The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes...
详细信息
The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edgecomputing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of ne
The healthcare model is considered an imperative part of remote sensing of health. Finding the disease requires constant monitoring of patients' health and the detection of diseases. In order to diagnose the disea...
详细信息
The healthcare model is considered an imperative part of remote sensing of health. Finding the disease requires constant monitoring of patients' health and the detection of diseases. In order to diagnose the disease utilizing an edgecomputing platform, this study develops a method called grey wolf invasive weed optimization-deep maxout network (GWIWO-DMN). The proposed GWIWO, which is developed by integrating invasive weed optimization (IWO) and grey wolf optimization (GWO), is used here to train the DMN. The distributed edge computing platform consists of four units, namely monitoring devices, first layer edge server, second layer edge server, and cloud server. The monitoring devices are used for accumulating patient information. The preprocessing and feature selection are performed in the first layer edge server. Here, the preprocessing is carried out using the exponential kernel function. The selection of features is done using Jaro-Winkler distance in the first layer edge server. Then, at the second layer edge server, clustering and classification are carried out using deep fuzzy clustering and DMN, respectively. The proposed GWIWO algorithm is used to do the DMN training. Finally, the cloud server processes the decision fusion. The proposed GWIWO-DMN outperformed with the highest true positive rate (TPR) of 89.2%, highest true negative rate (TNR) of 93.7%, and highest accuracy of 90.9%.
As electric vehicles (EVs) join the 6G Internet of Vehicles (6G-IoV), they must charge while moving and process data in real time. Static plug-in charging and on-board CPUs cannot meet these dual demands. We therefore...
详细信息
This paper proposes a novel edge-computing based structure to support learning-based decision-making in industry manufacturing field. This structure consists of four functional layers, respectively realizing model est...
详细信息
ISBN:
(纸本)9781728143958
This paper proposes a novel edge-computing based structure to support learning-based decision-making in industry manufacturing field. This structure consists of four functional layers, respectively realizing model establishment, task allocation and task processing work. In order to take full advantage of the distributedcomputing resources at the edge, the manufacturing computing task can be further decomposed into several sub-tasks, separating the complex computing problem with large problem size into regional scheduling ones with much smaller problem size. All the sub-tasks are allocated to the edges, accomplished by the algorithm deployed on computing devices of region-related edge node, which contributes to faster data-processing and problem-solving speed. A simulation test has been performed in which a multi-AGV scheduling problem was solved according to a distributed reinforcement learning method configured in such edgecomputing architecture. The objective of each edge node is to acquire AGV schedule of related region that minimizes the makespan. Simulation results demonstrate that this distributed edge computing system can be enabled to learn satisfying solution and converge much faster when it is compared with conventional method applied in centralized architecture.
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile edgecomputing (MEC) servers or cloud regulated by a third...
详细信息
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile edgecomputing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.
We consider a distributed edge computing scenario consisting of several wireless nodes that are located over an area of interest. Specifically, some of the "master" nodes are tasked to sense the environment ...
详细信息
ISBN:
(纸本)9781728150895
We consider a distributed edge computing scenario consisting of several wireless nodes that are located over an area of interest. Specifically, some of the "master" nodes are tasked to sense the environment (e.g., by acquiring images or videos via cameras) and process the corresponding sensory data, while the other nodes are assigned as "workers" to help the computationally-intensive processing tasks of the masters. A new tradeoff that has not been previously explored in the existing literature arises in such a formulation: On one hand, one wishes to allocate as many master nodes as possible to cover a large area for accurate monitoring. On the other hand, one also wishes to allocate as many worker nodes as possible to maximize the computation rate of the sensed data. It is in the context of this tradeoff that this work is presented. By utilizing the basic physical layer principles of wireless communication systems, we formulate and analyze the tradeoff between the coverage and computation performance of spatial networks. We also present an algorithm to find the optimal tradeoff and demonstrate its performance through numerical simulations.
暂无评论