A Software Defined Networking architecture for the Internet-of-Things

Qin, Zhijing; Denker, Grit; Giannelli, Carlo; Bellavista, Paolo; Venkatasubramanian, Nalini

doi:10.1109/noms.2014.6838365

Cited by 300 publications

(143 citation statements)

References 19 publications

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“…In [1], the authors have presented an SDN-based framework for managing traffic and network resources in an IoT environment. Along the same lines, there are a handful of other efforts that suggest taking an SDN-based approach to solving management issues in IoT [2][3][4][5][6].…”

Section: R E T R a C T E D R E T R A C T E D R E T R A C T E D R E T mentioning

confidence: 99%

[Retracted] A Survey on Management Frameworks and Open Challenges in IoT

Kiani

2018

Wireless Communications and Mobile Computing

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The new era of technology is a rollback in which massive information comes from interconnected devices. These types of things that have Internet connectivity are called the Internet of Things (IoT). IoT and its applications have enabled novel service provisioning, whereas at the same time it has raised complex challenges of managing such network. Resource limitation such as computational power and energy limitation defines the characteristics of IoT that require a novel design of management framework. Designing a management framework that meets the IoT requirements is a daunting task. Recently, Software-Defined Networking (SDN) paradigm has eased the management of traditional Internet by provisioning centralized view of the network. This makes SDN a potential candidate for managing IoT. However, traditional design of the SDN paradigm needs to be redefined to adapt it for the IoT. Hence, in this paper, we have discussed trends and research of management architecture for IoT based on Software-Defined Networking principles. In addition, we discuss non-SDN-based approaches. In this paper, we compare all of the efforts in the last five years in addressing issues of IoT management that involves security service provisioning, fault tolerance, energy management, and load balancing. We also discuss future research directions that evolved from our detailed survey and taxonomy of the Software-Defined IoT (SDIoT) management frameworks.

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Section: R E T R a C T E D R E T R A C T E D R E T R A C T E D R E T mentioning

confidence: 99%

[Retracted] A Survey on Management Frameworks and Open Challenges in IoT

Kiani

2018

Wireless Communications and Mobile Computing

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“…For instance, being able to predict with an acceptable accuracy a sudden crowd effect in data consumption on a per-application way can support adaptive scale in/out of virtual servers in NFV and MEC infrastructures and even legacy cloud infrastructure. Dually, virtual link Quality of Service (QoS) reservation operations at the controller level of mobile backhauling network [8] [9] [10] can be triggered as a result of mobile application usage estimation, in particular if explicit traffic engineering and software-defined network interfaces exist.…”

Section: Introductionmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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In this paper, we propose data clustering techniques to predict temporal characteristics of data consumption behavior of different mobile applications via wireless communications. While most of the research on mobile data analytics focuses on the analysis of call data records and mobility traces, our analysis concentrates on mobile application usages, to characterize them and predict their behavior. We exploit mobile application usage logs provided by a Wi-Fi local area network service provider to characterize temporal behavior of mobile applications. More specifically, we generate daily profiles of "what" types of mobile applications users access and "when" users access them. From these profiles, we create usage classes of mobile applications via aggregation of similar profiles depending on data consumption rate, using three clustering techniques that we compare. Furthermore, we show that we can utilize these classes to analyze and predict future usages of each mobile application through progressive comparison using distance and similarity comparison techniques. Finally, we also detect and exploit outlying behavior in application usage profiles and discuss methods to efficiently predict them.

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“…As recent work on IoT, MINA [38] is an integrated network system that provides seamless unification of different wireless access technologies like cellular, WiFi, ZigBee, and Bluetooth and multi-hop communication technologies like MANET. It uses software defined networks (SDNs) and flexibly delivers data streams among devices.…”

Section: Networking Technologies For Iot Data Streamingmentioning

confidence: 99%

Survey of Real-time Processing Technologies of IoT Data Streams

Yasumoto

Yamaguchi

Shigeno

2016

Journal of Information Processing

107

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Abstract:Recently, Internet of Things (IoT) has been attracting attention due to its economical impact and high expectations for drastically changing our modern societies. Worldwide by 2022, over 50 billion IoT devices including sensors and actuators are predicted to be installed in machines, humans, vehicles, buildings, and environments. Demand is also huge for the real-time utilization of IoT data streams instead of the current off-line analysis/utilization of stored big data. The real-time utilization of massive IoT data streams suggests a paradigm shift to new horizontal and distributed architecture because existing cloud-based centralized architecture will cause large delays for providing service and waste many resources on the cloud and on networks. Content curation, which is the intelligent compilation of valuable content from IoT data streams, is another key to fully utilize and penetrate IoT technologies. In this paper, we survey the emerging technologies toward the real-time utilization of IoT data streams in terms of networking, processing, and content curation and clarify the open issues. Then we propose a new framework for IoT data streams called the Information Flow of Things (IFoT) that processes, analyzes, and curates massive IoT streams in real-time based on distributed processing among IoT devices.

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A Software Defined Networking architecture for the Internet-of-Things

Cited by 300 publications

References 19 publications

[Retracted] A Survey on Management Frameworks and Open Challenges in IoT

[Retracted] A Survey on Management Frameworks and Open Challenges in IoT

Characterizing and predicting mobile application usage

Survey of Real-time Processing Technologies of IoT Data Streams

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