Internet Of Vehicles Research Articles

IIDS: Intelligent Intrusion Detection System for Sustainable Development in Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) enable various capabilities and functionalities like automated driving assistance, navigation and path planning, cruise control, independent decision making, and low-carbon transportation in the real-time environment. However, the increased CAVs usage renders the potential vulnerabilities in the Internet of Vehicles (IoV) environment, making it susceptible to cyberattacks. An Intrusion Detection System (IDS) is a technique to report network assaults by potential Autonomous Vehicles (AVs) without encryption and authorization procedures for internal and external vehicular communications. This paper proposes an Intelligent IDS (IIDS) to enhance intrusion detection and categorize malicious AVs using a modified Convolutional Neural Network (CNN) with hyperparameter optimization approaches for IoV systems. The proposed IIDS framework works in a 5G Vehicle-to-Everything (V2X) environment to effectively broadcast messages about malicious AVs. Thus IIDS aids in preventing collisions and chaos, enhancing safety monitoring in the traffic. The experimental results depict that the proposed IIDS achieves 98% accuracy in detecting attacks.

A V2I and V2V Collaboration Framework to Support Emergency Communications in ABS-Aided Internet of Vehicles

Providing reliable and flexible emergency communications for the Internet of Vehicles (IoV) is challenging in post-disaster scenarios, especially when communications infrastructures are unavailable or seriously damaged. Under such circumstances, it is highly demanded to deploy the air base station (ABS) to restore connectivity in disaster areas. Although the ABS can offer improved performance, the massive access of communications devices and the rising demand for many emergency-related services will bring new challenges to the ABS-aided IoV. In this paper, we propose a vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) collaboration framework to support emergency communications. Specifically, we incorporate non-orthogonal multiple access (NOMA) into the ABS-aided IoV. By using the successive interference cancellation (SIC) technique, V2I and V2V links can operate in four spectrum reuse modes, which are the extension of the conventional underlaying reuse mode. To fully exploit the advantages of the NOMA enhanced collaboration framework, we formulate a total power consumption minimization problem by jointly considering the mode selection, power control, and the channel state information (CSI) latency. The formulated problem is constrained by the reliability and rate requirements, and the decoding threshold of the SIC technique. Due to the NP-hard characteristics of the formulated problem, we decouple it first, and then adopt the graphical method to derive the analytical solution of optimal power control policies. Afterwards, we can get every feasible spectrum reuse mode. Among the feasible modes, we select the mode with minimum power consumption as the optimal mode. Finally, simulation results demonstrate that our proposed algorithm outperforms the current works in term of the total power consumption, e.g., by adopting the ABS and the NOMA technology, the total power consumption can be reduced by 22.3% and 40.9%, respectively.

Optimal Pricing Design for Coordinated and Uncoordinated IoT Networks

An Internet of Things (IoT) system can include several different types of service providers, who sell IoT service, network service, and computation service to customers, either jointly or separately. A deep understanding of complicated coupling among these providers in terms of pricing and service decisions is critical to the success of IoT networks. This paper studies the impact of the provider interaction structures on the overall IoT system with heterogeneous customers. Specifically, we first study a generic IoT scenario with three interaction structures: coordinated, vertically-uncoordinated, and horizontally-uncoordinated structures. Despite the challenging non-convex optimization problems involved in modeling and analyzing these structures, we successfully obtain the closed-form optimal pricing strategies of providers in each interaction structure. We further extend the analysis to a specific IoT scenario with local computation capability (e.g., Internet of Vehicles (IoV)). We prove that the coordinated structure is better than two uncoordinated structures for both providers and customers, as it avoids selfish price markup behaviors in uncoordinated structures. Between the two uncoordinated structures, when customers' demand variance is large and utility-cost ratio is medium, vertically-uncoordinated structure is better than horizontal one for both providers and customers, due to the complementary providers' competition in horizontally-uncoordinated structure. Counter-intuitively, we identify that providers' optimal prices do not change with their costs at the critical point of customers' full participation in the vertically-uncoordinated structure.

