相关论文: AdaptiveFog: A Modelling and Optimization Framewor…

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking fog applications, however, is hard since runtime…

With the rapid growth of the Internet of Things (IoT) and a wide range of mobile devices, the conventional cloud computing paradigm faces significant challenges (high latency, bandwidth cost, etc.). Motivated by those constraints and…

Modern smart grids demand fast, intelligent, and energy-aware computing at the edge to manage real time fluctuations and ensure reliable operation. This paper introduces FOGNITE Fog-based Grid In intelligence with Neural Integration and…

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of…

Edge-cloud collaborative inference is becoming a practical necessity for LLM-powered edge devices: on-device models often cannot afford the required reasoning capability, while cloud-only inference could be prohibitively costly and slow…

Federated learning (FL) has become a transformative paradigm for distributed machine learning across wireless networks. However, the performance of FL is often hindered by the unreliable communication links between resource-constrained…

The number of applications running in the cloud has dramatically increased in the past decade as well as the number of users accessing them. Data centres resources, architectures and conditions define the performance of the applications…

Prior to the advent of the cloud, storage and processing services were accommodated by specialized hardware, however, this approach introduced a number of challenges in terms of scalability, energy efficiency, and cost. Then came the…

Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this…

With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service (QoS) for IoT applications through fog computing is becoming an important problem. In this paper, we…

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a…

The rapid development of vehicles on-board units and the proliferation of autonomous vehicles in modern cities create a potential for a new fog computing paradigm, referred to as vehicular fog computing (VFC). In this paper, we propose an…

Current fog systems rely on centralized and strongly consistent services for configuration management originally designed for cloud systems. In the geo-distributed fog, such systems can exhibit high communication latency or become…

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The…

Meeting the requirements of future services with time sensitivity and handling sudden load spikes of the services in Fog computing environments are challenging tasks due to the lack of publicly available Fog nodes and their characteristics.…

Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications requires methods that handle the resource diversity and network structure of Fog devices while maximizing the service…

Vehicular fog computing (VFC) is a promising paradigm for reducing the computation burden of vehicles, thus supporting delay-sensitive services in next-generation transportation networks. However, traditional VFC schemes rely on radio…

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and…

Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods…

The present manuscript concentrates on the application of Fog computing to a Smart Grid Network that comprises of a Distribution Generation System known as a Microgrid. It addresses features and advantages of a smart grid. Two computational…