Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Obinikpo, Alex Adim; Kantarcı, Burak

doi:10.3390/jsan6040026

Cited by 48 publications

(30 citation statements)

References 89 publications

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“…Several authors have proposed taxonomies for dealing with IoE and IoT systems in the following distinct approaches: technology and architecture design [ 24 , 35 , 36 , 37 , 38 , 39 , 40 ], sensors’ capabilities [ 26 , 41 , 42 , 43 , 44 , 45 ], and observation context issues [ 25 , 27 , 28 , 46 , 47 , 48 , 49 , 50 , 51 ]. However, still, due to the considerable heterogeneity of actual IoT devices, these taxonomies have approach limitations, mostly restricted to enabling technology and infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.

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Section: Related Workmentioning

confidence: 99%

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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“…Reddy and Mehta [77] propose a system for smart traffic management for smart cities. Muhammed et al [48] and Obinikpo and Kantarci [78] applied deep learning to deal with the concerns in the health sector. Finally, Madu et al [79] propose a framework to evaluate urban sustainability utilizing deep learning.…”

Section: Deep Learningmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neurofuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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“…Aside from their many advantages, deep learning solutions cannot replace machine-learning techniques in the smart city ecosystem. Deep learning is particularly suitable for applications that involve high data dimensionality [99]. Particularly, these solutions are becoming more relevant with the diffusion of crowd-sensing and non-dedicated sensing [52], where only deep learning techniques can cope with the astounding volume and velocity of the data [48].…”

Section: :27mentioning

confidence: 99%

“…Typically, CNN, DBN, RBM, and Autoencoders are suggested for image processing. CNN-based solutions are proposed for both signal and video processing [99]. Table 3 summarizes this section's discussion about deep learning techniques.…”

Section: :27mentioning

confidence: 99%

Smart City System Design

et al. 2019

Self Cite

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Recent global smart city efforts resemble the establishment of electricity networks when electricity was first invented, which meant the start of a new era to sell electricity as a utility. A century later, in the smart era, the network to deliver services goes far beyond a single entity like electricity. Supplemented by a wellestablished Internet infrastructure that can run an endless number of applications, abundant processing and storage capabilities of clouds, resilient edge computing, and sophisticated data analysis like machine learning and deep learning, an already-booming Internet of Things movement makes this new era far more exciting. In this article, we present a multi-faceted survey of machine intelligence in modern implementations. We partition smart city infrastructure into application, sensing, communication, security, and data planes and put an emphasis on the data plane as the mainstay of computing and data storage. We investigate (i) a centralized and distributed implementation of data plane's physical infrastructure and (ii) a complementary application of data analytics, machine learning, deep learning, and data visualization to implement robust machine intelligence in a smart city software core. We finalize our article with pointers to open issues and challenges.

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Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Cited by 48 publications

References 89 publications

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Smart City System Design

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