Mobility Patterns Of Vehicles Research Articles

Application of Fuzzy Computing for VANET Routing

In the realm of vehicular ad hoc networks (VANETs), routing plays a pivotal role in ensuring efficient communication among vehicles and infrastructure. Traditional routing protocols often struggle to adapt to the dynamic and uncertain environment of VANETs, characterized by rapidly changing network topologies and unpredictable traffic conditions. In any case, the without a doubt solid and stunning nature of vehicular conditions presents superb difficulties for controlling shows, including unusual straightforwardness, changing association geologies, and dynamic traffic conditions [1, 2]. By incorporating fuzzy logic into routing algorithms, vehicles can make more informed decisions regarding route selection, considering factors such as traffic congestion, road conditions, and vehicle mobility patterns. This paper provides an overview of the key concepts of fuzzy computing and discusses its potential applications in VANET routing. Furthermore, it explores existing research efforts in this area, highlighting the benefits and challenges associated with fuzzy based routing approaches. Through a comprehensive review of the literature, this paper identifies current trends and future directions for leveraging fuzzy computing in VANET routing, aiming to improve the reliability, scalability, and efficiency of communication in vehicular networks.

AI-Enabled Spatial-Temporal Mobility Awareness Service Migration for Connected Vehicles

In the future 6 G intelligent transportation system, the edge server will bring great convenience to the timely computing service for connected vehicles. To guarantee the quality of service, the time-critical services need to be migrated according to the future location of the vehicle. However, predicting vehicle mobility is challenging due to the time-varying of road traffic and the complex mobility patterns of vehicles. To address this issue, a spatial-temporal awareness proactive service migration strategy is proposed in this paper. First, a spatial-temporal neural network is designed to obtain accurate mobility by using gated recurrent units and graph convolutional layers extracting features from spatial road traffic and multi-time scales driving data. Then a proactive migration method is proposed to guarantee the reliability of services and reduce energy consumption. Considering the reliability of services and the real-time workload of servers, the migration problem is modeled as a multi-objective optimization problem, and the Lyapunov optimization method is utilized to obtain utility-optimal migration decisions. Extensive simulations based on real-world datasets are performed to validate the performance of the proposed method. The results show that the proposed method achieved 6% higher prediction accuracy, 10% lower dropping rate, and 10% lower energy consumption compared to state-of-the-art methods.

A study on the impact of mobility on caching in non-standalone 5G vehicular networks

Responding to numerous requests of mobile users and maintaining the quality of service is one of the important challenges in designing and implementing future vehicular networks. One of the solutions to reduce network traffic is caching on the radio access network and close to users. In this paper, we investigate the impact of the mobility of users on caching performance and propose novel mobility-aware schemes to turn the mobility challenge into an opportunity to reduce latency. We study vehicular networks with Non-Standalone (NSA) 5G deployment in which a mobile user (MU) can access the 5G Next Generation NodeB (gNB) stations with caching capability in addition to accessing the 4G Evolved NodeB (eNB) stations. We provide an analytical framework and present a closed-form expression of the average end-to-end data transfer time in cache-enabled vehicular networks with NSA 5G deployment, considering users' mobility and the stochastic arrival of user requests. We study various caching schemes and propose novel mobility-aware caching schemes. Using the map-based mobility patterns of various vehicles including cars and buses and also pedestrians, it is shown that the proposed mobility-aware schemes improve the performance of mobile networks in terms of latency.

A Dynamic Task Scheduling Strategy for Multi-Access Edge Computing in IRS-Aided Vehicular Networks

Multi-access Edge Computing (MEC) has played an important role in realizing intelligent beyond 5G (B5G) vehicular networks. The computation tasks of intelligent applications can be offloaded to and processed by near-end-user MEC servers to meet strict latency requirements. However, the latency of provided services is dependent on MEC processor scheduling and millimeter wave (mmWave) transmission conditions for the urban B5G vehicular networks. To alleviate the mmWave signal attenuation caused by buildings, Intelligent Reflecting Surface (IRS) has been regarded as efficient and prospective infrastructure. In this article, we study the IRS-aided MEC-served vehicular networks and analyze the relationship between computation resource allocation and offloading policy at an intersection. Considering the vehicle mobility patterns, transmission conditions, and task sizes, we optimize the task scheduling by improving the allocation of limited processors and IRS resource. Moreover, the mutual interference among concurrent transmissions is taken into account. Assuming the moving directions available, a dynamic task scheduling algorithm is proposed which considers both the communications and computations. The simulation results illustrate that our proposal outperforms benchmark methods in terms of task offloading rate, computing rate, and finish rate for the IRS-aided MEC-served vehicular networks.

Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target’s home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

A Heuristic Distributed Scheme to Detect Falsification of Mobility Patterns in Internet of Vehicles

Autonomous vehicles in the Internet of Vehicles (IoV) have the ability to generate their mobility pattern in advance and share it with other vehicles or a central location. This information enables traffic systems to be aware of future traffic flow. However, the current classical traffic systems barely consider the spatial dependency of traffic data, and more advanced systems suffer from storage limitations. These problems will be exacerbated by the falsification of traffic data through corresponding data attacks, such as position forging attacks. This, in turn, negatively impacts the performance of traffic management systems. This article proposes a heuristic distributed scheme (HIDE) to validate the mobility pattern of vehicles by penalizing or rewarding vehicles based on the contacts’ conformation. HIDE enables every vehicle to consider the claimed mobility pattern of every neighboring vehicle that is exchanged, and assigns a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">penalty</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reward</i> to each received mobility pattern and share with cloudlets via roadside units (RSUs). These calculations are based on an efficient time-homogeneous semi-Markov process (THSMP) to predict the likelihood of the accuracy of mobility patterns. Cloudlets calculate a weight factor to determine if a vehicle is malicious. The validation results from THSMP reveal that a high correlation is achieved between the theoretical model and simulation. Also, the results show that the model fairly identifies the malicious vehicles and assigns a low weight of impact on them compared to normal vehicles.

Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation

Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks (VANETs) for smart transportation that results from dynamic topology, limited resources and non-centralized architecture. The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation. To design a robust clustering algorithm, careful attention must be paid to components like mobility models and performance objectives. A clustering algorithm may not perform well with every mobility pattern. Therefore, we propose a supervisory protocol (SP) that observes the mobility pattern of vehicles and identifies the realistic Mobility model through microscopic features. An analytical model can be used to determine an efficient clustering algorithm for a specific mobility model (MM). SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations. The simulation has performed in three parts that is the central part simulation for setting up the clustering environment, In the second part the clustering algorithms are tested for efficiency in a constrained atmosphere for some time and the third part represents the proposed scheme. The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count, re-affiliation rate, control overhead and cluster lifetime.

A Smart Cloud Service Management Algorithm for Vehicular Clouds

Vehicular clouds (VCs) have become a promising research area due to its on-demand solutions, resource pooling, unified services, autonomous cloud formation and transformational management. It makes use of the underutilized resources of vehicles on the parking lot, roadways, driveways and streets, and creates the infrastructure to support various services offered by the cloud service provider (CSP) by deploying virtual machines (VMs). However, these vehicles can leave the coverage/grid of VC due to its mobility and change in the environment. Therefore, the hosted VMs on those vehicles can be transferred to other potential vehicles (i.e., migration) in order to avoid disruption of services. These services can be viewed as user requests (URs) submitted to the CSP by cloud users. Here, the challenging tasks are to map the URs to the VMs (or vehicles) and identify the potential vehicles for migration, and they need immediate attention. In this paper, we propose a smart cloud service management (SCSM) algorithm for VCs and address the above challenges. This algorithm consists of three phases, namely assignment of vehicles to grids, URs to grids and URs to vehicles by considering the mobility pattern of vehicles. The performance of SCSM is assessed using three traffic congestion scenarios and thirty-six instances of four datasets, and compared with round-robin (RR) and deficit weighted RR (DWRR) using seven performance metrics. The comparison results show that SCSM achieves 58% and 57% (33% and 33%) better than RR and DWRR in makespan (number of migrations) and other performance metrics.

