相关论文: Optimal Service Provisioning in IoT Fog-based Envi…
Recent advances in the areas of Internet of Things (IoT), Big Data, and Machine Learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as…
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…
The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides…
Fog nodes in the vicinity of IoT devices are promising to provision low latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices such as vehicles, wearable devices and smartphones. Owing to…
Fog computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can offload their computation tasks to nearby Fog Nodes. Online scheduling in such fog networks is challenging due to stochastic network…
The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. Building up such flexibility within the edge-to-cloud…
The next generation of mobile networks, namely 5G, and the Internet of Things (IoT) have brought a large number of delay sensitive services. In this context Cloud services are migrating to the edge of the networks to reduce latency. The…
In recent years, there has been a significant expansion in the Internet of Things (IoT), with a growing number of devices being connected to the internet. This has led to an increase in data collection and analysis as well as the…
The rise of Internet of Things (IoT) devices has led to the development of numerous time-sensitive applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but…
Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the…
In recent years, service-oriented-based Internet of Things (IoT) has received massive attention from research and industry. Integrating and composing smart objects functionalities or their services is required to create and promote more…
This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and…
Fog computing significantly enhances the efficiency of IoT applications by providing computation, storage, and networking resources at the edge of the network. In this paper, we propose a federated fog computing framework designed to…
Smart farming is a recent innovation in the agriculture sector that can improve the agricultural yield by using smarter, automated, and data driven farm processes that interact with IoT devices deployed on farms. A cloud-fog infrastructure…
With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can…
The Internet of Things (IoT) devices are highly reliant on cloud systems to meet their storage and computational demands. However, due to the remote location of cloud servers, IoT devices often suffer from intermittent Wide Area Network…
Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference…
Fog computing is a promising paradigm for real-time and mission-critical Internet of Things (IoT) applications. Regarding the high distribution, heterogeneity, and limitation of fog resources, applications should be placed in a distributed…
In this paper, we propose a load balancing algorithm based on Reinforcement Learning (RL) to optimize the performance of Fog Computing for real-time IoT applications. The algorithm aims to minimize the waiting delay of IoT workloads in…
Fog computing is essentially the expansion of cloud computing towards the network edge, reducing user access time to computing resources and services. Various advantages attribute to fog computing, including reduced latency, and improved…