scholarly journals IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review

2021 ◽  
Vol 13 (8) ◽  
pp. 218
Author(s):  
Taher M. Ghazal ◽  
Mohammad Kamrul Hasan ◽  
Muhammad Turki Alshurideh ◽  
Haitham M. Alzoubi ◽  
Munir Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensor Networks ◽  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
Healthcare Sector ◽  
Future Research ◽  
Successful Implementation ◽  
Learning Approaches ◽  
Smart Healthcare

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

scholarly journals Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3198 ◽  
Author(s):  
Victor Garcia-Font ◽  
Carles Garrigues ◽  
Helena Rifà-Pous
Keyword(s):  
Machine Learning ◽  
Sensor Networks ◽  
Smart City ◽  
Smart Cities ◽  
Technical Difficulty ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques ◽  
Complex Scenario

Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments.

scholarly journals Smart City in Practice: Learn from Taipei City

2021 ◽  
Vol 8 (1) ◽  
pp. 50-59
Author(s):  
Muhammad Iqbal
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Successful Implementation ◽  
Smart Transportation ◽  
Smart Building ◽  
Smart Healthcare ◽  
Taipei City ◽  
Smart Education ◽  
Smart Mobility ◽  
Smart Governance

The main aspects of building a smart city according to Frost and Sullivan in 2014 are smart governance, smart technology, smart infrastructure, smart healthcare, smart mobility, smart building, smart energy and smart citizens. The smart city's purpose is to form a comfortable, safe city and strengthen its competitiveness. Based on these indicators, Taipei City can become one of the cities with the best Smart City implementation globally. This article uses a qualitative approach with literature review techniques in data collection. This study's findings indicate that the Smart Education, Smart Transportation, Smart Social Housing and Smart Healthcare policies are essential policies in supporting the successful implementation of smart cities in Taipei City. The four main pillars in implementing smart city in Taipei City have integrated Artificial intelligence and big data in smart city governance in Taipei City.

scholarly journals Blockchain as a Driver for Smart City Development: Application Fields and a Comprehensive Research Agenda

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 853-872 ◽  
Author(s):  
Horst Treiblmaier ◽  
Abderahman Rejeb ◽  
Andreas Strebinger
Keyword(s):  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
Implementation Process ◽  
Future Research ◽  
City Development ◽  
Blockchain Technology ◽  
Research Propositions ◽  
Application Fields ◽  
The Impact

The term “Smart City” denotes a comprehensive concept to alleviate pending problems of modern urban areas which have developed into an important work field for practitioners and scholars alike. However, the question remains as to how cities can become “smart”. The application of information technology is generally considered a key driver in the “smartization” of cities. Detailed frameworks and procedures are therefore needed to guide, operationalize, and measure the implementation process as well as the impact of the respective technologies. In this paper, we discuss blockchain technology, a novel driver of technological transformation that comprises a multitude of underlying technologies and protocols, and its potential impact on smart cities. We specifically address the question of how blockchain technology may benefit the development of urban areas. Based on a comprehensive literature review, we present a framework and research propositions. We identify nine application fields of blockchain technology in the smartization of cities: (1) healthcare, (2) logistics and supply chains, (3) mobility, (4) energy, (5) administration and services, (6) e-voting, (7) factory, (8) home and (9) education. We discuss current developments in these fields, illustrate how they are affected by blockchain technology and derive propositions to guide future research endeavors.

scholarly journals Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1012
Author(s):  
Himanshu Sharma ◽  
Ahteshamul Haque ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Smart City ◽  
Ieee 802.15.4 ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Wireless Sensor ◽  
Ieee 802.15.4 Standard

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

scholarly journals Challenges and Barriers for Net‐Zero/Positive Energy Buildings and Districts—Empirical Evidence from the Smart City Project SPARCS

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 78
Author(s):  
Daria Uspenskaia ◽  
Karl Specht ◽  
Hendrik Kondziella ◽  
Thomas Bruckner
Keyword(s):  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
Scale Up ◽  
Practical Experience ◽  
Positive Energy ◽  
Low Carbon ◽  
Net Zero ◽  
Multidimensional Representation

Without decarbonizing cities energy and climate objectives cannot be achieved as cities account for approximately two thirds of energy consumption and emissions. This goal of decarbonizing cities has to be facilitated by promoting net-zero/positive energy buildings and districts and replicating them, driving cities towards sustainability goals. Many projects in smart cities demonstrate novel and groundbreaking low-carbon solutions in demonstration and lighthouse projects. However, as the historical, geographic, political, social and economic context of urban areas vary greatly, it is not always easy to repeat the solution in another city or even district. It is therefore important to look for the opportunities to scale up or repeat successful pilots. The purpose of this paper is to explore common trends in technologies and replication strategies for positive energy buildings or districts in smart city projects, based on the practical experience from a case study in Leipzig—one of the lighthouse cities in the project SPARCS. One of the key findings the paper has proven is the necessity of a profound replication modelling to deepen the understanding of upscaling processes. Three models analyzed in this article are able to provide a multidimensional representation of the solution to be replicated.

