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A survey on nature-inspired algorithms and its applications in the Internet of Vehicles
Abstract
Summary: The concept of the Internet of Vehicles (IoV) was evolved to cater to the increasing demands of emerging and advanced vehicular applications such as intelligent transportation systems (ITS). IoV plays a vital role in the development of smart cities and smart transportation. As IoV involves real‐time data, so efficient routing mechanisms, as well as secure protocols, are very much needed to enhance the efficiency and security of the IoV network. In the existing literature, numerous researchers have focused on enhancing the efficiency of the IoV network. However, there still exist some flaws keeping in view the new security vulnerabilities that keep on emerging. So, there is a need to optimize the capabilities of existing frameworks for dealing with such issues. Nature‐inspired algorithms are a category of powerful tools to solve problems of optimization. In this first of its kind state‐of‐the‐art study, the potential of applicability of nature‐inspired algorithms in IoV networks for improving the overall performance of IoV is explored. First, various categories of nature‐inspired algorithms are presented to make the readers well acquainted with the concepts of nature‐inspired algorithms. Different nature‐inspired algorithms having their applicability in different aspects of IoV are studied with a special focus on security, routing, and parking space management for strengthening the existing IoV network. Various open issues and research challenges in the use of different nature‐inspired algorithms in IoV are also presented. Based upon the study conducted, it has been analyzed that nature‐inspired algorithms can optimize the overall performance of IoV networks.
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“The observation of genetic algorithms (GA), one type of optimization technique, is the focus of reference [69], which aimed to improve the security of IoV networks through the observation of GA. According to the survey, GA is frequently utilized to improve the security of IoV comparisons via various swarm intelligence optimizations. The utilization of the GA in an IoV security system was carried”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [69]
In the citing paper
“Despite the fact that there are several surveys on IoV security [12,65,67,69,71,72,73,74,75], few surveys have covered ML-based solutions in security or in trust schemes in this environment.”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [69]
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Sorted by citations
Alalwany, E., & Mahgoub, I. (2024) · Sensors · doi:10.3390/s24020368 · Cited by 130
Metadata
- Authors
- Sharma, S., & Kaushik, B.
- Affiliations
- Shri Mata Vaishno Devi University (IN)
- Corresponding author
- Sharma, Surbhi
- Venue
- International Journal of Communication Systems
- Publisher
- Wiley
- Published
- 2021-06-09
- Volume
- 34
- Issue
- 12
- ISSN
- 1074-5351 (ISSN-L)
- 1099-1131
- OpenAlex
- W3172814497
- Cited by
- 14
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 4 · 2021: 1
- FWCI
- 0.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Communication Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- Internet of Vehicles, bio-inspired algorithms, vehicular networks, vehicle routing, VANET security, parking space management, intelligent transportation systems, metaheuristic optimization, network optimization
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