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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…
Fog computing is an emerging paradigm that aims to meet the increasing computation demands arising from the billions of devices connected to the Internet. Offloading services of an application from the Cloud to the edge of the network can…
In this paper, a novel joint energy and age of information (AoI) optimization framework for IoT devices in a non-stationary environment is presented. In particular, IoT devices that are distributed in the real-world are required to…
The rapid proliferation of IoT applications has intensified the demand for efficient and secure service placement in Fog computing. However, heterogeneous resources, dynamic workloads, and diverse security requirements make optimal service…
Fog computing is a promising computing paradigm in which IoT data can be processed near the edge to support time-sensitive applications. However, the availability of the resources in the computation device is not stable since they may not…
In multi-tiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time IoT applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such…
Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about 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 number of Internet of Things (IoT) applications, especially latency-sensitive ones, have been significantly increased. So, Cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy…
In this paper, we tackle the network delays in the Internet of Things (IoT) for an enhanced QoS through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in the Quality-of-Service (QoS)…
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…
Fog computing becomes a promising technology to process user's requests near the proximity of users to reduce response time for latency-sensitive requests. Despite its advantages, the properties such as resource heterogeneity and…
The Internet of Things (IoT) has been increasingly used in our everyday lives as well as in numerous industrial applications. However, due to limitations in computing and power capabilities, IoT devices need to send their respective tasks…
Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is…
Flexible and efficient wireless resource sharing across heterogeneous services is a key objective for future wireless networks. In this context, we investigate the performance of a system where latency-constrained internet-of-things (IoT)…
Fog computing has become an attractive research topic in recent years. As an extension of the cloud, fog computing provides computing resources for Internet of Things (IoT) applications through communicative fog nodes located at the network…
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT). On the one hand, the learning accuracy of FL can be improved…
Comparing to cloud computing, fog computing performs computation and services at the edge of networks, thus relieving the computation burden of the data center and reducing the task latency of end devices. Computation latency is a crucial…
This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed…
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…