Bilinear pairing-based access control and key agreement scheme for smart transportation

Internet of Vehicles (IoV) enabled Intelligent Transportation System (ITS) allows smart vehicles to communicate with other vehicles on road, humans (customers or pedestrians), infrastructure (parking areas, traffic lights etc), Internet, Cloud etc. The vehicles communicate with other entities over wireless open channels directly or indirectly through messages or beacons. Open channel allows various attacks, like replay, man-in-the-middle, impersonation, fabrication etc., during communication. Also, malicious vehicles can be deployed in the network to misuse or have an unauthorized access to the services. To mitigate these issues, we propose a new remote access control scheme that ensures the secure communication among the vehicles. The vehicles are dynamic in nature in an IoV paradigm, that is, they are not under fixed domains. Therefore, whenever a vehicle changes its location it has to register to the nearest trusted authority (TA) in offline or secured channel mode. To make it applicable, we propose remote registration of the vehicles via the TA. Access control mechanism occurs in two phases: 1) node authentication phase, where vehicles are remotely authenticated by TA and 2) key agreement phase, where after successful mutual authentication they compute a session key by using cryptographic techniques and pre-loaded information. The computed secret session keys are used for ensuring secure communications in future between two vehicles in a cluster as well. Informal security analysis along with formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) show that our access control scheme is secured against various potential attacks. We also show the competency of our scheme by comparing it with other existing schemes in terms of computation and communication costs.

Improved Security-Based Model for Heterogeneous Wireless Sensor Networks Using the Dispense Incline Drop Algorithm

Numerous real-time applications rely on heterogeneous wireless sensor networks (HWSNs), including the Internet of Vehicles (IoV), Internet of Sensors (IoS), medical applications, and so on. Due to the restricted computational capacity, memory, and connectivity of wireless nodes, it is essential to establish authentication techniques that consume less energy, yet guarantee strong security. It takes a lot of time and energy to construct trust models between sensor devices that can recognize and differentiate between benign and malicious attackers, which, in turn, reduces the detection rate and increases overhead. This article suggests a hybrid forewarn approach based on improved security to attain this degree of safety in HWSN communication called the Improved Security-based hybrid forewarn model (ISBHF) and the Dispense Incline Drop Algorithm. It is recommended to offer a trust score and evaluations for messages and knowledge transfer to improve detection capabilities against attackers targeting essential data transmission. To further minimize packet loss, a reliability secure measure is created, and a weighted averaging strategy is implemented to ensure the robustness and veracity of the secure scores. Furthermore, empirical evidence from measurements and simulations will demonstrate that unsecured paths of communication are key to identifying fraudulent nodes that are causing an outsized fraction of network traffic or data breaches. The suggested protocol is superior to the current approach in terms of efficiency and security, making it ideal for use in HWSN networks by decreasing communication overhead, increasing detection performance, and other parameters such as packet delivery ratio and connectivity ratio.

Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV

The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security.

Emergency message broadcasting scheme based on V2V and V2I

As an important part of the advanced Intelligent Traffic System (ITS) in the Internet of Vehicles (IoV), Vehicle to everything (V2X) communication technology provides reliable end-to-end connection and efficient packet transmission. Currently, the main challenge is design and implementation of the multi-hop broadcast protocol. In particular, the network topology is constantly changing because of the high-speed vehicle and diversified road structure, which troubles the selection of forward nodes. Therefore, we propose an Emergency Message Broadcast Protocol (EMBP). In this method, we select the optimal relay node according to the directionality and mobility, channel fading, and link connectivity and apply a suboptimal relay node selection mechanism to guarantee the reliability of broadcast. Simulation results show that EMBP is more efficient and stable than part classical protocols. Due to the complex communication environment of urban roads, to better improve the reliability of emergency message transmission, a broadcasting scheme Roadside Unit (RSU) Emergency Message Broadcast Protocol (REMBP), based on Vehicle to Vehicle (V2V) and Vehicle to Road (V2R) hybrid transmission of emergency messages is proposed on the basis of EMBP. Compared with EMBP, REMBP has better performance in terms of delivery rate, number of forwarding hops, end-to-end delay, average number of collisions, number of packet redundancy, and number of retransmissions.

IoV-Fog-Assisted Framework for Accident Detection and Classification

The evolution of vehicular research into an effectuating area like the Internet of Vehicles (IoV) was verified by technical developments in hardware. The integration of the Internet of Things (IoT) and Vehicular Ad-hoc Networks (VANET) has significantly impacted addressing various problems, from dangerous situations to finding practical solutions. During a catastrophic collision, the vehicle experiences extreme turbulence, which may be captured using Micro-Electromechanical systems (MEMS) to yield signatures characterizing the severity of the accident. This study presents a three-layer design, with the data collecting layer relying on a low-power IoT configuration that includes GPS and an MPU 6050 placed on an Arduino Mega. The fog layer oversees data pre-processing and other low-level computing operations. With its extensive computing capabilities, the farthest cloud layer carries out Multidimensional Dynamic Time Warping (MDTW) to identify accidents and maintains the information repository by updating it. The experimentation compared the state-of-the-art algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest Tree (RFT) using threshold-based detection with the proposed MDTW clustering approach. Data collection involves simulating accidents via VirtualCrash for training and testing, whereas the IoV circuitry would be utilized in actual real-life scenarios. The proposed approach achieved an F1-Score of 0.8921 and 0.8184 for rear and head-on collisions.

