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The Internet of Vehicles (IoV) is a developing technology attracting attention from the industry and the academia. Hundreds of millions of vehicles are projected to be connected within the IoV environments by 2035. Each vehicle in the environment is expected to generate massive amounts of data. Currently, surveys on leveraging deep learning (DL) in...
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Citations
... Extracting features from videos of vehicles driving in traffic scenes collected from video surveillance devices [17]. Then utilize deep learning [18]. Train the model using other methods to detect abnormal behavior of the vehicle. ...
The intelligent transportation system with the Internet of Vehicles as its core is gradually penetrating into the lives of urban residents, but it has also exposed security threats such as remote control of vehicles and leakage of personal information of car owners. Compared to the security issues at the level of vehicle end devices and vehicle networking service platforms, the article focuses on the security issues of abnormal behavior in vehicle networking. Based on this, the article reviews the relevant research on abnormal behavior detection mechanisms in the Internet of Vehicles environment in recent years. Firstly, the definition of abnormal behavior was analyzed, and the basic framework for detecting abnormal behavior was provided; Then, the classification of abnormal behavior detection mechanisms was discussed from three aspects: deep learning based abnormal behavior detection, spatiotemporal fusion based abnormal behavior detection, and visual based abnormal behavior detection; Finally, the unresolved technical issues and future research trends in the current abnormal behavior detection mechanism for the Internet of Vehicles were summarized.
... The proposed work performed statistical significance testing on the impact of applying a multi-class neural network and multiclass random forest on a traffic accidents data set [8][9][10][11][12]. Some algorithms of machine learning can help in complicated decisions supporting system solutions [13][14][15][16][17][18][19][20][21][22]; also, some authors discuss the issue of traffic light control as a challenging problem in modern societies [23][24][25][26][27]36]. ...
... Delen and Sharda [32] identified the significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Alikhan and Lee [33][34][35][36][37][38][39][40][41] used the clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents. ...
... Spark is a big data processing framework based on streaming, machine learning, and graph processing Fig. 1. Big data analytic platforms for machine learning Fig. 2. Big Data processing [36]. It is an open-source framework and was developed to overcome some of the limitations of Hadoop MapReduce. ...
In this paper, our focus is on predicting the severity of traffic accidents, which represents a significant advancement in road accident management. Addressing this issue holds crucial implications for emergency logistical planning within urban areas. To assess accident severity within congested settings, we analyze the potential consequences of accidents, aiming to enhance the effectiveness of accident management protocols. In the context of this study, we introduce a real-time big data project. Our approach involves the implementation and comparison of various machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN). The objective is to accurately classify and predict the severity of traffic accidents. Our methodology revolves around the real-time capture of incoming datasets, which are then stored within a Hadoop Distributed File System cluster. Subsequently, we leverage the core functionality of Spark MLlib, making use of pre-implemented Lambda functions. Throughout the project, classification and recognition tasks are conducted as part of the data locality processing paradigm. To validate our approach, we utilize a confusion matrix, which enables us to gauge the interclass impacts among Pedestrians, Vehicles or pillion passengers, and Drivers or riders. For empirical validation, we employ the TRAFFIC ACCIDENTS_2019_LEEDS dataset sourced from the Road Safety Department of Transport. This dataset facilitates the classification of severity predictions into three distinct categories: Pedestrian, vehicle or pillion passenger, and driver or rider. Notably, our experiments reveal impressive results. The Random Forest algorithm achieves an accuracy rate of 93%, outperforming SVM at 82% and ANN at 87%. Furthermore, in terms of precision-recall metrics, Random Forest also excels with a score of 93.82%, compared to SVM's 82.22% and ANN's 87.88%.
... The Internet of Vehicles (IoV) has the potential to generate a large-scale dataset with features running into millions beyond what is currently being witnessed, due to the increase in the number of sensors to be embedded in emerging vehicles for communicating with the IoV environment (Chiroma et al., 2021) [1] . Smart ways of generating, collecting, managing and processing dynamic data from different sources in the IoV pave the way for a large dataset to be accumulated. ...
