A Research Perspective on Fog Computing

Bermbach, David; Pallas, Frank; Pérez, David García; Plebani, Pierluigi; Anderson, Maya; Kat, Ronen I.; Tai, Stefan

doi:10.1007/978-3-319-91764-1_16

Cited by 64 publications

(90 citation statements)

References 18 publications

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“…This implies that the fog can be seen as the natural evolution of hybrid clouds: public and private clouds are still used, but are augmented with large numbers of additional infrastructure elements in the network between public and private cloud as well as at locations even closer to the edge. These elements come in a variety of flavors ranging from single board computers to small clusters and are operated by a multitude of providers [1]. Figure 1: Fog computing combines and integrates cloud and edge computing with additional compute capacity in the network in-between, leading to highly distributed and heterogeneous environments.…”

Section: What Is Fogmentioning

confidence: 99%

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Fog Computing as Privacy Enabler

Pallas

Raschke

Bermbach

2020

IEEE Internet Comput.

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Despite broad discussions on privacy challenges arising from fog computing, the authors argue that privacy and security requirements might actually drive the adoption of fog computing. They present four patterns of fog computing fostering data privacy and the security of business secrets, complementing existing cryptographic approaches. Their practical application is illuminated on the basis of three case studies.

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Section: What Is Fogmentioning

confidence: 99%

“…Fog computing is an emerging computing paradigm in which applications and data are no longer deployed in the cloud alone. Instead, the cloud is enhanced with machines running closer to end users, e.g., in mobile applications, or data producers, e.g., in Internet of Things (IoT) scenarios [1].…”

Section: Introductionmentioning

confidence: 99%

Fog Computing as Privacy Enabler

Pallas

Raschke

Bermbach

2020

IEEE Internet Comput.

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“…Instead, Fog Computing must be seen as a “resource layer that fits between the edge devices and the cloud data centers, with features that may resemble either” (Varshney and Simmhan, 2017 ). In particular, as discussed in Bermbach et al ( 2018 ), the principles of Service Oriented Computing can be valuable also for Fog-based solutions to create a set of services able to simplify the data management in a Fog infrastructure in terms of new abstraction models able to hide the details of smart devices living on the edge of the network that could be very heterogeneous. Moreover, the effort in Fog computing should also be focused on simplifying resource management while considering both edge and cloud resources.…”

Section: Fog Computingmentioning

confidence: 99%

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

et al. 2020

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Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.

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“…In the same way as in previous sections and to better understand the technologies analyzed, the Table 3 details the most relevant pros and cons of these technologies regarding our proposal. 1 It has been repeatedly argued that the distinction between Edge Computing and Fog Computing is not always clear -either Fog Computing is defined similarly to Edge Computing (all computations taking place at the network edges), or regarded as a combination of Cloud Computing, Edge Computing, and all of the options in between [30]. In particular, there is one aspect that may lead to such confusion: mobile phone -the most popular edge device -is actually also a widely used smart gateway.…”

Section: Human Data Processing Techniquesmentioning

confidence: 99%

Human Data Model

Mäkitalo

Flores-Martín

Flores

et al. 2020

ACM Trans. Comput. Healthcare

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Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications as well as for improving old applications, in particular in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex-data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce Human Data Model (HDM)-a new tool and a programming model for programmers as well as end-users with scripting skills that help to combine data from various sources, to perform computations, and to develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run in almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the paper is inspired by a number of interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model. CCS Concepts: • Information systems → Data management systems; • Software and its engineering → Distributed systems organizing principles; Software design techniques; • Human-centered computing → Human computer interaction (HCI); Ubiquitous and mobile computing; • Computer systems organization → Distributed architectures; Embedded systems.

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A Research Perspective on Fog Computing

Cited by 64 publications

References 18 publications

Fog Computing as Privacy Enabler

Fog Computing as Privacy Enabler

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

Human Data Model

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