Vehicular Mobility Research Articles

Large-volume data dissemination for cellular-assisted automated driving with edge intelligence

Automated driving emerges as a potential technology for safe, convenient, and efficient transportation systems. In order to assist autonomous vehicles with large-volume data dissemination such as high definition (HD) map, cellular network becomes more inevitable to provide vehicles with the ability of accessing Internet. However, due to the bottleneck of wireless bandwidth and the high mobility of vehicles, efficient data dissemination assisted by cellular network is a challenging task. In this work, we investigate the large-volume data dissemination problem for cellular-assisted automated driving within given delay limit. Firstly, a data dissemination scheme is designed that transmits data from the base stations to the target vehicle via a collection of relay vehicles. Secondly, a novel user satisfaction model is derived by considering the vehicular mobility patterns and erasure coding, and the data dissemination is formulated into an optimization problem. Then, to solve this NP-hard problem, a low-complexity dynamic programming based data dissemination solution with edge intelligence is proposed. Finally, by carrying out extensive simulation, the effectiveness of proposed data dissemination scheme for cellular-assisted automated driving is demonstrated, in comparison with the optimal solution based on CPLEX as well as other conventional schemes.

An Efficient Mobility-Oriented Retrieval Protocol for Computation Offloading in Vehicular Edge Multi-Access Network

Computation offloading for vehicular edge computing (VEC) architecture has gained increasing attention, with the emergence of mobile and vehicular applications with high-computing and low-latency demands, such as Intelligent Transportation Systems and IoT-based applications. However, existing challenges need to be addressed for VEC’s resources to be used in an efficient manner. The fundamental challenges arise from high vehicular mobility and intermittent or lack of connectivity. In this paper, we model the computation offloading task in VEC, with the goal of assessing the ability to simulate the elements that contribute to enhancing its performance in VEC. In addition, we propose the Mobility Prediction Retrieval (MPR) data retrieval protocol, which allows VEC to efficiently retrieve the output processed data of the offloaded application by using both vehicles and road side units as communication nodes. The developed protocol uses geo-location information of the network infrastructure and the users to accomplish an efficient data retrieval in a Vehicular Edge Computing environment. Finally, the experiments performed show that the proposed protocol to achieves a more reliable data retrieval with lower communication delay when compared to related techniques.

Handoff Schemes in Mobile Environments

Vehicular ad-hoc networks are one of the most popular applications of Ad-hoc networks, where networks are formed without any sort of physical connecting medium and can be formed whenever required. It is an area in networks that has enjoyed a considerable amount of attention for quite some time. Due to the highly mobile environment where these networks find their usability, it can be understood that there are a lot of problems with respect to maintaining the communication links between the moving vehicular nodes and the static infrastructures which act as the access points (AP) for these moving vehicular mobile nodes (MN). The coverage area of each AP is limited and as such, the connections need to be re-established time and again between the MNs and the closest accessible AP. Handoff is the process involved here, which deals with selecting the optimal APs as well as the best network available for data transmission. In this article, the authors compare various handoff methods and categorize them based on the different approaches they follow.

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.

A New Affinity Propagation Clustering Algorithm for V2V-Supported VANETs

Clustering is an efficient method for improving the communication performance of Vehicular Ad hoc NETworks (VANETs) that adopt Vehicle to Vehicle (V2V) communications. However, how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem. In this paper, we first reconstruct the similarity function of the Affinity Propagation (AP) clustering algorithm by introducing communication-related parameters, so the vehicles with low relative mobility and good communication performance can easily be selected as cluster heads. Then, by formally defining three scaling functions, a weighted mechanism is designed to quantitatively assess the effect on the cluster stability when a vehicle joins it. Base on them, from the perspective of global balance, a new AP clustering algorithm for the whole clustering process is proposed. To ensure the validity of simulations, we use the vehicular mobility data generated on the realistic map of Cologne, Germany, and perform a series of simulations for eleven metrics commonly adopted in similar works. The results show that our proposed algorithm performs better than other algorithms in terms of the cluster stability, and it also effectively improves throughput and reduces packet loss rate of VANETs over the classical APROVE algorithm and the NMDP-APC algorithm.

