Mobile Edge Computing Research Topics

Mobile Edge Computing (MEC) Research Topics is one of the network structures. It is now widely being used in many applications or fields of domain. The proposed MEC technique will enhance their accuracy of the proposed technique by utilizing the existing technology. Then we offer some of the details that are relevant to this proposed MEC technique.

  1. Define Mobile Edge Computing

At the starting stage of the research we first see the explanation for MEC. It is also known as Multi-access Edge Computing, it is a network architecture concept which permits application execution, computation and data storage at the edge of the cellular network, closer to mobile users. In MEC, computing resources like storage, networking components and servers are arranged in the edge of the network infrastructure, generally over or near access points or base stations. Some of the components of MEC are Security and authentication mechanism, Access network, Edge servers, Cloud server, Orchestration managed layer, Edge gateway, MEC platform APIs and Virtualization platform.

  1. What is Mobile Edge Computing?

After the explanation we look for the brief definition for our proposed technology MEC. It lengthens the capacities of cloud computing by carrying it to the edge of the network. It is a network architecture which offers IT service surroundings and cloud computing at the edge of the network. MEC is utilized to precede the big volume of data that is created by edge applications and devices near to where it is taken. Types of MEC are Hybrid MEC, Network-integrated MEC, Private MEC, Centralized MEC, Public MEC and Distributed MEC.

  1. Where Mobile Edge Computing used?

Thereafter the brief definition we discuss where to employ these proposed MEC techniques. It alters the mobile networks by utilizing the computing resources at the edge of the network, closer to devices and users. This technique enhances the performance and decreases the latency for latency-sensitive applications like Real-time gaming, Virtual Reality (VR) and Augmented Reality (AR). Utilizing technologies like Software Defined Networking (SDN), Network Function Virtualization (NFV). MEC allows dynamic allocation of resources and effective delivery of services and content. With the capacity for mobile application offloading, content caching and IoT support, MEC optimizes bandwidth usage, lengthens battery life for devices and improves user experience along a extensive range of mobile services and applications.

  1. Why Mobile Edge Computing technology proposed? , previous technology issues

In this research we proposed the MEC technology and it is proposed to overcome the issues in the existing technologies. The primary issues of the previous methods in energy effective allocation in MEC surroundings were decreased system performance, impact of slow and uncertainty convergence speed, computational complexity, conflicting objectives and imbalance metrics.

  1. Algorithms / protocols

Our proposed MEC technique uses the subsequent methods to overcome the limitations in the previous technologies and to face the challenges to propose this novel new technique. The methods or techniques that we compared are Low-Complexity WOA Resource Allocation (Low-complexity with Whale optimization with channel resource allocation), PCASGD (Principal Component Analysis with Stochastic Gradient Descent), Bandit Learning method, Multi objective evolutionary algorithm (MOE) and Hybrid Deep MCF (Hybrid heuristic and deep learning with Most Capacity First).

  1. Comparative study / Analysis

For the comparative analysis section we have to compare various methods or techniques to address the issues in the previous technologies. The methods that we compared to overcome are as follows:

  • PCASGD provides a creative handle in the domain of MEC, where an effective resource allocation is important.
  • To create an effective decision by utilizing the Hybrid Heuristic Deep MCF decision-making technique to create an effective decision. On the basis of this technique the decision-making process enhances the performance of the system that improves the resources that are employed and also decreases congestion.
  • The MOE is incorporated to maintain the goals to find the relationship among the two metrics and identify the limitations of trade-off.
  • In MEC surroundings, for energy-effective allocation, we employ LC-WOA with Bin packing technique to define the energy-effective allocation methods and to optimize the system energy use under various situations.
  • By employing the bandit learning technique to decrease the effect of demand pattern unpredictability on energy-effective allocation.
  1. Simulation results / Parameters

Here we compare various parameters or metrics to obtain the best performance for this research with increased accuracy. The metrics that we compared are CPU usage (%) with Time (s), throughput, Energy efficiency, Scalability and Cost efficiency.

  1. Dataset LINKS / Important URL

We propose a MEC technique that is widely used in many of the fields and it will tackle some previous issues. In this we offer some important links to be preferred for the understanding of DBT:

  1. Mobile Edge Computing Applications

Numerous applications use this MEC technique. The applications are Autonomous vehicles, Augmented Reality (AR) and Virtual Reality (VR), video streaming, Healthcare, Gaming, Smart cities and Industrial Internet of Things (IoT).

  1. Topology for Mobile Edge Computing

Now we see the topology for this proposed technique. The topology that contains multiple edge servers advantageously allocated throughout the network to supply to the mobile users along different geographical positions. These servers are valuably located at key positions over the network structure, consisting of aggregation points or data centers, intended to decrease latency, improve service quality and base stations. The mobile devices generate interactions with edge servers over wireless access points or base stations, forming a hierarchical network topology.

  1. Environment for Mobile Edge Computing

Let’s look at the environment for this situation is a MEC framework that is arranged over a cellular network, presenting a heterogeneous network infrastructure inclusively for edge servers, mobile devices and base stations. It works in an environment marked by different user demands, heterogeneous resource constraints and stable and unstable workloads.

  1. Simulation tools

The software requirements that are required for this research are as follows. The developmental tool that is required for this research is IFOGSIM, JDK 1.8 and Netbeans-12.3. Then the programming language that is employed to execute the research is JAVA. We have to operate the research by using the operating system Windows 10-[64-bits].

  1. Results

The Mobile edge computing technique is proposed in this research. It overcomes several limitations in the existing research and it will evaluate the performance of this research by comparing the parameters or metrics with the existing research to improve the accuracy of this research. This research is operated by using the operating system Windows 10- [64-bits].

