Significant Trends of Smart Technologies

2022 ◽  
pp. 104-128
Keyword(s):  
Artificial Intelligence ◽  
High Speed ◽  
Predictive Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Open Data ◽  
Using Data ◽  
The Many ◽  
Edge And Fog Computing ◽  
Smart Technologies

Although technology advances in a high speed and in different tracks and sectors, among the many major areas of trends of smart technologies are clouds and artificial intelligence. This chapter presents such significant trends in smart technologies with emphasis on clouds and their applications which make the implementation of smart cities efficient. It focuses on the general paradigm for smart technology platforms with five different levels, including edge and fog computing as well as the internet of things. In the chapter, other trends are covered such as data analytics for strategic decision making, artificial intelligence, machine learning, blockchain, open data, and cloud-based data. It also introduces the significance of using predictive analytics and using data for effective deep learning for smart applications.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals A Model-Based Testing Framework for Validating an IoT Solution for Blockchain-Based Vehicles Communication

2020 ◽  
Author(s):  
Rateb Jabbar ◽  
Moez Krichen ◽  
Mohamed Kharbeche ◽  
Noora Fetais ◽  
Kamel Barkaoui
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Fog Computing ◽  
The Internet ◽  
Computing Platform ◽  
Model Based ◽  
Blockchain Technology ◽  
Fatal Crashes ◽  
Attack Trees ◽  
Smart Technologies

<div>The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every ``thing'' to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving traffic and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in ``Vehicle-to-Vehicle'' (V2V) and ``Vehicle-to-Infrastructure'' (V2I) communication. </div><div>To improve ``Vehicle-to-Everything'' (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform.</div><div> Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.</div>

scholarly journals Smart Cities, Big Data, Artificial Intelligence and Respect for the European Union Data Protection Rules

2020 ◽  
Vol 3 (2) ◽  
pp. 101
Author(s):  
Francisco Javier Durán Ruiz
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Big Data ◽  
Data Protection ◽  
Smart City ◽  
Smart Cities ◽  
Open Data ◽  
Personal Data ◽  
Point Of View ◽  
The European Union

The importance of cities and their populations grow more and more, as well as the need to apply ICT in their management to reduce their environmental impact and improve the services they offer to their citizens. Hence the concept of smart city arises, a transformation of urban spaces that the European Union is strongly promoting which is largely based on the use of data and its treatment using Big data and Artificial Intelligence techniques based in algorithms. For the development of smart cities it is basic, from a legal point of view, EU rules about open data and the reuse of data and the reconciliation of the massive processing of citizens' data with the right to privacy, non-discrimination and protection of personal data. The use of Big data and AI needed for the development of smart city projects requires a particular respect to data protection regulations. In this sense, the research explores in depth the specific hazards of vulnerating this fundamental right in the framework of smart cities due to the use of Big Data and AI.

scholarly journals When 5G Meets Deep Learning: A Systematic Review

2020 ◽  
Author(s):  
Guto Leoni Santos ◽  
Patricia Takako Endo ◽  
Djamel Sadok ◽  
Judith Kelner
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
The Internet ◽  
Battery Life ◽  
Correct Operation ◽  
Broadband Internet ◽  
Main Research

This last decade, the amount of data exchanged in the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. But for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include: high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements. Among these we list ultra-densification, millimeter Wave usage, antennas with massive multiple-input multiple-output (MIMO), antenna beamforming to increase spacial diversity, edge/fog computing, among others. Each of these technologies brings its own design issues and challenges. For instance, ultra-densification and MIMO will increase the complexity to estimate channel condition and traditional channel state information (CSI) estimation techniques are no longer suitable due to the complexity of the new scenarios. As a result, new approaches to evaluate network condition such as by continuously collecting and monitoring key performance indicators become necessary. Timely decisions are needed to ensure the correct operation of such network. In this context, deep learning (DL) models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contributions, this paper presents a systematic review about how DL is being applied to solve some 5G issues. We examine data from the last decade and the works that addressed diverse 5G problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using DL models in 5G scenarios and identify several issues that deserve further consideration.

scholarly journals FOG computing and its various uses in different applications

2018 ◽  
Vol 7 (2.7) ◽  
pp. 345
Author(s):  
Chandra Sekhar Maganty ◽  
Kothamasu Kiran Kumar
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Fog Computing ◽  
End User ◽  
New Era ◽  
Streaming Applications ◽  
Computing Model ◽  
Use Of Resources ◽  
To Come ◽  
Edge And Fog Computing

Cloud computing is the transformation, which involves storing large applications where data or information is exchanged among differ-ent platforms for giving good service to clients who belong to different organizations. It assures great use of resources by making data, software and infrastructure available with minimal cost along with security and reliability. Even though cloud computing gives many advantages, it has certain limitations like network congestion, fault tolerance, less bandwidth etc. To come out of this issue a new era computing model is introduced called Fog Computing. This new computing model can transfer fragile data without any delay to other devices in the network. The only difference between both is fog is located more close to the end user or the device and gives response to the client instantly. Moreover, it is beneficial to the real time streaming applications, internet of things which need reliable internet con-nectivity along with high speed. This paper is a review on Fog Computing, differences in edge and fog computing, use cases of fog and the architecture.

