Edge Computing Approach Research Articles

An Automated Edge Computing Approach for IoT Device Registration and Application Deployment

An Automated Edge Computing Approach for IoT Device Registration and Application Deployment

An Ensemble Edge Computing Approach for SD-IoT security Using Ensemble of Feature Selection Methods and Classification

An Ensemble Edge Computing Approach for SD-IoT security Using Ensemble of Feature Selection Methods and Classification

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Online Education Oriented Design and Estimation of Ideological and Political: An Edge Computing Approach

Online Education Oriented Design and Estimation of Ideological and Political: An Edge Computing Approach

Sustainable Soundscape Monitoring of Modified Psycho-Acoustic Annoyance Model with Edge Computing for 5G IoT Systems

Next-generation IoT systems will allow sustainable performance in long-term monitoring systems. This sustainability concept applies to soundscape description, as it allows monitoring in urban environments. In this work, the implementation of psycho-acoustic annoyance models in a 5G-enabled IoT system is proposed, applying two edge-computing approaches. A modified Zwicker’s model is adopted in this research, introducing a term that takes into account the tonal component of the captured sound. These implementations have been validated in a measurement campaign where several IoT devices have been deployed to evaluate different sound environments of a university campus. Then, the analysis of the sound-quality metrics is conducted in a different location, showing that if tonality is present in a noisy environment, it results in greater subjective annoyance. Moreover, the Just-Noticeable Difference of these results is derived from Zwicker’s psycho-acoustic annoyance to establish a limitation for this metric.

Cognitive Cameras on the Edge for Crowd Physical Distancing Monitoring in the Covid-19 Era

With the rise of the Internet of Things (IoT) architectures and protocols, new video analytics systems and surveillance applications have been developed. In conventional systems, all the streams produced by cameras are sent to a centralized node where they can be seen by human operators whose task is to identify uncommon on abnormal situations. However, this way, much bandwidth is necessary for the system to work, and the number of necessary resources is proportional to the number of cameras and streams involved. In this paper, we propose an interesting approach to this problem: transforming any IP camera into a cognitive object. A cognitive camera (CC) can be considered a classic connected camera with onboard computational power for intelligent video processing. A CC can understand and interact with the surroundings, intelligently analyze complex scenes, and interact with the users. The IoT Edge Computing approach decreases latency in the decision-making process and consumes a tiny portion of bandwidth concerning the stream of a video, even in low resolution. CCs can help to address COVID-19. As a preventive measure, proper crowd monitoring and management systems must be installed in public places to limit sudden outbreaks and improve healthcare. The number of new infections can be significantly reduced by adopting physical distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for physical distance classification using CCs is proposed in this research paper. The experiment on Movidius board, an AI accelerator device, provides promising results of our proposed method in which the accuracies can achieve more than 85% from different datasets.

A Mobile Edge Computing Approach for Vehicle to Everything Communications

This paper explores the exploitation of Mobile/Multi-access Edge Computing (MEC) for Vehicle-to-Everything (V2X) communications. Certain V2X applications that aim at improving road safety require reliable and low latency message delivery. As the number of connected vehicles increases, these requirements cannot be satisfied by technologies relying on the IEEE 802.11p standard. Therefore, the exploitation of the 4th generation Long Term Evolution (LTE) mobile networks has been considered. However, despite their widespread use, LTE systems are characterized by high end-to-end latency since the messages have to traverse the core network. MEC addresses this problem by offering computing, storage and network resources at the edge of the network closer to the end-users. This paper aims at investigating the benefits MEC may offer toward implementing V2X communications. In this framework, simulation scenarios were examined concerning various V2X use cases implemented employing either LTE or MEC. The simulation results indicate a clear superiority of MEC over LTE, especially in the case of delivering critical data.

A characterization of quality of sheared edge in fine blanking using edge-computing approach

In fine blanking the sheared edge’s quality is of major importance. As the sheared edge needs to transmit process forces and precisely align parts, attributes like die rolls, tears and tear-offs need to be eliminated. Currently, these attributes are manually determined at the end of the process chain, which makes a complete correlation between influencing factors and the attributes hardly possible. If it would be possible to create a real-time evaluation of the attributes, not only unknown correlations would be found, also an instant process adaption could be made, optimizing the quality of the sheared edge. Therefore, this contribution focusses on the development of an edge computing approach.