Highly efficient approach for discordant BSMs detection in connected vehicles environment

Zamouche, Djamila; Aissani, Sofiane; Omar, Mawloud; Mohammedi, Mohamed

doi:10.1007/s11276-022-03104-8

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…The data-centric approach utilizes data generated inside the vehicle to detect abnormalities for self-recovery or transmits this data to the outside to determine whether an abnormality exists in an external system [1][2][3][4]. Image-based approaches identify vehicles through image-processing technology and detect abnormal behavior based on this [5][6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow.

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Section: Related Workmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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“…The network uses both communication modes i.e. V2V and V2I and Dedicated Short Range Communication (DSRC) technology [82] for short and medium communication ranges. The RSUs and vehicles use IEEE 802.11p standard protocol with a data rate of 6 Mbps, while the vehicle coverage range is set to 250 meters using Friis propagation loss model.…”

Section: B Simulation Environmentmentioning

confidence: 99%

Mobile Edge-Based Information-Centric Network for Emergency Messages Dissemination in Internet of Vehicles: A Deep Learning Approach

et al. 2023

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With the rapid advancement of Internet of Things (IoT) communication technologies, the Internet of Vehicles (IoV) has gained significant attention for providing the real-time exchange of emergency traffic information among vehicles and Road Side Units (RSU) to improve ultimate driving experiences and road safety. Information-Centric Networking (ICN) has emerged as a novel networking architecture that shifts the communication model from Internet protocol (IP) based host-centric to content-centric architecture. ICN provides support to push and pull-based messages for efficient content dissemination and retrieval by aiming at content names rather than IP addresses. The Mobile Edge Computing (MEC) paradigm facilitates proximity-based real-time traffic applications and services, reducing the content retrieval latency from the core network without the excessive broadcast overhead. Deep Learning (DL) techniques have been tremendously successful in detecting the severity of real-time traffic data. The integration of DL based ANN model for edge-based ICN-IoV brings real-time traffic prediction, content caching, and forwarding of push-based messages closer to the target area. Furthermore, the deployment of mobile edge servers at critical network positions enhances the availability and responsiveness of the name-based content in the ICN paradigm. In this paper, we propose Mobile Edge-based Emergency Messages Dissemination Scheme (MEMDS) to deliver push-based messages delivery at the event-reported geographical location. We also propose a hybrid DL-based Artificial Neural Network (ANN) and MEMDS model to detect and predict the severity of the safety application under real traces from different cities based on specific parameters. The simulation results demonstrate that the proposed scheme significantly improves the data delivery ratio, average delay, hop count, content retrieval delay, and network overhead than DCN and flooding techniques. Secondly, the proposed hybrid model successfully detects the severity of the request with the highest accuracy, precision, recall, and f1-scores values of 96% than benchmark models using real-time vehicular datasets.INDEX TERMS Artificial neural network, deep learning, Internet of Vehicles, Internet of Things, information-centric networking, mobile edge computing.The associate editor coordinating the review of this manuscript and approving it for publication was Yiming Tang .

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VANET Severity Classification in BSM Messages with a Novel Naïve Bayes Feature Selection

Putrada,

Alamsyah,

Pane

et al. 2023

2023 International Conference on Advancement in Data Science, E-Learning and Information System (ICADEIS)

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Highly efficient approach for discordant BSMs detection in connected vehicles environment

Cited by 3 publications

References 39 publications

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Mobile Edge-Based Information-Centric Network for Emergency Messages Dissemination in Internet of Vehicles: A Deep Learning Approach

VANET Severity Classification in BSM Messages with a Novel Naïve Bayes Feature Selection

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