An adaptive resource placement policy by optimizing live VM migration for ITS applications in vehicular cloud network

AbstractVehicular cloud computing (VCC) is a promising approach that uses cloud computing techniques in vehicular environment to execute smart applications. Vehicular environment is characterized by high mobility. This requires the services executed by a vehicular resource in a vehicular cloud to be migrated before it leaves a network. Migration is also required when a service‐requesting vehicle has to leave a network before its request is executed. Moreover, to balance the network load, virtual machine (VM) migration is desired as, at times, a single physical host may become overloaded. In this work, we propose an adaptive resource placement policy by optimizing live VM migration process in vehicular cloud network. The Pareto optimal mapping of migrating VMs to a physical host is carried out using a hybrid optimized algorithm, which is the combination of particle swarm optimization and genetic algorithm. The optimization is done by maximizing a fitness function that is developed by considering significant quality‐of‐service (QoS) parameters, such as network latency, migration delay, power consumption, and vehicular mobility. These QoS parameters are mathematically formulated in view of a vehicular network. The proposed algorithm reduces migration latency, transmission latency, and service delay. It also distributes the load among the available physical hosts within a cloud system, thus reducing the average wait time for each cloud user. Simulation results exhibit significant decrease in waiting time, service delay, and migration delay. It is also shown that the proposed algorithm reduces total power consumption of the system.

Traffic-Centric Mesoscopic Analysis of Connectivity in VANETs

Abstract Vehicular ad hoc networks (VANETs) have emerged as an appropriate class of information propagation technology promising to link us even while moving at high speeds. In VANETs, a piece of information propagates through consecutive connections. In the most previous vehicular connectivity analysis, the provided probability density function of intervehicle distance throughout the wide variety of steady-state traffic flow conditions is surprisingly invariant. But, using a constant assumption, generates approximate communication results, prevents us from improving the performance of the current solutions and impedes designing the new applications on VANETs. Hence, in this paper, a mesoscopic vehicular mobility model in a multilane highway with a steady-state traffic flow condition is adopted. To model a traffic-centric distribution for the spatial per-hop progress and the expected spatial per-hop progress, different intervehicle distance distributions are utilized. Moreover, the expected number of hops, distribution of the number of successful multihop forwarding, the expected time delay and the expected connectivity distance are mathematically investigated. Finally, to model the distribution of the connectivity distances, a set of simplistic closed-form traffic-centric equations is proposed. The accuracy of the proposed model is confirmed using an event-based network simulator as well as a road traffic simulator.

Toward a sustainable mobility: A solar vehicle for a new quality of life

The vehicular mobility causes 15% of greenhouse gases emission: one million tons of carbon anhydrite per hour. In addition, it produces CO, NOx, fine powders, carcinogenic and mutagenic elements: these substances will disappear in the presence of solar vehicles. And solar mobility would also mitigate indirect effects: fuel used to transport fuel, energy for the distillation of hydrocarbons, gas leaks, even fracking, explosions, rivers and oceans. In contrast, electric and hybrid vehicles do not allow this improvement in the quality of life. In almost all modern countries, the energy mix is strongly unbalanced towards fossil fuels: massive electrification would not make mobility sustainable, but rather risks worsening its effect on the environment by shifting the problem of emissions from cities to power plants. The Sun, indeed, can guarantee long-term sustainable mobility: for every circulating solar vehicle CO2 production is really zero. From July to today, our solar racing car has travelled 3000 km, avoiding to emit half a ton of CO2: reporting these data to a conventional use, each solar vehicle would avoid the release of 1.5 tons of CO2 per year: like planting 10 large trees for each month in our garden. This study describes how to transform a solar super-car into an ordinary vehicle for urban and everyday mobility.

Fast Broadcast Technique for Real Time Applications with WAVE 1609.4 Protocol

As the technology improves, the attention on vehicular communication is increasing with a wide range of applications accessible to the passengers and also the increasing number of cars on the roads leads to delay in accessing the various applications. The safety and entertainment applications need fast transmissions of messages and due to the specific characteristics of vehicular mobility (such as short connection times of oncoming vehicles, high relative velocities and possibly unstable connections) the inter-vehicular communication is affected. Considering this, we propose a system with faster broadcast technique and with the use of IEEE WAVE 1609.4 protocol and DSRC with an aim to reduce number of hops, transmission time. The simulation has been carried out in NS 2.34.