Quantifying the Spatio-Temporal Potential of Drive-by Sensing in Smart Cities

ABSTRACT Recently, portable sensors, with high accuracy and embedded communication technologies, have become available and affordable. By deploying such sensors on various urban vehicles that routinely navigate through city streets, vehicles can form a dynamic network for comprehensively and efficiently monitoring the urban environment. This drive-by sensing approach benefits also from the lower costs of sensor deployment and maintenance compared to stationary sensor networks. However, the data sampling frequency and spatial granularity of measurements are constrained by factors such as topology of the underlying street network and mobility pattern of sensor-equipped vehicles. In this paper we investigate the effect of street network topology on the quality of data captured through drive-by sensing. To this end, we first study the temporal aspects of drive-by sensing and present a quantitative method for comparing various street networks. Then, we consider the spatial aspects of drive-by sensing by defining a sensing-potential indicator for urban areas based on the geometrical properties of the street networks. This indicator is then combined with vehicle mobility patterns derived to measure the sensing potential of routes and cycles. In this context, we define the novel concept of Sensogram for describing the spatial sensing potential of network cycles using dedicated vehicles.

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.

Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services

Observing the headway in vehicular industry, new applications are developed demanding more resources. For instance, real-time vehicular applications require fast processing of the vast amount of generated data by vehicles in order to maintain service availability and reachability while driving. Fog devices are capable of bringing cloud intelligence near the edge, making them a suitable candidate to process vehicular requests. However, their location, processing power, and technology used to host and update services affect their availability and performance while considering the mobility patterns of vehicles. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Kubeadm Clustering, Docker Containerization, and micro-services technologies. In this context, we propose an efficient resource and context aware approach for deploying containerized micro-services on on-demand fogs called Vehicular-OBUs-As-On-Demand-Fogs. Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. An Evolutionary Memetic Algorithm is elaborated to solve our vehicular container placement problem. Experiments and simulations demonstrate the relevance and efficiency of our approach compared to other recent techniques in the literature.

Cooperative Location Privacy in Vehicular Networks: Why Simple Mix Zones are Not Enough

Vehicular communications disclose rich information about the vehicles and their whereabouts. Pseudonymous authentication secures communication while enhancing user privacy. To enhance location privacy, cryptographic mix zones were proposed to facilitate vehicles covertly transition to new ephemeral credentials. The resilience to (syntactic and semantic) pseudonym linking (attacks) highly depends on the geometry of the mix zones, mobility patterns, vehicle density, and arrival rates. We introduce a tracking algorithm for linking pseudonyms before and after a cryptographically protected mix zone. Our experimental results show that an eavesdropper, leveraging standardized vehicular communication messages and road layout, could successfully link ≈73% of pseudonyms during nonrush hours and ≈62% of pseudonyms during rush hours after vehicles change their pseudonyms in a mix zone. To mitigate such inference attacks, we present a novel cooperative mix zone scheme that enhances user privacy regardless of the vehicle mobility patterns, vehicle density, and arrival rate to the mix zone. A subset of vehicles, termed relaying vehicles, is selected to be responsible for emulating nonexisting vehicles. Such vehicles cooperatively disseminate decoy traffic without affecting safety-critical operations: with 50% of vehicles as relaying vehicles, the probability of linking pseudonyms (for the entire interval) drops from ≈68% to ≈18%. On average, this imposes 28 ms extra computation overhead, per second, on the roadside unit (RSU) and 4.67 ms extra computation overhead, per second, on the (relaying) vehicle side; it also introduces 1.46 kB/s extra communication overhead by (relaying) vehicles and 45 kB/s by RSUs for the dissemination of decoy traffic. Thus, user privacy is enhanced at the cost of low computation and communication overheads.

VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. We evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. Compared with state-of-the-art benchmark mobility models, the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

Service Migration in Fog Computing Enabled Cellular Networks to Support Real-Time Vehicular Communications

Driven by the increasing number of connected vehicles and related services, powerful communication and computation capabilities are needed for vehicular communications, especially for real-time and safety-related applications. A cellular network consists of radio access technologies, including the current long-term evolution (LTE), the LTE advanced, and the forthcoming 5th generation mobile communication systems. It covers large areas and has the ability to provide high data rate and low latency communication services to mobile users. It is considered the most promising access technology to support real-time vehicular communications. Meanwhile, fog is an emerging architecture for computing, storage, and networking, in which fog nodes can be deployed at base stations to deliver cloud services close to vehicular users. In fog computing-enabled cellular networks, mobility is one of the most critical challenges for vehicular communications to maintain the service continuity and to satisfy the stringent service requirements, especially when the computing and storage resources are limited at the fog nodes. Service migration, relocating services from one fog server to another in a dynamic manner, has been proposed as an effective solution to the mobility problem. To support service migration, both computation and communication techniques need to be considered. Given the importance of protocol design to support the mobility of the vehicles and maintain high network performance, in this paper, we investigate the service migration in the fog computing-enabled cellular networks. We propose a quality-of-service aware scheme based on the existing handover procedures to support the real-time vehicular services. A case study based on a realistic vehicle mobility pattern for Luxembourg scenario is carried out, where the proposed scheme, as well as the benchmarks, are compared by analyzing latency and reliability as well as migration cost.

