A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

Sharma, Surbhi; Kaushik, Baijnath

doi:10.1002/dac.4895

Cited by 5 publications

(5 citation statements)

References 114 publications

(181 reference statements)

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“…The authors attempted to demonstrate the potential benefits of context awareness in vehicular networks; however, the authors did not consider ML as a potential solution to the problems of trust and security in IoV. 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.…”

Section: Related Surveysmentioning

confidence: 99%

“…We provide a comprehensive discussion of related surveys, the difference between our survey and these related surveys, as well as the survey’s main focus, which is shown in Figure 2 . 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. For example, the surveys in [ 50 , 70 , 76 ] addressed the use of ML-based trust evaluation in IoV focusing on the trust scheme.…”

Section: Related Surveysmentioning

confidence: 99%

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Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

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Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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“…Furthermore, it elaborates on the significance of nature-inspired algorithms, elucidating their contributions to addressing diverse issues within ad hoc networks. According to [20], nature-inspired algorithms represent a potent toolset for addressing optimization challenges. Various algorithms falling under this category have been explored due to their relevance in distinct facets of the Internet of Vehicles (IoV), with particular emphasis on security, routing, and parking space management.…”

Section: Related Workmentioning

confidence: 99%

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.

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“…Correct selection of cluster centers can improve work efficiency and reduce the consumption of some materials and human resources. Genetic algorithms (GAs) are inspired by the theory of nature, which is known for its global adaptability and robust search capabilities to capture good solutions (Sharma and Kaushik, 2021 ). Therefore, we adopt a genetic algorithm to complete the selection of initial seeds.…”

Section: Clustering Recommendation Based On Psomentioning

confidence: 99%

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Cao

Liu

et al. 2023

Front. Neurorobot.

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Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

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A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

Cited by 5 publications

References 114 publications

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

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