Mobile Edge Computing Research Ideas:

The succeeding are the research topics that are based on this proposed research MEC. These topics will offer the details that are relevant to this proposed strategy; we utilize to clarify the doubts on this research.

  1. Online Learning Aided Decentralized Multi-User Task Offloading for Mobile Edge Computing
  2. Attention Mechanism-Empowered MADDPG for Distributed Resource Allocation in Cell-Free Mobile Edge Computing
  3. Hastening Stream Offloading of Inference via Multi-Exit DNNs in Mobile Edge Computing
  4. Method of Minimizing Energy Consumption for RIS Assisted UAV Mobile Edge Computing System
  5. Distributed Convex Relaxation for Heterogeneous Task Replication in Mobile Edge Computing
  6. OR-EDI: A Per-Edge One-Round Data Integrity Verification Scheme for Mobile Edge Computing
  7. Cross-FCL: Toward a Cross-Edge Federated Continual Learning Framework in Mobile Edge Computing Systems
  8. Joint Computing, Pushing, and Caching Optimization for Mobile-Edge Computing Networks via Soft Actor–Critic Learning
  9. Joint Optimization of Transmission and Computing Resource in Intelligent Reflecting Surface-Assisted Mobile-Edge Computing System
  10. Enhancing Computation Offloading In Wireless-Powered Mobile-Edge Computing Networks With Deep Reinforcement Learning For Online Optimization
  11. A Task Offloading Method Based on User Satisfaction in C-RAN With Mobile Edge Computing
  12. Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning
  13. Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization
  14. Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing
  15. Data Integrity Verification in Mobile Edge Computing With Multi-Vendor and Multi-Server
  16. Deep Reinforcement Learning-Based Task Assignment for Cooperative Mobile Edge Computing
  17. Data Integrity Verification in Mobile Edge Computing With Multi-Vendor and Multi-Server
  18. Mean Field Graph Based D2D Collaboration and Offloading Pricing in Mobile Edge Computing
  19. Com-DDPG: Task Offloading Based on Multiagent Reinforcement Learning for Information-Communication-Enhanced Mobile Edge Computing in the Internet of Vehicles
  20. Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing
  21. Joint Task Offloading Scheduling and Resource Allocation in Air–Ground Cooperation UAV-Enabled Mobile Edge Computing
  22. QoS-Aware Online Service Provisioning and Updating in Cost-Efficient Multi-Tenant Mobile Edge Computing
  23. Federated Learning With Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing
  24. Intelligent Content Caching and User Association in Mobile Edge Computing Networks for Smart Cities
  25. Performance Analysis and Power Allocation for Covert Mobile Edge Computing With RIS-Aided NOMA
  26. Offline Reinforcement Learning for Asynchronous Task Offloading in Mobile Edge Computing
  27. Transparent Third-Party Authentication With Application Mobility for 5G Mobile-Edge Computing
  28. Secure Computation Offloading and Service Caching in Mobile Edge Computing Networks
  29. Achieving Fast Environment Adaptation of DRL-Based Computation Offloading in Mobile Edge Computing
  30. A Novel Quantum Hash-Based Attribute-Based Encryption Approach for Secure Data Integrity and Access Control in Mobile Edge Computing-Enabled Customer Behavior Analysis
  31. Fed-PEMC: A Privacy-Enhanced Federated Deep Learning Algorithm for Consumer Electronics in Mobile Edge Computing
  32. Energy-Latency Aware Intelligent Reflecting Surface Aided Multi-Cell Mobile Edge Computing
  33. Energy Efficient Transmission Strategy for Mobile Edge Computing Network in UAV-Based Patrol Inspection System
  34. MicrosMobiNet: A Deep Lightweight Network With Hierarchical Feature Fusion Scheme for Microscopy Image Analysis in Mobile-Edge Computing
  35. Parallel Offloading and Resource Optimization for Multi-Hop Ad Hoc Network-Enabled CBTC With Mobile Edge Computing
  36. Machine Learning Empowered Green Task Offloading for Mobile Edge Computing in 5G Networks
  37. Trajectory Planning, Phase Shift Design, and IoT Devices Association in Flying-RIS-Assisted Mobile Edge Computing
  38. Joint Channel Estimation and Reinforcement-Learning-Based Resource Allocation of Intelligent-Reflecting-Surface-Aided Multicell Mobile Edge Computing
  39. Reliability-Aware Proactive Offloading in Mobile Edge Computing Using Stackelberg Game Approach
  40. A Cooperative Computation Offloading Strategy With On-Demand Deployment of Multi-UAVs in UAV-Aided Mobile Edge Computing
  41. Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO
  42. Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things
  43. A Hybrid Secure Resource Allocation and Trajectory Optimization Approach for Mobile Edge Computing Using Federated Learning Based on WEB 3.0
  44. Blockchain-Integrated UAV-Assisted Mobile Edge Computing: Trajectory Planning and Resource Allocation
  45. A Mobile Edge Computing Framework for Traffic Optimization at Urban Intersections Through Cyber-Physical Integration
  46. Latency Estimation and Computational Task Offloading in Vehicular Mobile Edge Computing Applications
  47. Modeling on Resource Allocation for Age-Sensitive Mobile-Edge Computing Using Federated Multiagent Reinforcement Learning
  48. Adaptive Resource Allocation for Mobile Edge Computing in Internet of Vehicles: A Deep Reinforcement Learning Approach
  49. Personalized and Differential Privacy-Aware Video Stream Offloading in Mobile Edge Computing
  50. Blockchain-Based Portable Authenticated Data Transmission for Mobile Edge Computing: A Universally Composable Secure Solution