Power-Efficient and Trustworthy Data Dissemination for Social Vehicle Associations in the Internet of Vehicles

<div>In modern era, with the global spread of massive devices, connecting, controlling, and managing a significant amount of data in the IoT environment, especially in the Internet of vehicles (IoV) is a great challenge. There is a big problem of high-energy consumption due to overhead-unwanted data communication to the non-participatory vehicles, at high enduring connection rate. Therefore, this article proposed a social vehicle association-based data dissemination approach, which was segregated into three parts: <i>First</i>, develop an improved power evaluation approach for discovering power-efficient vehicles. <i>Second</i>, using the Fokker–Planck equation, the connection likelihood of these vehicles is calculated in the second phase to find trustworthy and steady connections. <i>Last</i>, develop an evaluation approach for vehicles community association using convolutional neural network (CNN). It filtered most likely vehicles to form a community for data dissemination by considering temporal, spatial, and social attributes of vehicles. The proposed approach has evaluated using widespread simulation tests in a highway environment. It verified the efficacy of proposed approach regarding power, linking, and community score of vehicles. The finding of experiment shows that, with advancement of power, connectivity, and community score of vehicles, data dissemination also enhanced. Furthermore; it guarantees that data will be shared efficiently with great reliability.</div>

CWEmd: A Lightweight Similarity Measurement for Resource-Constrained Vehicular Networks

Generating an accurate machine learning (ML) model is of great importance for the Internet of Vehicles (IoV). However, obtaining such a model is challenging due to the fact that sub-groups of in-network vehicles receive data from different resources. A worthwhile investment then would be identifying those groups before inferring models. Similarity metrics are widely used to distinguish different groups. However, the efficiency of most existing similarity measurements is at the cost of increased computational complexity and decreased accuracy, making them unsuitable for IoV’s stringent conditions. To address this issue, we propose a computationally efficient method to measure the similarity of different vehicles, where a simplified version of Earth Mover’s Distance (EMD) is adopted. This distance metric is then embedded into a distributed clustering algorithm to learn the global pattern for vehicular systems. Our algorithm’s overall performance is measured using an Asynchronous Message Delay Simulator. Compared to the best algorithm of the state-of-the-art, our proposed algorithm converges slightly slower (by less than 1%) but improves the clustering accuracy by as much as 20% with synthetic data. Additionally, real-world data collected from Vehicles validates the efficiency of our proposed algorithm.

Multi-mobile vehicles task offloading for vehicle-edge-cloud collaboration: A dependency-aware and deep reinforcement learning approach

The wide application of edge cloud computing in the Internet of Vehicles (IoV) provides lower latency, more efficient computing power, and more reliable data transmission services for vehicle applications. Realistic vehicle applications frequently consist of multiple tasks with dependencies. Efficiently and quickly scheduling individual tasks for multiple vehicle applications to reduce latency and energy consumption is challenging. Our proposed approach leverages Deep Reinforcement Learning (DRL) to develop a task scheduling strategy that ensures real-time and efficient operations. We maximize the utilization of available resources by harnessing the computational capabilities of vehicles, multiple MEC servers, and a cloud server. Specifically, we model the dependencies of tasks using a Directed Acyclic Graph (DAG) and design dynamically adjustable weights for delay and energy consumption. Transforming the task offloading problem in a vehicle-edge-cloud environment, which considers dependencies, into a Markov Decision Process (MDP) enables us to tackle it effectively. To obtain optimized offloading decisions quickly, we employ Double Deep Q-Network (DDQN) along with specially designed mobility management strategies. A penalty mechanism is introduced in DDQN to impose penalties when the vehicle application is delayed beyond its deadline. Simulation results show that the proposed scheme can significantly decrease the latency and energy consumption of multi-applications compared to the other three schemes and ensure the successful execution of tasks.

