A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks

Lai, Wei Kuang; Lin, Mei-Tso; Wang, Jen Ren

doi:10.1155/2015/374391

Cited by 44 publications

(18 citation statements)

References 24 publications

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“…It was able to predict the network capability of paths to optimize vehicle route selection based on vehicle mobility and transmission capacity. In dynamic IoV, RSUs-based ML can predict the vehicle's moves and direction [45].…”

Section: For Dynamic and High-mobility Iovmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

132

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Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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Section: For Dynamic and High-mobility Iovmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

132

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“…Lai et al developed a routing information system called machine learning‐assisted route selection system. This method collects the road information for routing and stores it in RSU using machine learning.…”

Section: Background Studymentioning

confidence: 99%

Routing using reinforcement learning in vehicular ad hoc networks

Saravanan

Ganeshkumar

2020

Computational Intelligence

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In vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry‐and‐forward scenarios; however, these techniques fail in obtaining accurate traffic density in real‐time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on‐demand routing information. The simulation results show that the DRL‐based routing is effective in routing the data packets between the source and destination vehicles than other existing method.

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“…In [9], a machine learning assisted route selection (MARS) system is proposed to design routing protocols for urban environment. In order to predict the moves of vehicles and choose some suitable routing paths with better transmission capacity, the widely applied machine learning algorithm K-means is adopted.…”

Section: Related Workmentioning

confidence: 99%

“…More detailed information will be described in next part. (1) initialization: determine the relationship between RSUs and vehicles periodically (2) if S is covered by a RSU (3) deliver packets to RSU s in V2R mode else fuzzy-rule-based wireless transmission method vehicle-based short-term vehicle speed prediction (4) (1) find potential paths (5) (2) evaluate potential paths (6) (3) determine wireless transmission path (optimum) (7) S sends packets to RSU s (8) if RSU fails to receive packets after threshold time (9) select another path (sub-optimum) (10) go to step (7) else (11) end (12) RSU s sends packets to RSU d (13) if D is covered by a RSU (14) deliver packets to RSU d in V2R mode else machine learning system (15) (1) ML1 predicts D's turning direction (16) (2) ML2 predicts RSU n (highest possibility) (17) (3) ML3 predicts travelling path (18) two-way mode transfer fuzzy-rule-based wireless transmission method (19) if D fails to receive packets after threshold time (20) select RSU n again (second highest possibility) (21) go to step (17) else (22) end Algorithm 1…”

Section: Proposed Modelmentioning

confidence: 99%

“…The judgment errors are detected using the consistency ratio (CR), which is the ratio of the consistency index (CI) to the random index (RI). The CI value is calculated as (9), where n is the number of decision factors, max is maximal eigenvalue of matrix A, and RI values are shown in Table 4 [27]. The errors in judgments are considered tolerable when CR ≤ 0.1; otherwise, the pairwise comparisons need to be adjusted [25].…”

Section: Relay Node Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy‐Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection

Zhou

Shi

et al. 2018

Wireless Communications and Mobile Computing

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Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy-rule-based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.

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A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks

Cited by 44 publications

References 24 publications

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Routing using reinforcement learning in vehicular ad hoc networks

A Fuzzy‐Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection

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