Performance analysis of content discovery for ad-hoc tactile networks

Tactile Internet evolves communications to encompass sensory information such as smell and haptic sensations combining ultra-low latency with extremely high availability, reliability, and security. Tactile Internet is realized through underpinning technologies such as Multi-access Edge and Fog computing which facilitate decentralized infrastructures and machine to machine (M2M) communications. Mobile ad hoc networks (MANETs) form the foundation layer of such infrastructures, enabling direct communication between autonomous and decentralized devices such as sensors and vehicles. Among other applications, autonomous ad hoc vehicular networks (VANETs) and vehicle to vehicle (V2V) communications require efficient content discovery and quality of data transfer. The mobility patterns of vehicles within this communication model could effect the quality of data exchanged between devices in a tactile network. Several mobility models exist describing mobility patterns of mobile users in MANETs. In this paper, we present a first performance study to evaluate the impact of different mobility models on content discovery techniques for tactile Internet comprising of fast-moving vehicles and devices. This study combines direct and derived mobility metrics evaluating impact on content discovery and content dissemination using NS-3. Our simulation results indicate that unstructured techniques may not scale well within a tactile network of fast moving vehicles while maintaining low latency and could suffer from performance degradation in a saturated environment. Furthermore, simulation results also demonstrate the resilience of the unstructured content discovery protocol in mobility scenarios with proactive routing and diverse behavior.

CoSense

The real-time vehicle sensing at urban scale is essential to various urban services. To date, most existing approaches rely on static infrastructures (e.g., traffic cameras) or mobile services (e.g., smartphone apps). However, these approaches are often inadequate for urban scale vehicle sensing at the individual level because of their static natures or low penetration rates. In this paper, we design a sensing system called coSense to utilize commercial vehicular fleets (e.g., taxis, buses, and trucks) for real-time vehicle sensing at urban scale, given (i) the availability of well-equipped commercial fleets sensing other vehicles by onboard cameras or peer-to-peer communication, and (ii) an increasing trend of connected vehicles and autonomous vehicles with periodical status broadcasts for safety applications. Compared to existing solutions based on cameras and smartphones, the key features of coSense are in its high penetration rates and transparent sensing for participating drivers. The key technical challenge we addressed is how to recover spatiotemporal sensing gaps by considering various mobility patterns of commercial vehicles with deep learning. We evaluate coSense with a preliminary road test and a large-scale trace-driven evaluation based on vehicular fleets in the Chinese city Shenzhen, including 14 thousand taxis, 13 thousand buses, 13 thousand trucks, and 10 thousand regular vehicles. We compare coSense to infrastructure and cellphone-based approaches, and the results show that we increase the sensing accuracy by 10.1% and 16.6% on average.

Revealing Privacy Vulnerabilities of Anonymous Trajectories

The proliferation of various mobile devices equipped with GPS positioning modules makes the collection of trajectories more easier than ever before, and more and more trajectory datasets have been available for business applications or academic researches. Normally, published trajectories are often anonymized by replacing real identities of mobile objects with pseudonyms (e.g., random identifiers); however, privacy leaks can hardly be prevented. In this paper, we introduce a novel paradigm of de-anonymization attack re-identifying trajectories of victims from anonymous trajectory datasets. Different from existing attacks, no background knowledge or side channel information about the target dataset is required. Instead, we claim that, for each moving object, there exist some mobility patterns that reflect the preference or usual behavior of the object, and will not change dramatically over a period of time. As long as those relatively stable patterns can be extracted from trajectories and be utilized as quasi-identifiers, trajectories can be linked to anonymous historical ones. To implement such kind of de-anonymization attacks, an adversary only needs to collect a few trajectory segments of a victim, the durations of which do not necessarily overlap with that of trajectories in the target dataset (in simple terms, those trajectory segments are not necessary sub-trajectories included in the target dataset). Since the movements of victims in public areas could be observed openly, an adversary can obtain traces or locations about the victims either by direct monitoring them (e.g., tracking) or from third parties (e.g., social-networks). Then, the adversary extracts useful patterns from both the historical trajectories in the accessible dataset and newly obtained trajectory segments of victims, the historical trajectory with most similar patterns to that of a victim is considered as belonging to the victim. In order to demonstrate the feasibility of such attacks, we conduct extensive trace-driven simulations. We extract road segment preferences and stop of interests from trajectories of vehicles, and construct feature vectors (mobility patterns) of vehicles according to them, used for trajectory comparisons. Simulation results show that the adversary could re-identify anonymous trajectories effectively.

