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CIoT-Net: a scalable cognitive IoT based smart city network architecture

Jin-ho Park, Mikail Mohammed Salim, Jeong Hoon Jo, Jose Costa Sapalo Sicato, Shailendra Rathore, Jong Hyuk Park
2019 Human-Centric Computing and Information Sciences  
Introduction Smart cities are witnessing significant growth in data produced by IoT based sensors.  ...  Watson learns data Abstract In the recent era, artificial intelligence (AI) is being used to support numerous solutions for human beings, such as healthcare, autonomous transportation, and so on.  ...  It is considered that IoT architectures accommodate the large and fast data processing requirements for extracting deep insights from data using cognitive computing capabilities in IoT architectures.  ... 
doi:10.1186/s13673-019-0190-9 fatcat:nb5p6ws74bepbggn3s3eyycyyy

Deep Learning for IoT Big Data and Streaming Analytics: A Survey [article]

Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, Mohsen Guizani
2018 arXiv   pre-print
In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain.  ...  We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics.  ...  Despite several works in this direction, IoT fast data analytics based on DL has many spaces for development of algorithms and architectures.  ... 
arXiv:1712.04301v2 fatcat:kr64lst37rhlfcpaxckgzlozvu

Implementation and Design of Wireless IoT Network using Deep Learning

S.V.Manikanthan Et.al
2021 Turkish Journal of Computer and Mathematics Education  
for incorporating wireless communicationenvironment, computers, organizations, and governments.  ...  The formation is subdivided into four tiers, according to the IoT architecture.  ...  The analytics for streaming data on these mechanisms are based on data parallelism and rapid under for parallelism.  ... 
doi:10.17762/turcomat.v12i3.761 fatcat:6zrmbsn4gbettoldvpyjouhp64

A Survey on Edge Computing Systems and Tools

Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai, Xingzhou Zhang, Tongqing Zhou
2019 Proceedings of the IEEE  
A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems.  ...  To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing  ...  In [21] , an open full-stack edge computing-based platform OpenVDAP is proposed for the data analytics of connected and autonomous vehicles (CAVs).  ... 
doi:10.1109/jproc.2019.2920341 fatcat:rocspx5ziffblfzaye2xhebe3e

Deep Learning: Edge-Cloud Data Analytics for IoT

Ananda M. Ghosh, Katarina Grolinger
2019 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)  
Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based approach for data reduction on the edge with the machine learning on the  ...  However, edge computing does not have sufficient resources for complex data analytics tasks.  ...  This study explores the use of deep learning, specifically autoencoders, for data reduction in the edge-cloud IoT data analytics context.  ... 
doi:10.1109/ccece.2019.8861806 dblp:conf/ccece/GhoshG19 fatcat:q5ybcleaoffzfab2m3gcut3uv4

Sustainable Smart Cities: Convergence of Artificial Intelligence and Blockchain

Ashutosh Sharma, Elizaveta Podoplelova, Gleb Shapovalov, Alexey Tselykh, Alexander Tselykh
2021 Sustainability  
The artificial intelligence plays a significant role for big data analytics and presents accurate data analysis in real time.  ...  In this article, blockchain-based IoT framework with artificial intelligence is proposed which presents the integration of artificial intelligence and blockchain for IoT applications.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su132313076 fatcat:vtlgh6lt65fdjiekijwyqyk5au

Big Data in IoT Systems [article]

Fayeem Aziz, Stephan K. Chalup, James Juniper
2019 arXiv   pre-print
Its approach towards Big Data and IoT is predicated on a distinction between the digital economy and the characteristics of what Robin Milner has described as the Ubiquitous Computing System.  ...  Due to rapid progress in Machine Learning and new hardware developments, a dynamic turnaround of methods and technologies can be observed.  ...  SC addressed some aspects of machine learning and data analytics. JJ contributed theoretical and philosophical aspects.  ... 
arXiv:1905.00490v1 fatcat:24xkj2vw5jamjj3wgv6hdfar3a

A Review on Deep Learning Techniques for IoT Data

Kuruva Lakshmanna, Rajesh Kaluri, Nagaraja Gundluru, Zamil S. Alzamil, Dharmendra Singh Rajput, Arfat Ahmad Khan, Mohd Anul Haq, Ahmed Alhussen
2022 Electronics  
With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection.  ...  Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology  ...  Conflicts of Interest: The authors declare that they have no conflict of interest.  ... 
doi:10.3390/electronics11101604 fatcat:xdqompeur5a6pb72e7d5gtpqxa

