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.

scholarly journals Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
Author(s):  
Giuliano Vitali ◽  
Matteo Francia ◽  
Matteo Golfarelli ◽  
Maurizio Canavari
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Management ◽  
Cropping System ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Crop Fields ◽  
Learning Techniques ◽  
The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

scholarly journals Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

2021 ◽  
Vol 11 (6) ◽  
pp. 5066
Author(s):  
Vusi Sithole ◽  
Linda Marshall
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
The Internet ◽  
Use Case ◽  
Object Oriented Design ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
The Internet Of Things

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

scholarly journals Mitigating Malicious Insider Attacks in the Internet of Things using Supervised Machine Learning Techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 13-28
Author(s):  
Mir Shahnawaz Ahmad ◽  
Shahid Mehraj Shah
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Supervised Machine Learning ◽  
Security Requirements ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Prime Field ◽  
Learning Techniques ◽  
The Internet Of Things

The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network.

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 PERBANDINGAN QUALITY OF SERVICE PROTOKOL KOMUNIKASI DATA PADA SISTEM DETEKSI ASAP ROKOK BERBASIS INTERNET OF THINGS

2020 ◽  
Vol 16 (1) ◽  
pp. 19-24
Author(s):  
Pether V B Romony ◽  
Lanny Sitanayah ◽  
Junaidy B Sanger
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Transmission Control Protocol ◽  
The Internet ◽  
Data Loss ◽  
Transmission Control ◽  
User Datagram Protocol ◽  
Control Protocol ◽  
The Internet Of Things

Asap rokok adalah salah satu asap beracun yang berbahaya bagi kesehatan manusia dari sisi biologis maupun sisi kimiawi. Pada penelitian ini, penulis mengimplementasikansebuah sistem deteksi asap rokok berbasis The Internet of Things menggunakan sensor MQ135, Arduino board dan NodeMCU. Kemudian, penulis melakukan perbandingan Quality of Service dari dua protokol komunikasi data, yaitu Transmission Control Protocol dan User Datagram Protocol pada sistem tersebut. Parameter Quality of Service yang dibandingkan saat proses pengiriman data adalah delay dan data loss. Untuk setiap protokol, simulasi dilakukan selama 1 jam dengan pengiriman data setiap 5 detik, 10 detik, sampai 1 menit. Hasil yang diperoleh adalah data loss dengan Transmission Control Protocol lebih rendah dari pada data loss dengan User Datagram Protocol, sedangkan delay dengan User Datagram Protocol lebih rendah dari pada delay dengan Transmission Control Protocol.

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