Crowd V-IoE: Visual Internet of Everything Architecture in AI-Driven Fog Computing

Rahim

IEEE Trans. Intell. Transport. Syst.

et al. 2022

The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, effcient, fexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective ownerdriver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition's outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

Section: Infrastructure Concept For Vhmsmentioning

confidence: 99%

A Secure and Intelligent Framework for Vehicle Health Monitoring Exploiting Big-Data Analytics

Rahim

IEEE Trans. Intell. Transport. Syst.

et al. 2022

“…The intelligent devices in IoT need the ability of communication, computing and caching to complete information collection, data storage and interaction between intelligent devices. Ji et al (2020) used the integration of communication-computing-caching to calculate the intelligence level, which is suitable for IoT intelligence calculating. So the communication-computing-caching integration is applied in the algorithm of this paper to help represent the intelligence level of IoT.…”

Section: The Related Workmentioning

confidence: 99%

An intelligence optimization method based on crowd intelligence for IoT devices

Yang

Liang

et al. 2021

IJCS

Self Cite

Purpose The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently. Design/methodology/approach In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices. Findings Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level. Originality/value This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

Artificial Intelligence

“…Traditional supervised learning deals with the analysis of single-label data, which means that samples are associated with a single label. However, in many realworld data mining applications, such as text classification [1,2], scene classification [3,4], crowd sensing/mining [5][6][7][8][9][10][11], and gene functional classification [12,13], the samples are associated with more than one label. From this description, we understand that the challenge of the multilabel classification task is its potential output.…”

Section: Introductionmentioning

confidence: 99%

Multilabel Classification Based on Graph Neural Networks

Ye¹,

Wang²

2022

Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise.