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The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively.…

The surge in Internet of Things (IoT) devices and data generation highlights the limitations of traditional cloud computing in meeting demands for immediacy, Quality of Service, and location-aware services. Fog computing emerges as a…

The amount of data being produced at every epoch of second is increasing every moment. Various sensors, cameras and smart gadgets produce continuous data throughout its installation. Processing and analyzing raw data at a cloud server faces…

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of IoT applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide…

Intelligent Internet-of-Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence". This shall unleash the full…

Resource management in Fog computing is very complicated as it engages significant number of diverse and resource constraint Fog nodes to meet computational demand of IoT-enabled systems in distributed manner. Its integration with Cloud…

Fog computing can provide computational resources and low-latency communication at the network edge. But with it comes uncertainties that must be managed in order to guarantee Service Level Agreements. Service observability can help the…

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the…

Due to the big data exchange on the Internet of Things, proper routing and selecting the best routes for fast data transmission improve network performance. There are major challenges, like high delay, when cloud computing is used.…

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…

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this…

The emerging Fog paradigm has been attracting increasing interests from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide. Many Fog applications such as video mining and event…

The Internet of Things(IoT) will revolutionize the Future Internet through ubiquitous sensing. One of the challenges of having the hundreds of billions of devices that are estimated to be deployed would be rise of an enormous amount of…

The IoT area has grown significantly in the last few years and is expected to reach a gigantic amount of 50 billion devices by 2020. The appearance of serverless architectures, specifically highlighting FaaS, raises the question of the of…

Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the…

With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting…

Resource management is the principal factor to fully utilize the potential of Edge/Fog computing to execute real-time and critical IoT applications. Although some resource management frameworks exist, the majority are not designed based on…

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…

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…

Social sensing services use humans as sensor carriers, sensor operators and sensors themselves in order to provide situation-awareness to applications. This promises to provide a multitude of benefits to the users, for example in the…