A distributed message authentication scheme with reputation mechanism for Internet of Vehicles

Real-time and interactive traffic information sharing systems are crucial in the Internet of Vehicles (IoV) as they enable vehicles to make informed decisions, thereby improving the efficiency of intelligent transportation systems (ITS). Message authentication ensures the accuracy, integrity, and tamper-resistance of information in IoV. Existing schemes aim to achieve time-critical message authentication. However, these schemes are time-consuming and cannot meet the real-time requirements of IoV. Additionally, there are issues with latency in data synchronization and data redundancy when vehicles traverse different domains. We propose an efficient, distributed, and resistant-to-malicious-attacks authentication scheme based on the reputation mechanism. Our scheme supports batch verification, enabling fast authentication. By leveraging the decentralized and ledger-synchronized features of blockchain, our distributed scheme reduces data redundancy. We also employ a reputation mechanism to ensure reliable reports in IoVs. We experimentally confirm that our scheme outperforms EADA (59.73%), RCoM (76.35%), MLGSDT (63.36%), and TRAJ (82.08%). This approach provides a secure and reliable solution for report authentication.

Simulation based QoS aware dynamic caching scheme for heterogeneous content requests in vehicular edge computing

The Internet of Vehicles (IoV) has witnessed a substantial surge in its expansion during recent years, with the integration of edge servers emerging as an increasingly prevalent practice to cater to content requests that demand a superior quality of service. Nonetheless, conventional cache policies often prove inadequate for IoV applications, primarily due to their incapacity to effectively handle a diverse array of content requests, the high-speed mobility of vehicles, and the inherent instability of network connections. Within the scope of this study, we commence by bifurcating various content requests into two distinct categories: latency-sensitive content request and bandwidth-sensitive content request. Subsequently, we establish a model to evaluate the service quality under the joint consideration of different types of content requests, the mobility of vehicles and storage capacity of edge nodes. Furthermore, we transform the quality-of-service (QoS) penalty function into a system reward function. This adaption enables us to propose an innovative edge cache scheme founded upon the Deep Deterministic Policy Gradient (DDPG) algorithm of Reinforcement Learning (RL) method, which empowers dynamic adjustments in response to the evolving IoV environment. To validate the effectiveness of our proposed approach, we harness the Simulation of Urban Mobility (SUMO) traffic simulation software and construct a traffic road scenario based on a specific part segment of Nanjing Beltway. A wide-ranging set of contrast experiments was performed to ensure the improved performance of the DDPG based deep reinforcement learning method. Simulation experimental results show that the proposed algorithm converges quickly and outperforms existing algorithms in terms of service quality-hit ratio for latency-sensitive content and transmission speed for bandwidth-sensitive content.

V2XCom: Lightweight and secure message dissemination scheme for Internet of vehicles

AbstractSmart cities provide a sustainable transport ecosystem to connect smart vehicles through sensors and networking units. Internet‐of‐vehicles (IoV) is vital in disseminating various messages, including road safety, exact location sharing, road accidents and blocks, collision warning, driver assistance, network congestion, or toll payment among vehicle‐to‐anything (V2X) units. Due to the mission‐critical nature of the IoV ecosystem, it requires reliable, lightweight, and real‐time communication for vehicle‐to‐vehicle (V2V) and V2X units. However, due to the availability of insecure wireless channels, an adversary can perform several security attacks such as replay, password guessing, masquerade, trace, message tampering, Man‐in‐the‐middle attack (MIMA), and plain‐text attacks in an IoV environment which may lead to potential disruptions. Motivated by the aforementioned facts, we propose a V2XCom, a lightweight and secure message dissemination scheme for the IoV network using low‐cost cryptographic SHA‐256, XoR operation, and concatenation. We performed security verification of V2XCom using the Scyther and AVISPA tools. Moreover, security proofs are provided for an informal security analysis of the proposed scheme. We have done a comparative analysis of a V2XCom with recent dissemination schemes in the IoV ecosystem concerning security features, communication latency, computational cost, and energy usage.

Maximizing the Connectivity of Network Slicing Enabled Internet of Vehicle With Differentiated Services

The advent of 5G opens up hitherto unimagined opportunities for delivering the much-anticipated Internet of Vehicles (IoV). There are two typical types of services in IoV, i.e., safety service and non-safety service. However, the existing IoV framework cannot efficiently support the differentiated IoV services. In addition, maximizing the number of accessed vehicles/users is another critical issue in IoV, especially in the dense urban area. To address these problems, in this paper, we utilize the emerging network slicing technology to support different services and employ Non-Orthogonal Multiple Access technology (NOMA) to help increase the connectivity. In particular, for the safety service, we take advantage of the finite blocklength capacity to correctly record the delay. Our goal is to maximize the connectivity of users by jointly considering the user association and their beamforming vectors, under the restrictions of limited physical resource. The problem is formulated as a Mixed-Integer Nonlinear Programming problem (MINLP). To tackle the intractable MINLP, we propose a two-stage scheme. Firstly, we exploit efficient approaches to solve the beamforming problem, i.e., successive convex approximation, semidefinite relaxation and second-order cone programming. Secondly, we propose a low-complexity Greedy User Association (GUA) algorithm to solve the user association problem. Finally, comprehensive simulations verify that our proposed GUA algorithm is close to global optimal solution and outperforms the benchmark schemes.

