scholarly journals The Future of Emerging IoT Paradigms: Architectures and Technologies

2019 ◽  
Author(s):  
Abdul Salam ◽  
Anh Duy Hoang ◽  
Atluri Meghna ◽  
Dylan R. Martin ◽  
Gabriel Guzman ◽  
...  
Keyword(s):  
Vehicular Networks ◽  
New Technologies ◽  
Smart Cities ◽  
Wireless Systems ◽  
Power Grids ◽  
Control Power ◽  
The Future ◽  
Iot Devices ◽  
Smart Thermostat ◽  
Communication Methods

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

WiFi-Enabled User Authentication through Deep Learning in Daily Activities

2021 ◽  
Vol 2 (2) ◽  
pp. 1-25
Author(s):  
Cong Shi ◽  
Jian Liu ◽  
Hongbo Liu ◽  
Yingying Chen
Keyword(s):  
Deep Learning ◽  
User Authentication ◽  
Smart Cities ◽  
Security And Privacy ◽  
Daily Activities ◽  
Indoor Environments ◽  
Home Environments ◽  
Training Cost ◽  
Iot Devices ◽  
Smart Thermostat

User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities.

Security Aspects of the Internet of Things

2021 ◽  
pp. 207-233
Author(s):  
Dominik Hromada ◽  
Rogério Luís de C. Costa ◽  
Leonel Santos ◽  
Carlos Rabadão
Keyword(s):  
Internet Of Things ◽  
New Technologies ◽  
Smart Cities ◽  
The Internet ◽  
Detection Systems ◽  
Private Data ◽  
Promising Solution ◽  
Iot Devices ◽  
Smart Industry ◽  
The Internet Of Things

The Internet of Things (IoT) comprises the interconnection of a wide range of different devices, from Smart Bluetooth speakers to humidity sensors. The great variety of devices enables applications in several contexts, including Smart Cities and Smart Industry. IoT devices collect and process a large amount of data on machines and the environment and even monitor people's activities. Due to their characteristics and architecture, IoT devices and networks are potential targets for cyberattacks. Indeed, cyberattacks can lead to malfunctions of the IoT environment and access and misuse of private data. This chapter addresses security concerns in the IoT ecosystem. It identifies common threats for each of IoT layers and presents advantages, challenges, and limitations of promising countermeasures based on new technologies and strategies, like Blockchain and Machine Learning. It also contains a more in-depth discussion on Intrusion Detection Systems (IDS) for IoT, a promising solution for cybersecurity in IoT ecosystems.

scholarly journals A MACHINE LEARNING FRAMEWORK BASED ON VARIOUS NETWORK TRAFFIC CHARACTERISTICS TO IDENTIFY AND CLASSIFY THE DEFAULT BEHAVIOR OF IOT DEVICES ON A NETWORK

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Pagalla Bhavani Shankar ◽  
Yogi Reddy Maramreddy ◽  
Padala S Venkata Durga Gayatri
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Network Flow ◽  
New Technologies ◽  
Smart Cities ◽  
Smart Environments ◽  
Traffic Characteristics ◽  
Trade Offs ◽  
Iot Devices ◽  
Robust Framework

The Internet of Things (IoT) is being well acquire to the next era of revolutionary generations amongst the new technologies. IoT technology being hailed so hard we had to stop in our society, smart homes, enterprises, and smart cities. Dynamics of smart one’s are increasingly being equipped with a profusion of IoT devices. Due to the tremendous upgradation of knowledge in various aspects impresarios of such smart environments may not even be fully aware of their working nature or principles of IoT devices, assets and functioning properly safe from cyberattacks. In this paper, we addressing this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the level of network level. As a part of robust framework, firstly, we have a tendency to instrument a smart environment with 28 completely different IoT devices, spanning cameras, lights, plugs, motion sensors, appliances and health-monitors. We have a tendency to collect and synthesize traffic traces from this framework infrastructure for a period of 6 months, a type of subset of which we release as open data for the community to use. Second, we have to present or gifts the insights into the underlying network traffic characteristics using statistical and applied mathematical attributes such as activity cycles, port numbers, signaling patterns and cipher suites. Third, we have a tendency to develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99% accuracy based on their network flow of activity. Finally, we have a tendency to discuss the trade-offs between cost, speed, and performance involved in deploying the classification network framework in real-time. Our study paves the way for impresarios of smart environments to monitor their IoT devices and assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