Potencial impacto energético de los vehículos eléctricos en la red de distribución del Distrito Nacional, República Dominicana

El mundo se encuentra en una transición energética, lo que trae como consecuencia el surgimiento de tecnologías para contrarrestar la dependencia de los derivados de petróleo en la movilidad vehicular. En respuesta a esto, se presentan los vehículos eléctricos como una alternativa ecológica para el desplazamiento cotidiano de los seres humanos. Se estableció como objetivo determinar el impacto de conectar y cargar vehículos eléctricos en la redde distribución, para ello se utilizó la herramienta de simulación Power System Simulator (PSS/ADEPT) y hojas de cálculos, en donde se realizaron corridas de flujos de carga a diferentes escenarios de penetración vehicular, evaluando en cada caso parámetros eléctricos como cargabilidad y caída de tensión en los conductores troncales y transformadores de los circuitos seleccionados. Los resultados arrojados en las simulaciones permiten afirmar que la infraestructura actual de los circuitos seleccionados no está apta para soportar una penetración considerable de vehículos eléctricos, donde en circuitos como el EMBA-105 a un 10 % de penetración vehicular eléctrica las pérdidas permisibles fueron superadas. Con estos resultados se pudo detectar cuáles circuitos de la red de distribución eléctrica seleccionados pudieran colapsar con la entrada masiva de la movilidad eléctrica.

Modeling and Analysis of a Shared Edge Caching System for Connected Cars and Industrial IoT-Based Applications

The next revolution of industrial applications, known as smart industry or Industry 4.0, will rely on Internet of Things (IoT) to automate the monitoring, inspection, and control of industrial equipment and processes. In Industry 4.0, efficient content delivery is one of the fundamental challenges to be addressed. Nowadays, the promising solution for content delivery in smart industrial applications is the use of hierarchical caching systems at the network edge (5G small cells). This approach reduces the delay for content delivery and helps improve the performance of smart industrial applications. However, the caching management is a challenging and complex task, especially in those scenarios of shared storage resources on edge devices to support multiple concurrent applications (e.g., industrial, mobile users, and connected cars applications). In this article, we study the performance of a shared edge caching system for content delivery in smart industry and connected cars applications. To do so, we propose a mathematical framework to model the performance of a hierarchical shared edge caching system. The proposed mathematical framework considers the distinct content catalogs of the different applications (e.g., industrial and connected cars applications) and content request characteristics from industrial IoT devices and vehicles. Numerical results show that the performance of the shared edge caching system is sensitive to vehicular mobility (i.e., vehicular speed).

Adaptive Configuration of Mobile Roadside Units for the Cost-Effective Vehicular Communication Infrastructure

In this paper, we target to figure out an adaptive and efficient mechanism that deploys roadside units (RSUs), which are the major components of the vehicle-to-infrastructure (V2I) communication, so that vehicles on the road can have satisfactory connectivity to the vehicular communication infrastructure in a cost-effective way. Only with the conventional fixed RSUs (fRSUs), the coverage requirement of vehicles from the perspective of V2I communications cannot be easily met because of high vehicular mobility compared to the static nature of fRSUs and the high deployment and operational expenditure of fRSUs. Recently, mobile RSUs (mRSUs) mounted on public and commercial vehicles like buses are considered as the substitutes or the supplements of fRSUs. The research on mRSUs is in its early stage and mostly focuses on the deployment of mRSUs from a static viewpoint. In this paper, we consider the environment with densely deployed mRSUs, such as the city environment, thanks to low cost, in which multiple active mRSUs generate lots of control messages to form the mRSU backbone network. To overcome this inefficiency, we propose a mechanism in which each mRSU adaptively and effectively determines its own state, active or inactive, according to the states of its neighboring mRSUs and vehicles. For the NP-hardness proof, we formulate the problem as a 0-1 integer linear programming problem. We evaluate the performance of our mechanism in terms of the ratio of vehicles covered by active mRSUs and the control message overhead compared with the case of nonadaptive mRSU configuration (i.e., the case of all mRSU being active).

