilibrium Model
- Research on Fine-Grained Task Offloading in Mobile Edge Computing
