Smart Storage with the Internet of Things and Voice Recognition

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.

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IMPROVING INTERNET OF THINGS PARKING SYSTEMS

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr2003163b ◽

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.

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Security for IoT

IoT Protocols and Applications for Improving Industry, Environment, and Society - Advances in Web Technologies and Engineering ◽

10.4018/978-1-7998-6463-9.ch006 ◽

2021 ◽

pp. 117-143

Author(s):

Anjum Nazir Qureshi Sheikh ◽

Asha Ambhaikar ◽

Sunil Kumar

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

The Internet ◽

Security Threats ◽

Security Issues ◽

The Earth ◽

The World ◽

Wireless Medium ◽

Iot Devices ◽

The Internet Of Things

The internet of things is a versatile technology that helps to connect devices with other devices or humans in any part of the world at any time. Some of the researchers claim that the number of IoT devices around the world will surpass the total population on the earth after a few years. The technology has made life easier, but these comforts are backed up with a lot of security threats. Wireless medium for communication, large amount of data, and device constraints of the IoT devices are some of the factors that increase their vulnerability to security threats. This chapter provides information about the attacks at different layers of IoT architecture. It also mentions the benefits of technologies like blockchain and machine learning that can help to solve the security issues of IoT.

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

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v9i430226 ◽

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.

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An Overview on Security Schemes Based on Elliptic Curve for Cloud-Iot

JOURNAL OF ADVANCES IN NATURAL SCIENCES ◽

10.24297/jns.v5i1.7861 ◽

2018 ◽

Vol 5 ◽

pp. 352-361

Author(s):

Amrani Ayoub ◽

Rafalia Najat ◽

Jaafar Abouchabaka

Keyword(s):

Cloud Computing ◽

Internet Of Things ◽

Radio Frequency Identification ◽

Secure Communication ◽

Physical Object ◽

The Internet ◽

Huge Number ◽

The World ◽

Frequency Identification ◽

The Internet Of Things

The Internet of things appears as a solution in order to connect people around the world. With this concept of interconnection, sharing and dissemination of information between different physical objects. Many objects and services in different fields will be created, such as smart homes, e-health, transport and logistics that will make our everyday needs easier. The main characteristic of a connected object is that it must be identifiable, using technologies such as RFID (Radio-Frequency Identification), must interact with the environment by adding sensory techniques, and finally a connected object must be able to communicate with others. The evolution of Internet of things, increase the number of connected objects. Devices with sensors, generate a huge number of data. With this evolution, the big questions come! how can we control this big data? Cloud Computing a notion that is not newer than the IoT concept, but it's a revolution has steadily been gaining ground. It's a technology that offers to end users a great services in terms of storage, elasticity, analyzing data and other services . In this paper, we cite the benefits of integrating Cloud Computing and Internet of things to manage data provided by physical object and security difficulties that may have this convergence. We also present an overview of the security algorithms proposed in the literature, based on elliptic curves, in order to secure communication between smart objects and cloud computing.

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Anomaly Detection in IoT Frameworks Using Machine Learning

Applied Approach to Privacy and Security for the Internet of Things - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-2444-2.ch004 ◽

2020 ◽

pp. 88-112

Author(s):

Phidahunlang Chyne ◽

Parag Chatterjee ◽

Sugata Sanyal ◽

Debdatta Kandar

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Anomaly Detection ◽

Physical World ◽

The Internet ◽

Network Transport ◽

Machine Learning Model ◽

The World ◽

Iot Devices ◽

The Internet Of Things

Rapid advancements in hardware programming and communication innovations have encouraged the development of internet-associated sensory devices that give perceptions and information measurements from the physical world. According to the internet of things (IoT) analytics, more than 100 IoT devices across the world connect to the internet every second, which in the coming years will sharply increase the number of IoT devices by billions. This number of IoT devices incorporates new dynamic associations and does not totally replace the devices that were purchased before yet are not utilized any longer. As an increasing number of IoT devices advance into the world, conveyed in uncontrolled, complex, and frequently hostile conditions, securing IoT frameworks displays various challenges. As per the Eclipse IoT Working Group's 2017 IoT engineer overview, security is the top worry for IoT designers. To approach the challenges in securing IoT devices, the authors propose using unsupervised machine learning model at the network/transport level for anomaly detection.

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

DYNA INGENIERIA E INDUSTRIA ◽

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].

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

Telecom IT ◽

10.31854/2307-1303-2019-7-3-50-55 ◽

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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