Internet of Things Analytics for Smart Home Applications

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The overall objective of this paper is to study the existing IoT analytics techniques which are used to build smart applications for homes.  ...  IoT devices are responsible for monitoring and sensing the data about home appliances with the help of sensor nodes, the obtained data is then communicate to given high-end devices for taking the suitable  ...  For edge computing, it analyzed different ML algorithms like Clustering, Markov-model, Linear support vector machine, Cascade classifier, Deep learning, Linear regression algorithm and Bayesian Networks  ... 
doi:10.35940/ijitee.g5295.059720 fatcat:ipkmkf25wzcflpzwryzl46wpk4

Deep Learning for Edge Computing Applications: A State-of-the-art Survey

Fangxin Wang, Miao Zhang, Xiangxiang Wang, Xiaoqiang Ma, Jiangchuan Liu
2020 IEEE Access  
In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances  ...  The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications.  ...  On one hand, the applications of edge computing urgently need the powerful processing capabilities of deep learning to handle various complicated scenarios, such as video analytics [9] , transportation  ... 
doi:10.1109/access.2020.2982411 fatcat:43atfhktujbuxns2bsl2cfpnay

Fog-Assisted wIoT: A Smart Fog Gateway for End-to-End Analytics in Wearable Internet of Things [article]

Nicholas Constant, Debanjan Borthakur, Mohammadreza Abtahi, Harishchandra Dubey, Kunal Mankodiya
2017 arXiv   pre-print
Results demonstrated the usability and potential of proposed architecture for converting the real-world data into useful analytics while making use of knowledge-based models.  ...  This paper presents an end-to-end architecture that performs data conditioning and intelligent filtering for generating smart analytics from wearable data.  ...  Smart Fog Computing: Semantics, Cognition and Perception We used knowledge-based models for computation and analytics from big data collected by wearables connected to internet.  ... 
arXiv:1701.08680v1 fatcat:24mu72mf4rbhjeki4bubkhpwty

WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

Aras R. Dargazany, Paolo Stegagno, Kunal Mankodiya
2018 Mobile Information Systems  
(IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part  ...  of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent  ...  CNS-Brain vs DL & cloud computing for big data analytics The cloud computing servers are equivalent to the physical architecture of the brain, and the DL-based big data analytics resembles the function  ... 
doi:10.1155/2018/8125126 fatcat:ty3a7n4in5aahbqyl7wum5vonq

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

Dina Fawzy, Sherin M. Moussa, Nagwa L. Badr
2022 IEEE Access  
Despite of the massive studies dedicated for IoT, no explicit processing architecture is proposed based on real investigation of software engineering concepts and big data analytics characteristics in  ...  The review deduces a proposed domain-independent software architecture for big IoT data analytics, maintaining various IoT data processing challenges, including data scalability, timeliness, heterogeneity  ...  Authors in [18] provided a survey for deep learning techniques only in IoT by investigating the current types of deep learning approaches applied in many IoT applications and evaluating their processing  ... 
doi:10.1109/access.2022.3140409 fatcat:gshllsojgneiregt2kgmt5jlza

Review of the Complexity of Managing Big Data of the Internet of Things

David Gil, Magnus Johnsson, Higinio Mora, Julian Szymański
2019 Complexity  
The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well.  ...  There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT).  ...  Acknowledgments The authors acknowledge the support from the research center Internet of Things and People (IOTAP) at Malmö University in Sweden.  ... 
doi:10.1155/2019/4592902 fatcat:ltxfdzaqvnfq7ajyj3nufm65re

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics [article]

Petar Radanliev, David De Roure, Kevin Page, Max Van Kleek, Rafael Mantilla Montalvo, Omar Santos, La Treall Maddox, Stacy Cannady, Pete Burnap, Eirini Anthi, Carsten Maple
2020 arXiv   pre-print
This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for  ...  This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed  ...  (ML) and deep learning (DL) to create SDN-based NIDS [46] .  ... 
arXiv:2005.12150v1 fatcat:2aexajsa3ze2fbnskqry7sayd4
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