| Title |
Nature-inspired algorithm-based secure data dissemination framework for smart city networks |
| ID_Doc |
41198 |
| Authors |
Qureshi, KN; Ahmad, A; Piccialli, F; Casolla, G; Jeon, G |
| Title |
Nature-inspired algorithm-based secure data dissemination framework for smart city networks |
| Year |
2021 |
| Published |
Neural Computing & Applications, 33, 17 |
| DOI |
10.1007/s00521-020-04900-z |
| Abstract |
Unceasing population growth and urbanization have intensified the traditional systems to deal with citizen lifestyle, environment, economic issues and good governess. New communication technologies have played a vital role in changes traditional urbanization into a smarter and comfort zone for the citizen. Due to various systems and integration of several new standards and systems, the smart cities have suffered from various open challenges related to technologies, system controlling and management, scalability and security concerns. The new concepts of nature-inspired solutions have implemented to deal with smart cities' challenges by more optimization and performance-oriented methods. Therefore, this paper aims to handle at least three areas of smart cities including smart mobility, smart living and security provision by developing three nature-inspired solutions. The three proposed solutions are dragon clustering mobility in IoV, moth flame electric management for smart living and ant colony-based intrusion detection system for security provision. These solutions are based on a dragonfly, moth flame and ant colony optimization techniques. The proposed solutions are evaluated in a simulation to check the performance. These solutions will help new researchers to explore the nature-inspired solutions to tackle the new and complex systems of smart cities. |
| Author Keywords |
Optimization; Algorithm; Security; Detection; Living; Mobility; Standards |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000557493000001 |
| WoS Category |
Computer Science, Artificial Intelligence |
| Research Area |
Computer Science |
| PDF |
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