scholarly journals Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

2021 ◽  
Vol 13 (9) ◽  
pp. 4716
Author(s):  
Moustafa M. Nasralla
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Virtual Reality ◽  
Time Series Analysis ◽  
Smart City ◽  
Smart Cities ◽  
Rehabilitation Services ◽  
Simulation Scenario ◽  
Common Problems ◽  
Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

scholarly journals A Unified Smart City Model (USCM) for Smart City Conceptualization and Benchmarking

2016 ◽  
Vol 12 (2) ◽  
pp. 77-93 ◽  
Author(s):  
Leonidas Anthopoulos ◽  
Marijn Janssen ◽  
Vishanth Weerakkody
Keyword(s):  
Health Education ◽  
Smart City ◽  
Smart Cities ◽  
Policy Impact ◽  
Future Research ◽  
Management Architecture ◽  
The World ◽  
City Model ◽  
And Control ◽  
Urban Characteristics

Smart cities have attracted an extensive and emerging interest from both science and industry with an increasing number of international examples emerging from all over the world. However, despite the significant role that smart cities can play to deal with recent urban challenges, the concept has been being criticized for not being able to realize its potential and for being a vendor hype. This paper reviews different conceptualization, benchmarks and evaluations of the smart city concept. Eight different classes of smart city conceptualization models have been discovered, which structure the unified conceptualization model and concern smart city facilities (i.e., energy, water, IoT etc.), services (i.e., health, education etc.), governance, planning and management, architecture, data and people. Benchmarking though is still ambiguous and different perspectives are followed by the researchers that measure -and recently monitor- various factors, which somehow exceed typical technological or urban characteristics. This can be attributed to the broadness of the smart city concept. This paper sheds light to parameters that can be measured and controlled in an attempt to improve smart city potential and leaves space for corresponding future research. More specifically, smart city progress, local capacity, vulnerabilities for resilience and policy impact are only some of the variants that scholars pay attention to measure and control.

scholarly journals Smart cities IOT & Digital network connectivity devices: Optimization on business in Africa

2021 ◽  
pp. 064-071
Author(s):  
Makeri Yakubu Ajiji ◽  
Xi’an Jiaotong Victor Chang ◽  
Targio Hashem Ibrahim Abaker ◽  
Uzorka Afam ◽  
T Cirella Giuseppe
Keyword(s):  
Internet Of Things ◽  
Urban Communities ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
The Internet ◽  
City Development ◽  
Short Article ◽  
The Internet Of Things

Today the world is becoming connected. The number of devices that are connected are increasing day by day. Many studies reveal that about 50 billion devices would be connected by 2020 indicating that Internet of things have a very big role to play in the future to come Considering the perplexing engineering of Smart City conditions, it ought not to be failed to remember that their establishment lies in correspondence advancements that permit availability and information move between the components in Smart City conditions. Remote interchanges with their capacities speak to Smart City empowering advancements that give the open door for their fast and effective execution and extension as well. The gigantic weight towards the proficient city the board has triggered various Smart City activities by both government and private area businesses to put resources into Information and Communication Technologies to discover feasible answers for the assorted chances and difficulties (e.g., waste the executives). A few specialists have endeavored to characterize a lot of shrewd urban areas and afterward recognize openings and difficulties in building brilliant urban communities. This short article likewise expresses the progressing movement of the Internet of Things and its relationship to keen urban communities. Advancement in ICT and data sharing innovation are the drivers of keen city degree and scale. This quick development is changing brilliant city development with the beginning of the Internet of Things (IoT). This transformation additionally speaks to difficulties in building (Kehua, Li, and Fu ,Su et al.1). By knowing the attributes of specific advances, the experts will have the occasion to create proficient, practical, and adaptable Smart City frameworks by actualizing the most reasonable one.

scholarly journals Embodied AI-Driven Operation of Smart Cities: A Concise Review

2021 ◽  
Author(s):  
FARZAN SHENAVARMASOULEH ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini ◽  
Hamid R. Arabnia
Keyword(s):  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Communication Technologies ◽  
Autonomous Driving ◽  
Future Research ◽  
Intelligent Network ◽  
Concise Review ◽  
Information And Communication ◽  
Cloud Technologies

<div>A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using wireless and cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of available resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from these connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient.</div><div>Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays.</div><div>In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.</div>

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