On the performance of social-based and location-aware forwarding strategies in urban vehicular networks

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work evaluates the performance of several location-based and social-aware forwarding schemes through emulations and in a real scenario. Gateway Location Awareness (GLA), a location-aware ranking classification, makes use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns. Aging Social-Aware Ranking (ASAR) exploits the social behavior of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles. To merge both location and social aforementioned algorithms, a HYBRID approach emerges, thus generating a more intelligent mechanism. For each strategy, we evaluate the influence of several parameters in the network performance, as well as we comparatively evaluate the strategies in different scenarios. Experiment results, obtained both in emulated (with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network) and real scenarios, show the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of mobile networks as a smart city network infrastructure.

An Interest-Based Approach for Reducing Network Contentions in Vehicular Transportation Systems.

Traffic management systems (TMS) are the key for dealing with mobility issues. Moreover, 5G and vehicular networking are expected to play an important role in supporting TMSs for providing a smarter, safer and faster transportation. In this way, several infrastructure-based TMSs have been proposed to improve vehicular traffic mobility. However, in massively connected and multi-service smart city scenarios, infrastructure-based systems can experience low delivery ratios and high latency due to packet congestion in backhaul links on ultra-dense cells with high data traffic demand. In this sense, we propose I am not interested in it (IAN3I), an interest-based approach for reducing network contention and even avoid infrastructure dependence in TMS. IAN3I enables a fully-distributed traffic management and an opportunistic content sharing approach in which vehicles are responsible for storing and delivering traffic information only to vehicles interested in it. Simulation results under a realistic scenario have shown that, when compared to state-of-the-art approaches, IAN3I decreases the number of transmitted messages, packet collisions and latency in up to , and respectively while dealing with traffic efficiency properly, not affecting traffic management performance at all.

Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information

Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.

Impacts of Changing from Permissive/Protected Left-Turn to Protected-Only Phasing: Case Study in the City of Tucson, Arizona

Recent research has focused on the safety or mobility impacts of signal timing. Several studies have compared the choice between a protected-only left turn (PO) and a protected-permissive left turn (PPLT). However, few have compared both the safety and mobility impacts, and their tradeoffs. This study proposed data-driven methods to conduct a pilot study at an intersection in Tucson, Arizona. This study evaluated the impacts on vehicular mobility and multi-modal safety when changing from a PPLT to a PO. First, the daily and annual delay for the through and left-turn movements for the intersection was evaluated using a calibrated delay model and year-long 15-min traffic sensor data. Then, real-world near misses between cyclists, pedestrians, and vehicles were manually collected and analyzed using 48 h of videos. Last, both mobility and safety measures were converted into an annual cost to determine the trade-off between the before (PPLT) and the after (PO) situations. The results of this study demonstrate the feasibility of the proposed methods, providing practitioners with different options to evaluate left-turn phasing strategies effectively and efficiently.

Fast, Efficient Broadcast Schemes Based on the Prediction of Dynamics in Vehicular Ad Hoc Networks

The performance of multi-hop broadcast in vehicular ad hoc networks can be greatly affected by the highly varying link dynamics caused by mobility of vehicles and complex wireless channel environment. The conventional receiver-based and sender-based broadcast protocols usually decide the relay sequences only based on distance, neglecting the influence of links qualities. In this paper, we consider the impact of link qualities and vehicular mobility and investigate how to select broadcast relays to minimize the broadcast delay and maximize the broadcast efficiency. We first propose a novel broadcast scheme based on the prediction of dynamics (BPD), which utilizes the dynamic information to achieve the model-based prediction and combines the sender-based and receiver-based relay selection schemes for multi-hop broadcast. Then, we propose a novel metric called the expected remaining delay (D) and implement it in BDP (BPD-D) for minimizing the broadcast delay. We also propose a novel metric called the expected rebroadcast efficiency (E) and implement it in BDP (BPD-E) for maximizing the broadcast efficiency. The simulation results show that our proposed BPD-D and BPD-E broadcast protocols outperform the conventional protocols, while BPD-D can achieve the least delay and BPD-E has the highest dissemination efficiency.

