A novel SDN-based IoT architecture for big data

Kakız, Muhammet Talha; Öztürk, Ercüment; Çavdar, Tuğrul

doi:10.1109/idap.2017.8090186

Cited by 18 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IoT devices in each network could connect to different access points depending on their locations. In [35], kakiz et al propose an IoT architecture based on SDN to overcome big data issues. Also, [36] focuses on analyzing big data in smart cities using SDN.…”

Section: Related Workmentioning

confidence: 99%

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Montazerolghaem

Yaghmaee

2020

IEEE Internet Things J.

View full text Add to dashboard Cite

Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality of Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this paper, we propose a novel framework based on Software-defined Networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an Integer Linear Programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterwards, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the non-occurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Montazerolghaem

Yaghmaee

2020

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Work [54] suggests an SDN-based IoT architecture solving the problem of large amounts of data generated in IoT. Instead of evaluating the data from sensors in the application layer, Ref.…”

Section: State Of the Art Of Knowledge For Sdn And Iotmentioning

confidence: 99%

“…Instead of evaluating the data from sensors in the application layer, Ref. [54] analyzes them in lower layers, especially in the gateway layer, before being sent to the Internet. The proposed architecture concentrates on solving the big data problems of IoT with the help of SDN.…”

Section: State Of the Art Of Knowledge For Sdn And Iotmentioning

confidence: 99%

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Ding

et al. 2020

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

show abstract

“…It provides a general implementation by virtualizing the IoT gateway which makes it possible to be dynamic, scalable and elastic in the IoT networks. Another IoT architecture originated from SDN to overcome big data problem is proposed in [10]. By evaluating the usefulness of the sensed values in the lower layers (especially in gateway layer) instead of application layer, the number of packets being sent to the Internet is reduced, which overcomes the huge data volume problem of IoT.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks

Zhao

et al. 2019

IEEE Internet Things J.

189

View full text Add to dashboard Cite

Software Defined Internet of Things (SD-IoT) Networks profits from centralized management and interactive resource sharing which enhances the efficiency and scalability of IoT applications. But with the rapid growth in services and applications, it is vulnerable to possible attacks and faces severe security challenges. Intrusion detection has been widely used to ensure network security, but classical detection means are usually signature-based or explicit-behavior-based and fail to detect unknown attacks intelligently, which are hard to satisfy the requirements of SD-IoT Networks. In this paper, we propose an AI-based two-stage intrusion detection empowered by software defined technology. It flexibly captures network flows with a globle view and detects attacks intelligently through applying AI algorithms. We firstly leverage Bat algorithm with swarm division and Differential Mutation to select typical features. Then, we exploit Random forest through adaptively altering the weights of samples using weighted voting mechanism to classify flows. Evaluation results prove that the modified intelligent algorithms select more important features and achieve superior performance in flow classification. It is also verified that intelligent intrusion detection shows better accuracy with lower overhead comparied with existing solutions.

show abstract

A novel SDN-based IoT architecture for big data

Cited by 18 publications

References 10 publications

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks

Contact Info

Product

Resources

About