Accurate performance prediction of IoT communication systems for smart cities
An efficient deep learning based solution
Bibliographic Data
| ID | 21234860 |
|---|---|
| Authors | Omar Said (0000-0002-8868-8000, Taif University), Amr Tolba (0000-0003-3439-6413, King Saud University, corresponding author) |
| Year | 2021 |
| Volume | 69 |
| Pages | 102830 |
| Publication date | 2021-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2021.102830 |
| OpenAlex | W3138858865 |
| Language | EN |
| Citations received | 6 |
| References cited | 40 |
Artificial neural network · Communications system · Computer network · Distributed computing · Embedded system · Information and Communications Technology · Internet of Things · Real-time computing · Smart city · Telecommunications network · Computer Science · Human Mobility and Location-Based Analysis · IoT and Edge/Fog Computing · Smart Cities and Technologies · Artificial Intelligence
Green energy harvesting strategies on edge-based urban computing in sustainable internet of things
ConvXSS
Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies
An IOT based efficient energy management in smart grid using DHOCSA technique
Low carbon technology for carbon neutrality in sustainable cities
Where do Smart Cities grow? The spatial and socio-economic configurations of smart city development
A deep learning-based IoT-oriented infrastructure for secure smart City
Urban sustainability management
Smart sustainable cities of the future
Enabling technologies and sustainable smart cities
Deep learning and medical image processing for coronavirus (Covid-19) pandemic
Social inequalities in neighborhood visual walkability
Data mining and machine learning methods for sustainable smart cities traffic classification
Congestion prediction for smart sustainable cities using IoT and machine learning approaches
| Citations per year | 1.2 |
|---|---|
| Citation span | 2021 - 2023 (3) |
| Highly cited | No |