... The Internet of Vehicles (IoV) has the potential to generate a large-scale dataset with features running into millions beyond what is currently being witnessed, due to the increase in the number of sensors to be embedded in emerging vehicles for communicating with the IoV environment (Chiroma et al., 2021) [1] . Smart ways of generating, collecting, managing and processing dynamic data from different sources in the IoV pave the way for a large dataset to be accumulated. ...
... Vehicles that are connected with sensors, computers, radars, GPS antennas, etc. have the capacity to collect and process a large-scale amount of data as a result of vehicle-to-vehicle communication [20]. Other forms of communication that exist in the IoV environment includes vehicle-to-roadside, vehicle-to-infrastructure, vehicle-to-personal device, vehicle-to-sensor, vehicle-to-pedestrian [17,18,21]; and vehicle-to-home [1], as depicted in Figure 1. The main aim of the IoV is the facilitation of information exchange between the vehicles and other related entities with the objective of reducing vehicle accidents, deviating from traffic congestion and reeling out information services [21]. ...
The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions of relevant, irrelevant and redundant features. Therefore, feature selection to select only the significant features for developing vehicle collision detection alarm systems for deployment in the IoV edge is critical. However, previous studies on vehicle collision detection in the IoV have not conducted rigorous feature selection. Limited studies have mainly applied Pearson correlation coefficient (PCC) to select subset features influencing vehicle collision in the domain of IoV. However, PCC can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking, which, in turn, increases the chances of developing a model that will give a poor outcome. To close this gap, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III with a gain ratio and bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV. The proposed approach selected the minimal most significant subset features for developing a vehicle collision detection classifier with maximum accuracy for deployment in the IoV. A comparative study proves that the proposed approach performs better than the compared algorithms across hybrid-, wrapper- and filter-based feature selection methods within the family of the NAGA. Further, a comparative analysis with other evolutionary algorithms proves the superiority of the proposal. This study can help researchers in the future by avoiding the use of large-scale computing resources in acquiring data to develop vehicle collision alert systems in the IoV. This can be achieved since only the subset features discovered in this study are collected, as opposed to collecting large features, thus saving time and resources in the subsequent vehicle collision detection data collection in the IoV.
... Performance parameters like latency, packet loss, and throughput were considered as indicators in the experiment. In [32], the authors have addressed management of big data. ...
One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.
... Few papers were found to apply deep learning algorithms to solve machine learning problems in 5G powered IoV. Chiroma et al. [102] argued that the deep learning algorithms are anticipated to drive the data analytics in IoV for better understanding and improvement of the IoV because largescale data are predictable to be collected from the IoV as a result of vehicles mobility in the IoV environment. e network slicing isolates the network functions logically and resources that are meant for the vertical market on a common infrastructure of a network. ...
The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers.
Mobile Edge Computing (MEC) is a modern paradigm that involves moving computing and storage resources closer to the network edge,reducing latency, and enabling innovative, delay-sensitive applications.
ithin MEC, computation offloading refers to the process of transfer-ring computationally intensive tasks or processes from mobile devices to edge servers, optimizing the performance of mobile applications. Tradi-tional numerical optimization methods for computation offloading often necessitate numerous iterations to attain optimal solutions. In this paper,we provide a tutorial on how Deep Neural Networks (DNNs) resolve the challenges of computation offloading. The article explores various applications of DNNs in computation offloading, encompassing channel estimation, caching, AR and VR applications, resource allocation, mode selection, unmanned aerial vehicles (UAVs), and vehicle management. We present a comprehensive taxonomy that categorizes these applications,and offer an overview of existing schemes, comparing their effective-ness. Additionally, we outline the open research issues that can be addressed through the application of DNNs in MEC offloading. We also highlight specific challenges related to DNN utilization in computation offloading. In conclusion, we affirm that DNNs are widely acknowledged as invaluable tools for optimizing computation offloading in MEC.
At present, China is moving towards the direction of “Industry 4.0”. The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people’s life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as “IoV”) opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.
Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first, which reduces the average waiting time of job execution. As a result, the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing, significantly improving the speed of data retrieval up to 53% and increasing system throughput by 53%. On the other hand, the proposed job scheduling algorithm defeats both first-in-first-out and memory-sensitive heterogeneous early finish time scheduling algorithms, effectively shortening the average waiting time up to 5% and average weighted turnaround time by 19%, respectively.