Into the Future: Language, Culture, and New Technologies

2000 ◽  
Vol 45 (4) ◽  
pp. 437-439
Author(s):  
Michele Knobel
Keyword(s):  
New Technologies ◽  
The Future

Legal Providing of Application of Energy Effective Lightning Technology and Intellectual Networks in the Conditions of Digital Economy

2019 ◽  
pp. 123-128 ◽  
Author(s):  
Maksim V. Demchenko ◽  
Rostislav O. Ruchkin ◽  
Eugenia P. Simaeva
Keyword(s):  
Public Relations ◽  
New Technologies ◽  
Electric Power Industry ◽  
Legal Regulation ◽  
Power Grids ◽  
Smart Power ◽  
Smart Power Grids ◽  
Russian Economy ◽  
Intellectual Networks ◽  
Efficient Power

The article substantiates the expediency of improving the legal support for the introduction and use of energy-efficient lighting equipment, as well as smart networks (Smart Grid), taking into account the ongoing digitalization of the Russian economy and electric power industry. The goal of scientific research is formulated, which is to develop practical recommendations on optimization of the public relations legal regulation in the digital power engineering sector. The research methodology is represented by the interaction of the legal and sociological aspects of the scientific methods system. The current regulatory and legal basis for the transformation of digital electricity relations has been determined. The need to modernize the system of the new technologies introduction legal regulation for generation, storage, transmission of energy, intelligent networks, including a riskbased management model, is established. A set of standardsetting measures was proposed to transform the legal regulation of public relations in the field of energyefficient lighting equipment with the aim of creating and effectively operating a single digital environment, both at the Federal and regional levels. A priority is set for the development of “smart” power grids and highly efficient power equipment in the constituent entities of the Russian Federation through a set of legal, economic (financial), edu cational measures.

Smart Cities and Smart Regulation

2018 ◽  
Author(s):  
Anita Rønne
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Fossil Fuels ◽  
System Analysis ◽  
New Technologies ◽  
Smart Cities ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Political Agenda ◽  
Smart Regulation

Increasing focus on sustainable societies and ‘smart cities’ due to emphasis on mitigation of climate change is simultaneous with ‘smart regulation’ reaching the forefront of the political agenda. Consequently, the energy sector and its regulation are undergoing significant innovation and change. Energy innovations include transition from fossil fuels to more renewable energy sources and application of new computer technology, interactively matching production with consumer demand. Smart cities are growing and projects are being initiated for development of urban areas and energy systems. Analysis from ‘Smart Cities Accelerator’, developed under the EU Interreg funding programme that includes Climate-KIC,——provides background for the focus on a smart energy system. Analysis ensures the energy supply systems support the integration of renewables with the need for new technologies and investments. ‘Smart’ is trendy, but when becoming ‘smart’ leads to motivation that is an important step towards mitigating climate change.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Site Experience Enhancement and Perspective in Cultural Heritage Fruition—A Survey on New Technologies and Methodologies Based on a “Four-Pillars” Approach

2021 ◽  
Vol 13 (4) ◽  
pp. 92
Author(s):  
Agnese Augello ◽  
Ignazio Infantino ◽  
Giovanni Pilato ◽  
Gianpaolo Vitale
Keyword(s):  
Cultural Heritage ◽  
New Technologies ◽  
Multimodal Interaction ◽  
Heritage Sites ◽  
The Future ◽  
Cultural Interest ◽  
And Behavior

This paper deals with innovative fruition modalities of cultural heritage sites. Based on two ongoing experiments, four pillars are considered, that is, User Localization, Multimodal Interaction, User Understanding and Gamification. A survey of the existing literature regarding one or more issues related to the four pillars is proposed. It aims to put in evidence the exploitation of these contributions to cultural heritage. It is discussed how a cultural site can be enriched, extended and transformed into an intelligent multimodal environment in this perspective. This new augmented environment can focus on the visitor, analyze his activity and behavior, and make his experience more satisfying, fulfilling and unique. After an in-depth overview of the existing technologies and methodologies for the fruition of cultural interest sites, the two experiments are described in detail and the authors’ vision of the future is proposed.

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