Related papers: Positioning Fog Computing for Smart Manufacturing
The advent of Industrial IoT (IIoT) along with Cloud computing has brought a huge paradigm shift in manufacturing industries resulting in yet another industrial revolution, Industry 4.0. Huge amounts of delay-sensitive data of diverse…
Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions…
Recent developments in the Internet of Things (IoT) and real-time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the…
As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power…
Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing…
Due to unfolded developments in both the IT sectors viz. Intelligent Transportation and Information Technology contemporary Smart Grid (SG) systems are leveraged with smart devices and entities. Such infrastructures when bestowed with the…
Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders their development and…
Nowadays, the continuous improvement and automation of industrial processes has become a key factor in many fields, and in the chemical industry, it is no exception. This translates into a more efficient use of resources, reduced production…
Fog computing serves as a computing layer that sits between the edge devices and the cloud in the network topology. They have more compute capacity than the edge but much less so than cloud data centers. They typically have high uptime and…
In the long term, the Internet of Things (IoT) is expected to become an integral part of people's daily lives. In light of this technological advancement, an ever-growing number of objects with limited hardware may become connected to the…
In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the…
This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in…
Fog computing aims at extending the Cloud towards the IoT so to achieve improved QoS and to empower latency-sensitive and bandwidth-hungry applications. The Fog calls for novel models and algorithms to distribute multi-service applications…
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…
Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to…
Anomalies are common in network system monitoring. When manifested as network threats to be mitigated, service outages to be prevented, and security risks to be ameliorated, detecting such anomalous network behaviors becomes of great…
Internet of Things (IoT) has accelerated the deployment of millions of sensors at the edge of the network, through Smart City infrastructure and lifestyle devices. Cloud computing platforms are often tasked with handling these large volumes…
The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a…
The Internet of Things (IoT) is transforming industries by connecting billions of devices to collect, process, and share data. However, the massive data volumes and real-time demands of IoT applications strain traditional cloud computing…
The huge amount of data generated by the Internet of things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons.…