scholarly journals Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

2018 ◽  
Vol 5 (6) ◽  
pp. 4829-4842 ◽  
Author(s):  
Francesco Restuccia ◽  
Salvatore D'Oro ◽  
Tommaso Melodia
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Software Defined Networking ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Management of Software-Defined Networking Powered by Artificial Intelligence

2021 ◽  
Author(s):  
Jehad Ali ◽  
Byeong-hee Roh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Heterogeneous Networks ◽  
Software Defined Networking ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Underlying Network ◽  
And Control ◽  
The Internet Of Things

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

scholarly journals A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 880
Author(s):  
Imran ◽  
Zeba Ghaffar ◽  
Abdullah Alshahrani ◽  
Muhammad Fayaz ◽  
Ahmed Mohammed Alghamdi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Internet Of Things ◽  
Software Defined Networking ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Resources Management ◽  
Learning Techniques ◽  
The Internet Of Things

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

Current Trends in Integrating the Internet of Things Into Software Engineering Practices

2021 ◽  
pp. 413-428
Author(s):  
S. Kavitha ◽  
J. V. Anchitaalagammai ◽  
S. Nirmala ◽  
S. Murali
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Internet Of Things ◽  
The Internet ◽  
Infrastructure Management ◽  
Integration Testing ◽  
Current Trends ◽  
Software Construction ◽  
Engineering Practices ◽  
The Internet Of Things

The chapter summarizes the concepts and challenges of DevOps in IoT, DevSecOps in IoT, integrating security into IoT, machine learning and AI in IoT of software engineering practices. DevOps is a software engineering culture and practice that aims at unifying software development (Dev) and software operation (Ops). The main characteristic of DevOps is the automation and monitoring at all steps of software construction, from integration, testing, releasing to deployment and infrastructure management. DevSecOps is a practice of integrating security into every aspect of an application lifecycle from design to development.

Emerging Technologies and Universities

2021 ◽  
pp. 97-114
Author(s):  
Fausto E. Jacome
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
New Technologies ◽  
Emerging Technologies ◽  
The Internet ◽  
New Paradigm ◽  
New Era ◽  
Technological Revolution ◽  
Goods And Services ◽  
The Internet Of Things

Emerging technologies such as machine learning, the cloud, the internet of things (IoT), social web, mobility, robotics, and blockchain, among others, are powering a technological revolution in such a way that are transforming all human activities. These new technologies have generated creative ways of offering goods and services. Today's consumers demand in addition to quality, innovation, a real-time and ubiquitous service. In this context, what is the challenge that academy faces? What is the effect of these new technologies on the universities mission? What are people's expectations about academy in this new era? This chapter tries to get answers to these questions and explain how these emerging technologies are converting universities to lead society transformation to the digital age. Under this new paradigm, there are only two roads: innovate or perish. As might be expected universities are embracing these technologies for innovating themselves.

Sign in / Sign up

Export Citation Format

Share Document