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.

IoT ecosystem faces fragmented future

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Supply Chain ◽  
Internet Of Things ◽  
Data Privacy ◽  
The Internet ◽  
Privacy And Security ◽  
Content Type ◽  
Iot Devices ◽  
The Internet Of Things

Subject IoT ecosystem. Significance The market for the Internet of Things (IoT) or connected devices is expanding rapidly, with no manufacturer currently forecast to dominate the supply chain. This has fragmented the emerging IoT ecosystem, triggering questions about interoperability and cybersecurity of IoT devices. Impacts Firms in manufacturing, transportation and logistics and utilities are expected to see the highest IoT spending in coming years. The pace of IoT adoption is inextricably linked to that of related technologies such as 5G, artificial intelligence and cloud computing. Data privacy and security will be the greatest constraint to IoT adoption.

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 Technology and More-Than-Human Design

Design Issues ◽  
2020 ◽  
Vol 36 (4) ◽  
pp. 33-44 ◽  
Author(s):  
Elisa Giaccardi ◽  
Johan Redström
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
The Internet ◽  
User Centered Design ◽  
Human Centered Design ◽  
Design Methodologies ◽  
The Everyday ◽  
The Internet Of Things ◽  
User Centered

Are we reaching the limits of what human-centered and user-centered design can cope with? Developing new design methodologies and tools to unlock the potentials of data technologies such as the Internet of Things, Machine Learning and Artificial Intelligence for the everyday job of design is necessary but not sufficient. There is now a need to fundamentally question what happens when human-centered design is unable to effectively give form to technology, why this might be the case, and where we could look for alternatives.

scholarly journals Smart Storage with the Internet of Things and Voice Recognition

2019 ◽  
Vol 9 (2) ◽  
pp. 4348-4352
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Small Business ◽  
Time Complexity ◽  
Voice Recognition ◽  
The Internet ◽  
Entire Field ◽  
The World ◽  
The Internet Of Things

In the era of automation ruling the world by coming into each and every field, now it has entered into the field of Storage. Automation has reduced the time complexity and the manual power in the entire field it has intruded. And likewise it will reduce the time complexity and tracking of the stored items and retrieving the same from the storage. This model of storage can be done with the help of Internet of Things, Cloud computing and machine learning. Cloud computing plays a major role due to its robustness and its portability which does give an extra edge in the business. To survive in business today you need to make smart choices. Storage can be a small business savior. This model can be used in many fields like medicine, business etc. Tracking and retrieving in these large amounts of storage can be made easier with the help of database.

scholarly journals A State of the Art Survey of Machine Learning Algorithms for IoT Security

2021 ◽  
pp. 12-34
Author(s):  
Alan Fuad Jahwar ◽  
Subhi R. M. Zeebaree
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Security Measures ◽  
Iot Security ◽  
Security Issues ◽  
The Internet Of Things

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

Analogizing the Internet of Things

2020 ◽  
Author(s):  
Scott J. Shackelford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Space Exploration ◽  
The Internet ◽  
New Frontier ◽  
The Internet Of Things

As any new frontier opens or industry matures, it’s natural to search for analogies and historical precedents to guide both our actions and perceptions. President Kennedy famously compared space exploration to seafaring.1 The rise of artificial intelligence and machine learning discussed further...

THE FUTURE OF SMART INDUSTRY: TECHNOLOGIES, MACRO-TRENDS AND APPLICATION AREAS

10.6036/10342 ◽  
2021 ◽  
Vol 96 (6) ◽  
pp. 561-562
Author(s):  
MIKEL NIÑO
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Big Data Technologies ◽  
Smart Industry ◽  
The Internet Of Things

The Smart Industry has been developing has been developing at an accelerated pace since the beginning of the last decade, driven by of the last decade, driven by the by the emergence of technologies such as the Internet of Things, Compute of Things, Cloud Computing and Big Data Cloud Computing and Big Data technologies, as well as their connection and Big Data technologies, as well as their connection with machine learning algorithms for predictive data analysis [1] of data [1].

scholarly journals An Agent-Based Approach to Realize Emergent Configurations in the Internet of Things

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1347 ◽  
Author(s):  
Fahed Alkhabbas ◽  
Romina Spalazzese ◽  
Paul Davidsson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
The Internet ◽  
Daily Lives ◽  
Agent Based ◽  
Design Time ◽  
Underlying Processes ◽  
The Internet Of Things

The Internet of Things (IoT) has enabled physical objects and devices, often referred to as things, to connect and communicate. This has opened up for the development of novel types of services that improve the quality of our daily lives. The dynamicity and uncertainty of IoT environments, including the mobility of users and devices, make it hard to foresee at design time available things and services. Further, users should be able to achieve their goals seamlessly in arbitrary environments. To address these challenges, we exploit Artificial Intelligence (AI) to engineer smart IoT systems that can achieve user goals and cope with the dynamicity and uncertainty of their environments. More specifically, the main contribution of this paper is an approach that leverages the notion of Belief-Desire-Intention agents and Machine Learning (ML) techniques to realize Emergent Configurations (ECs) in the IoT. An EC is an IoT system composed of a dynamic set of things that connect and cooperate temporarily to achieve a user goal. The approach enables the distributed formation, enactment, adaptation of ECs, and conflict resolution among them. We present a conceptual model of the entities of the approach, its underlying processes, and the guidelines for using it. Moreover, we report about the simulations conducted to validate the feasibility of the approach and evaluate its scalability.

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.

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