Fine-Grained Data Sharing With Enhanced Privacy Protection and Dynamic Users Group Service for the IoV

The Internet of Vehicles (IoV) is expected to play a revolutionary role in improving users’ driving experience and urban traffic governance. By widely absorbing emerging technologies including cloud computing, the future IoV evolution is leading towards providing more flexible and diversified data services. However, the publicly accessible IoV environment arouses the user’s concerns about the leakage of data and personal privacy. Despite some cryptographic solutions have been proposed, they still raise challenges on privacy, efficiency and usability. To cope with these challenges, this paper first presents an efficient scheme PH-ABE-DS, which attains the full policy hiding by implementing the access control with the inner product. Besides, we design an efficient indirect revocation mechanism, to enable the cloud and users to update the ciphertext and user secret key with slight storage and computational overheads. On this basis, we then present the EA-PH-ABE-DS scheme, by resorting to edge computing, it further reduces the overheads of resource-constrained devices. We design a deployment model for EA-PH-ABE-DS in IoV to discuss its usability. Rigorous security proof and security properties analysis show that our proposal is secure and reliable. Finally, through detailed comparisons on theoretical and experimental, both our two schemes show their superiority over the latest related works in terms of functionality and performance. The simulation evaluates and demonstrates the practicality of our solutions in practical IoT scenarios.

A Secure Intelligent System for Internet of Vehicles: Case Study on Traffic Forecasting

Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV models that are based on deep learning is that the methods used do not tune hyperparameters efficiently. In this paper, a new system known as Secure and Intelligent System for the Internet of Vehicles (SISIV) is developed. A deep learning architecture based on graph convolutional networks and an attention mechanism are implemented. In addition, blockchain technology is used to protect data transmission between nodes in the IoV system. Moreover, the hyperparameters of the generated deep learning model are intelligently selected using a branch-and-bound technique. To validate SISIV, experiments were conducted on four networked vehicle databases dealing with prediction problems. In terms of forecasting rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 90%), F-measure ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 80%), and attack detection ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 75%), the results clearly show the superiority of SISIV over baseline systems. Moreover, compared to state-of-the-art solutions based on traffic prediction, SISIV enables efficient and reliable prediction of traffic flow in an IoV context.

An Adversarial Machine Learning-Based Fast Detection Method for Denial of Service-Oriented Cyber Attacks in Internet of Vehicles

Denial of Service (DoS)-Oriented cyber attack has been a major threat for physical security in many kinds of network media, including the Internet of Vehicles (IoV). This paper focuses on the scenario of IoV, and proposes a machine learning-based fast detection method for adversarial neural network-based fast detection method for DoS-oriented cyber attacks. First, by analyzing the implementation principles and attack characteristics of three attack types, three aspects of statistical features are extracted: maximum matching packet growth rate, source address entropy value, and flow table similarity. Then, they are used as the input features to establish an adversarial machine learning-based DoS cyber attack detection method. On this basis, the field features of six stream rules are extracted, and two DoS cyber attack detection methods via machine learning are formulated. The proposals are able to detect the low-rate DoS-based cyber attacks against the data layer. The experimental results show that the proposed DoS attack detection method based on machine learning can effectively detect three DoS attacks under IoV, and these two algorithms have higher detection rates when compared with other algorithms.

DAG-based swarm learning: a secure asynchronous learning framework for Internet of Vehicles

To provide diversified services in the intelligent transportation systems, smart vehicles will generate unprecedented amounts of data every day. Due to data security and user privacy issues, Federated Learning (FL) is considered a potential solution to ensure privacy-preserving in data sharing. However, there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles (IoV), such as unreliable communications and malicious attacks. In this paper, we propose a Directed Acyclic Graph (DAG) based Swarm Learning (DSL), which integrates edge computing, FL, and blockchain technologies to provide secure data sharing and model training in IoVs. To deal with the high mobility of vehicles, the dynamic vehicle association algorithm is introduced, which could optimize the connections between vehicles and road side units to improve the training efficiency. Moreover, to enhance the anti-attack property of the DSL algorithm, a malicious attack detection method is adopted, which could recognize malicious vehicles by the site confirmation rate. Furthermore, an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors. Finally, simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy, convergence rates and security compared with existing algorithms.

Dynamic Edge Computation Offloading for Internet of Vehicles With Deep Reinforcement Learning

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.