Control-as-a-Service in Cyber-Physical Energy Systems over Fog Computing

Vatanparvar, Korosh; Faruque, Mohammad Abdullah Al

doi:10.1007/978-3-319-57639-8_7

Cited by 10 publications

(11 citation statements)

References 36 publications

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“…As regard fog for IoT, first, as the number of devices connected to the system increases, it also growths the volume of data to be gathered and processed. Usually, Big Data and geodistribution are related properties, since obviously a system with a wide-range and a dense distribution generates more data [45]. Big Data scalability is a basic requirement of IoT applications where a growing number of devices has to be connected and the main goal of Big Data approaches to be effectively valuable in IoT environments.…”

Section: Scalabilitymentioning

confidence: 99%

A survey on fog computing for the Internet of Things

Bellavista

Berrocal

Corradi

et al. 2019

Pervasive and Mobile Computing

233

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Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.

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Section: Scalabilitymentioning

confidence: 99%

A survey on fog computing for the Internet of Things

Bellavista

Berrocal

Corradi

et al. 2019

Pervasive and Mobile Computing

233

View full text Add to dashboard Cite

show abstract

“…Some studies [81], [82] proposed using various techniques and technologies for data storage such as HDFS, HBASE, and HIVE. Furthermore, in [101], the authors stated that there are multiple methods to store and process data (for example, centralized/distributed or locally/remotely). FR5.…”

Section: ) Functional Requirementsmentioning

confidence: 99%

“…Interoperability: Many works analyzed this nonfunctional requirement since it is a crucial part of an architecture, giving systems the ability to exchange and share information regardless of the target language or system. Some of these studies adopted different techniques to conduct it, such as through web services [92]; however, in [101], the authors used fog computing to provide the necessary interoperability. NFR4.…”

Section: Fr7 Application and Presentation Layermentioning

confidence: 99%

Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

et al. 2021

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Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.

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“…Different data management techniques have been implemented to support these requirements in the Fog Computing paradigm. Examples are Hierarchical Data Aggregation [20] and Fog Storage. Fog Storage can even use virtualisation technology to implement a local repository storing the data in a non-volatile memory [21].…”

Section: Motivation and Inspirationmentioning

confidence: 99%

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

2019

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Sensor/Actuator devices are currently being massively adopted, often as nodes of larger sensor networks. These sensor networks are typically dedicated to context acquisition (e.g., get temperature) as well as providing acting services (e.g., open the blinds). However, regarding their own data storage, data is usually sent to Fog/Cloud servers. Fog/Cloud storage solutions provide several advantages over sensor network storage solutions, but also some drawbacks. For instance, in Cloud environments, privacy and legal issues may appear, while in Fog, additional costly hardware must be purchased and maintained, at least a server with redundant storage or many servers when distributed data storage is required. Nowadays, sensor nodes count in thousands around us, and they have significantly increased their storage and computational capabilities over the past few years. Therefore, traditional Fog/Cloud storage solutions could be combined or even replaced by Mist/Edge storage solutions for many use cases. A principal contribution of this paper is a novel data distribution and replication storage solution for wireless sensor networks, the first to consider sensor node heterogeneity to find the optimal storage replication according to node capabilities. The solution has been carefully planned and implemented to run even in very low-end microcontrollers, that lives in many of our surrounding smart devices. Other contributions include data comparing Mist/Edge and Amazon S3 regular storage, showing that there remains plenty of room for research into Mist/Edge storage, as well as into the industry itself.INDEX TERMS Mist computing, edge computing, distributed storage, sensor networks.

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Control-as-a-Service in Cyber-Physical Energy Systems over Fog Computing

Cited by 10 publications

References 36 publications

A survey on fog computing for the Internet of Things

A survey on fog computing for the Internet of Things

Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

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