Related papers: FIT A Fog Computing Device for Speech TeleTreatmen…
Detecting speech from biosignals is gaining increasing attention due to the potential to develop human-computer interfaces that are noise-robust, privacy-preserving, and scalable for both clinical applications and daily use. However, most…
With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people daily lives. With the surge demand on high-quality mobile services at anywhere, how to…
Data-intensive applications are growing at an increasing rate and there is a growing need to solve scalability and high-performance issues in them. By the advent of Cloud computing paradigm, it became possible to harness remote resources to…
Cloud computing has demonstrated itself to be a scalable and cost-efficient solution for many real-world applications. However, its modus operandi is not ideally suited to resource-constrained environments that are characterized by limited…
Industry 4.0 operates based on IoT devices, sensors, and actuators, transforming the use of computing resources and software solutions in diverse sectors. Various Industry 4.0 latency-sensitive applications function based on machine…
In IoT data processing, cloud computing alone does not suffice due to latency constraints, bandwidth limitations, and privacy concerns. By introducing intermediary nodes closer to the edge of the network that offer compute services in…
This article gives an overview of what Fog computing is, its uses and the comparison between Fog computing and Cloud computing. Cloud is performing well in todays World and boosting the ability to use the internet more than ever. Cloud…
This paper considers the support of grant-free massive access and solves the challenge of active user detection and channel estimation in the case of a massive number of users. By exploiting the sparsity of user activities, the concerned…
We propose an agile softwarized infrastructure for flexible, cost effective, secure and privacy preserving deployment of Internet of Things (IoT) for smart healthcare applications and services. It integrates state-of-the-art networking and…
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly…
Fog computing is an architecture that is used to distribute resources such as computing, storage, and memory closer to end-user to improve applications and service deployment. The idea behind fog computing is to improve cloud computing and…
The ability of large language models (LLMs) to transform, interpret, and comprehend vast quantities of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected…
Due to the big data exchange on the Internet of Things, proper routing and selecting the best routes for fast data transmission improve network performance. There are major challenges, like high delay, when cloud computing is used.…
Fog computing plays a crucial role in satisfying the requirements of delay-sensitive applications such as connected vehicles, smart grids, and actuator networks by moving data processing close to end users. Passive optical networks (PONs)…
Edge/Fog computing is a novel computing paradigm that provides resource-limited Internet of Things (IoT) devices with scalable computing and storage resources. Compared to cloud computing, edge/fog servers have fewer resources, but they can…
The heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to…
While the success of edge and fog computing increased with the proliferation of the Internet of Things (IoT) solutions, such novel computing paradigm, that moves compute resources closer to the source of data and services, must address many…
The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing…
Fog and edge computing require adaptive control schemes that can handle partial observability, severe latency requirements, and dynamically changing workloads. Recent research on Agentic AI (AAI) increasingly integrates reasoning systems…
Fog computing was designed to support the specific needs of latency-critical applications such as augmented reality, and IoT applications which produce massive volumes of data that are impractical to send to faraway cloud data centers for…