Fog computing is an emerging paradigm in the Internet of Things (IoT) space, consisting of a middle computation layer, sitting between IoT devices and Cloud servers. Fog computing provides additional computing, storage, and networking resources in close proximity to where data is being generated and/or consumed. As the Fog layer has direct access to data streams generated by IoT devices and responses/commands sent from the Cloud, it is in a critical position in terms of security of the entire IoT system. Currently, there is no specific tool or methodology for analysing the security of Fog computing systems in a comprehensive way.Generic security evaluation procedures applicable to most information technology products are time consuming, costly, and badly suited to the Fog context. In this article, we introduce a methodology for evaluating the security of Fog computing systems in a systematic way. We also apply our methodology to a generic Fog computing system, showcasing how it can be purposefully used by security analysts and system designers. K E Y W O R D Sattack, common criteria, fog computing, methodology, security INTRODUCTIONFog computing is an emerging technology which enriches Cloud computing with additional compute, storage and networking resources in close proximity with the end-user devices which generate and/or consume data streams. 1 With the development of Internet of Things (IoT) systems and applications, increasing volumes of data are being produced by IoT devices at the edges of the network. In this situation, it is often not feasible to send all IoT data to a remote Cloud data center and expect acceptable Quality of Service, especially for applications with low-latency requirements such as augmented reality, industrial control systems, and video streaming. Moreover, many applications such as quantified self which use wearable sensors to monitor individuals life often deal with sensitive personal data. In solely Cloud-based approaches for these applications, all these sensitive data would be sent to the Cloud for processing, leaving the user with little control over the usage of their data.An IoT system architecture with support of Fog computing comprises at least the following three layers: 2 (a) An IoT device layer (comprising the actual sensors and actuators); (b) A Fog computing layer located very close to the IoT devices; and (c) A Cloud computing layer.Fog computing therefore acts as a middle layer, sitting between the Cloud and the IoT devices. Depending on the application, some parts of the computation may be delegated to the Fog layer, which prevents one from having to send Softw: Pract Exper. 2020;50:973-997. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 973 974 FARHADI et al.raw data to the Cloud. As Fog computing servers process local data locally, we also expect a lower usage of long-distance network bandwidth as well as reduced response times. Also, the user data are now processed by computing elements which are geographically close to where data is generated, l...
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 analysis. However this also created new opportunities for a wider range of applications which in turn impose their own requirements on future fog computing platforms. This article presents a study of a representative set of 30 fog computing applications and the requirements that a general-purpose fog computing platform should support.
In recent years, fog computing has emerged as a paradigm that brings computing, storage and networking resources closer to end users and devices at the edge of the network. One of the use cases for fog computing is IoT, where a large amount of data is generated by sensors that need to be pre-processed in place before the results are sent to the cloud for further processing and long-term storage. However, actual fog deployments are at their infancy. In this paper, we present the smart-marina project at La Marina de Valencia in which the LivingFog fog computing platform integrating opensource software and LoRaWAN technologies were used to process data collected from several sensors. We show the benefits of the platform in terms of latency reduction and bandwidth saving. Moreover, the platform has been used by particpants of the "Hack the fog" hackathon to deploy applications to test different innovative ideas on using the sensor data.
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