scholarly journals Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

2020 ◽  
Vol 12 (11) ◽  
pp. 190
Author(s):  
Elarbi Badidi ◽  
Zineb Mahrez ◽  
Essaid Sabir
Keyword(s):  
Big Data ◽  
Public Space ◽  
Data Storage ◽  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
Fog Computing ◽  
Space Management ◽  
Network Bandwidth ◽  
Edge And Fog Computing

Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens’ safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over the Internet. The benefit of this move is to manage the vast amounts of data generated by the various city systems, including water and electricity systems, the waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to the various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in the field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe the design of a fog-based data pipeline to address the issues of latency and network bandwidth required by time-sensitive smart city applications.

scholarly journals A Model-Based Testing Framework for Validating an IoT Solution for Blockchain-Based Vehicles Communication

2020 ◽  
Author(s):  
Rateb Jabbar ◽  
Moez Krichen ◽  
Mohamed Kharbeche ◽  
Noora Fetais ◽  
Kamel Barkaoui
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Fog Computing ◽  
The Internet ◽  
Computing Platform ◽  
Model Based ◽  
Blockchain Technology ◽  
Fatal Crashes ◽  
Attack Trees ◽  
Smart Technologies

<div>The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every ``thing'' to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving traffic and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in ``Vehicle-to-Vehicle'' (V2V) and ``Vehicle-to-Infrastructure'' (V2I) communication. </div><div>To improve ``Vehicle-to-Everything'' (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform.</div><div> Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.</div>

scholarly journals Ambient Intelligence and Smart Environments: A Preliminary Overview

2021 ◽  
Author(s):  
Maria Poli
Keyword(s):  
Artificial Intelligence ◽  
Ambient Intelligence ◽  
Smart Cities ◽  
Human Action ◽  
Smart Environments ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
The World ◽  
Digital Environments ◽  
Smart Technologies

Nowadays human activities are incoming at a digitalization stage. The introduction of information technology along with new forms of communication, influence a variety of forms of human action and focus mainly on the integration and the convergence of the digital and physical worlds. The use of more intelligent – electronic solutions, improves the lives of people around the world, according to studies carried out on the ingress of new smart technologies. Artificial and Ambient intelligence nowadays getting more and more attention about the development of smart, digital environments. The Smart Cities designed for All must aim to arrange the disparity in cities through smart technology, making cities both smart and accessible to a range of users regardless of their abilities or disabilities. The birth of “Artificial Intelligence” (AI) has facilitated the complex computations for reality simulation the new communication era of wireless 5G, all combined have given the hope for a new and better future, to reverse disability to empower the humans with more capabilities, to be faster than they can ever be, stronger than they can ever dream. This paper provides an overview of Ambient Intelligence and smart environments, as well as how technological advancements will benefit everyday usage by devices in common spaces such as homes or offices, and how they will interact and serve as a part of an intelligent ecosystem by bringing together resources such as networks, sensors, human-computer interfaces, pervasive computing, and so on.

scholarly journals Efficient Workforce Management within the Concept of "Smart City". Using Artificial Intelligence as Part of the Future Counterterrorism Strategy

2020 ◽  
Author(s):  
Mihai Constantin ◽  
◽  
Anamaria Bucur ◽  
Andra-Nicoleta Borţea ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Smart City ◽  
Human Factor ◽  
Smart Cities ◽  
New Challenges ◽  
The Government ◽  
New Perspective ◽  
The Many ◽  
Public Workers

Today, the world is going through an unprecedented wave of urbanization, an evolution that tends to focus on both the biggest social problems and the biggest opportunities in the area of big cities. Once the concept of "smart city" appeared brought its new challenges for our society. In addition to the many benefits, such as increased quality of life, a smart city is challenging the current government. The security of the citizens in the smart cities is gaining new perspective, but also brings a number of threats, mostly considering the strategies regarding counter-terrorism. The human component, specifically the workforce adapted to the smart city, faces new challenges: the emergence of artificial intelligence, increased demand for digital skills, a must have also in labour market, together with the technologization of all areas of activity, which produces changes in all aspects of daily life. Under these conditions, the human factor is affected by all these changes. One side of the story regards the training and, also, the education of individuals, which must increase digitalisation skills; the other side involves the government who must adapt its strategies and policies to enable these changes in a safe manner for citizens and public workers, who perpetuate these changes through local administrations. Therefore, human resources are an important component in the project that aims developing smart cities that includes also developing adapted protection for citizens, specific to these cities. The use of artificial intelligence in smart cities seems to be the solution to the problems raised by smart cities in relation to the human factor and its vulnerability. But at what cost?

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