Related papers: Data Analytics for Fog Computing by Distributed On…
Stateful applications and virtualized network functions (VNFs) can benefit from state externalization to increase their reliability, scalability, and inter-operability. To keep and share the externalized state, distributed data stores…
Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive…
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…
Fog computing offers increased performance and efficiency for Industrial Internet of Things (IIoT) applications through distributed data processing in nearby proximity to sensors. Given resource constraints and their contentious use in IoT…
In the context of fog computing, we consider a simple case when data centers are installed at the edge of the network and assume that if a request arrives at an overloaded data center, then it is forwarded to a neighboring data center with…
Federated Learning has emerged as a transformative paradigm for collaborative machine learning across distributed environments. However, its performance is strongly influenced by the aggregation strategy used to combine local model updates…
In this paper, the fundamental problem of distribution and proactive caching of computing tasks in fog networks is studied under latency and reliability constraints. In the proposed scenario, computing can be executed either locally at the…
Smart Farming has brought a major transformation in the agriculture process by using the Internet of Things (IoT) devices, emerging technologies such as cloud computing, fog computing, and data analytics. It allows farmers to have real-time…
Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which…
In recent years, there has been a shift in computing architectures, moving away from centralized cloud computing towards decentralized edge and fog computing. This shift is driven by factors such as the increasing volume of data generated…
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable…
Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by the placement of their tasks on the edge and/or cloud servers. Recently, several Deep Reinforcement Learning (DRL)-based…
Modern Internet of Things (IoT) applications generate enormous amounts of data, making data-driven machine learning essential for developing precise and reliable statistical models. However, data is often stored in silos, and strict…
Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model…
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security. To address…
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…
Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The research in this paper is motivated towards characterising and…
Fog computing is introduced by shifting cloud resources towards the users' proximity to mitigate the limitations possessed by cloud computing. Fog environment made its limited resource available to a large number of users to deploy their…
Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data…
Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the…