City Scanner: Building and Scheduling a Mobile Sensing Platform for Smart City Services

A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. In this paper, drive-by approaches are discussed as a form of mobile sensing, that offer a number of advantages over more traditional sensing approaches. It is shown that the physical properties of the urban environment that can be captured using drive-by sensing include ambient fluid, electromagnetic, urban envelope, photonic, and acoustic properties, which comprise the FEELS classification. In addition, the spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. The mobility patterns of sensor-hosting vehicles play a major role in drive-by sensing. Vehicles with scheduled trajectories, e.g., buses, and those with less predictable mobility patterns, e.g., taxis, are investigated for sensing efficacy in terms of spatial and temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass data acquisition on a multitude of city features. The City Scanner framework follows a centralized IoT regime to generate a near real-time visualization of sensed data. The sensing platform was mounted on top of garbage trucks and collected drive-by data for eight months in Cambridge, MA, USA. Acquired data were streamed to the cloud for processing and subsequent analyses. Based on a real-world application, we discuss and show the potential of using drive-by approaches to collect environmental data in urban areas using a variety of nondedicated land vehicles to optimize data collection in terms of spatiotemporal coverage.

Performance evaluation and comparative study of main VDTN routing protocols under small- and large-scale scenarios

This paper presents a performance evaluation through simulations and a comparative study of main routing protocols dedicated to Vehicular Delay-Tolerant Networks. The assessment is conducted under small- and large-scale scenarios with realistic vehicle mobility patterns as defined in the TAPAS Cologne simulation scenario. Through the literature, several evaluations have been conducted on routing for Vehicular Networks, but with an abstraction or a simplification regarding the delay tolerant aspect. Furthermore, considered scenarios were relatively small and too idealistic compared to the real-world environment and its multiples challenges. Moreover, to the best of our knowledge, this is the first extensive study that compares, in the same realistic simulation environment, the main flag-carriers of various categories of VDTN routing protocols, namely Epidemic, Direct Delivery, Prophet and GeoSpray protocols. Simulation results reveal better performance for the geographical approach advocated by GeoSpray compared to the predictive one of Prophet, under all considered scenarios. Moreover, they highlight the possibility for a minimalist and naive protocol such as Direct Delivery to perform well, under specific network conditions, as when considering an anycast communication scheme. Finally, deeper analysis was undertaken on both GeoSpray and Prophet. The studies reveal the potentialities to increase the performances of GeoSpray to some extent and highlight the difficulties of adapting Prophet settings for optimal performance in realistic scenarios.

MPC: A RSUs deployment strategy for VANET

SummaryA Vehicle AdHoc Network is mainly composed of mobile vehicles and fixed Road Site Units (RSUs). The latter is usually very expensive to deploy and has a crucial role in maintaining the network connectivity. Therefore, the design of an efficient RSU deployment strategy that enables a high coverage ratio and a lower deployment cost has been of paramount importance. In this respect, we introduce in this paper a new spatiotemporal coverage strategy for nonsafety Vehicle AdHoc Network applications like driving assistance and business promotion, called Minimal Mobility Patterns Coverage (MPC). The main thrust of MPC is to (1) depict the mobility patterns of moving vehicles from their trace files and then (2) compute the adequate RSU locations in order to cover the extracted mobility patterns by the minimal possible number of RSUs. To this end, we firstly provide a new method to depict the mobility patterns of vehicles by mining the correlations between the kept track connections of vehicle trajectories versus crossed junctions. Secondly, we introduce a new way to compute the adequate RSU locations through the instantiation of the well‐known problem of extracting minimal transversals of a hypergraph. Experimental results show that our RSUs deployment strategy performs better than baseline strategies.