Smart city infrastructure protection: real-time threat detection employing online reservoir computing architecture

2021 ◽  
Author(s):  
Lili Gao ◽  
Xiaopeng Deng ◽  
Weimin Yang
Keyword(s):  
Real Time ◽  
Smart City ◽  
Reservoir Computing ◽  
Threat Detection ◽  
Computing Architecture ◽  
Infrastructure Protection

Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma

2021 ◽  
pp. 1-12
Author(s):  
Azarakhsh Jalalvand ◽  
Joseph Abbate ◽  
Rory Conlin ◽  
Geert Verdoolaege ◽  
Egemen Kolemen
Keyword(s):  
Real Time ◽  
Reservoir Computing ◽  
Fusion Plasma

scholarly journals Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams

2019 ◽  
Vol 6 (2) ◽  
pp. 2651-2668 ◽  
Author(s):  
Sefki Kolozali ◽  
Daniel Kuemper ◽  
Ralf Tonjes ◽  
Maria Bermudez-Edo ◽  
Nazli Farajidavar ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Smart City

scholarly journals Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing

2016 ◽  
Vol 9 ◽  
Author(s):  
Dhireesha Kudithipudi ◽  
Qutaiba Saleh ◽  
Cory Merkel ◽  
James Thesing ◽  
Bryant Wysocki
Keyword(s):  
Reservoir Computing ◽  
Computing Architecture ◽  
Biosignal Processing

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

2021 ◽  
Vol 12 (5/6) ◽  
pp. 507
Author(s):  
Ainun Kamal ◽  
Fiza Jefreen ◽  
Md. Pabel Sikder ◽  
Shah Reza Mohammad Fahad Ul Hossain ◽  
Shoaib Mahmud ◽  
...  
Keyword(s):  
Real Time ◽  
Smart City ◽  
Analytical Approach ◽  
City Planning ◽  
Cloud Services

Online Real Time (ORT) Waste Management Through “Si Detektif Sampah” Application in Implementing Smart City in Palangka Raya City

2020 ◽  
pp. 345-354
Author(s):  
Haddad Al Rasyid Sukawan ◽  
Bias Osean Ali ◽  
I Made Arya Widhyastana ◽  
Rini Rachmawati
Keyword(s):  
Waste Management ◽  
Real Time ◽  
Smart City

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

scholarly journals Fog Computing Framework for Smart City Design

2020 ◽  
Vol 14 (01) ◽  
pp. 109
Author(s):  
Mais Haj Qasem ◽  
Alaa Abu-Srhan ◽  
Hutaf Natoureah ◽  
Esra Alzaghoul
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Network Architecture ◽  
Data Exchange ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Network Usage ◽  
The Cost

Fog-computing is a new network architecture and computing paradigm that uses user or near-users devices (network edge) to carry out some processing tasks. Accordingly, it extends the cloud computing with more flexibility the one found in the ubiquitous networks. A smart city based on the concept of fog-computing with flexible hierarchy is proposed in this paper. The aim of the proposed design is to overcome the limitations of the previous approaches, which depends on using various network architectures, such as cloud-computing, autonomic network architecture and ubiquitous network architecture. Accordingly, the proposed approach achieves a reduction of the latency of data processing and transmission with enabled real-time applications, distribute the processing tasks over edge devices in order to reduce the cost of data processing and allow collaborative data exchange among the applications of the smart city. The design is made up of five major layers, which can be increased or merged according to the amount of data processing and transmission in each application. The involved layers are connection layer, real-time processing layer, neighborhood linking layer, main-processing layer, data server layer. A case study of a novel smart public car parking, traveling and direction advisor is implemented using IFogSim and the results showed that reduce the delay of real-time application significantly, reduce the cost and network usage compared to the cloud-computing paradigm. Moreover, the proposed approach, although, it increases the scalability and reliability of the users’ access, it does not sacrifice much time, nor cost and network usage compared to fixed fog-computing design.

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