Jamcloud: Turning Traffic Jams Into Computation Opportunities–Whose Time Has Come

Traffic jam has been and will remain a major problem in most cities around the world. We view this situation as a computation opportunity and propose to build the cloud-computing facilities on the top of jammed cars and other vehicles to turn the energy and other resources that otherwise would be wasted into computing power. Specifically, we define the vehicular mobile cloudlet as a group of nearby vehicular mobile devices congested in the traffic jams while connected by short-range communications. Based on local mobile cloudlets of congested vehicles and available remote cloud-computing resources, we propose and evaluate the JamCloud, a system to collect and aggregate the computation capacities of congested vehicles in the city. For this newly-conceived novel cloud system, the fundamental problems are how much computation capacity the mobile cloudlets have and what is the overall achievable performance of the whole JamCloud system. Based on the three realistic large-scale urban vehicular mobility traces, we analyze and model the vehicular mobility patterns as well as the computation capacity in both the mobile cloudlet and system wide. Specifically, by analyzing the patterns of staying time, resident number, and incoming and outgoing of vehicles in the regions with traffic jams, we model the mobile cloudlet as a periodic non-homogeneous immigration-death process, which predicts its computational capability with accuracy above 90%. Based on the observed strong Poisson features of mobile cloudlets, we further propose a queueing network model to characterize the overall performance of JamCloud with the computing resources of multiple mobile cloudlets and remote clouds. Our study thus reveals the microscopic computation capability of local cloudlets as well as the overall and asymptotic performance of the JamCloud, which provides foundational understanding to design, such systems in practice. With the inevitably growing trend of making vehicles electric, and in particular with the forthcoming 5th generation (5G) mobile communication technology, the time has finally come to turn JamCloud into reality.

Vehicular Network Simulation Environment via Discrete Event System Modeling

A vehicular ad-hoc network (VANET) enables vehicles to communicate with each other directly or via roadside infrastructure in order to improve road safety and efficiency. Within a VANET, communications can potentially alter vehicular mobility and conversely, that the mobility could potentially influence vehicular communications. Therefore, a VANET simulator environment is needed that can accurately model interactions between vehicular mobility and network protocols. In this paper, we present a novel vehicular network simulation environment designed using the MATLAB discrete event system (DES) in the SimEvents toolbox. The proposed simulation environment is a bit-accurate, discrete event simulator that integrates vehicular mobility operations with wireless network communication. This paper provides details on the design of the proposed simulator. Its computational costs are evaluated in terms of events quantities and execution time. The physical (PHY) layer of the proposed simulator shows a more realistic packet success rate (PSR) using bit-level processing techniques when compared with the packet-based NS-3 simulator. The performance of the priority-based media access control (MAC) layer proves the data with different priorities that can coexist in the same channel.

PTCCR: A Path Transmission Costs-Based Multi-Lane Connectivity Routing Protocol for Urban Internet of Vehicles

In Internet of Vehicles (IoV), high vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, variation of network topology and constraints for routing protocols. Besides, with vehicular positions and street-level digital maps available, the intersection-based geographic routing becomes indispensable considering its ability for avoid forwarding packets through segments with low network density and high scale of network disconnections. In this paper, considering the benefits of intersection-based routing and challenges of high dynamic IoV, a Path Transmission Costs-based Multi-lane Connectivity Routing protocol (PTCCR) is proposed with the help from intersection nodes and/or neighboring nodes. First, we investigate the multi-lane connectivity based on vehicular speed under free-flow state for various types of road sections. Second, A Path Transmission costs (PTC) measurement mechanism is proposed considering the impact of the sequence of selected sections or intersections on the routing performance. After that, the PTC of sent packets is quantitatively analyzed and used as the path selection metric. Finally, the path with the largest multi-lane connectivity and lowest PTC is selected as the optimal path taking the transmission direction, neighbor's location and destination position into account. Numerical results show that our proposed PTCCR outperforms two state-of-art routings, i.e., the real-time intersection-based segment aware routing protocol (RTISAR) and Reliable Traffic Aware Routing protocol (RTAR), in terms of packet delivery ratio, average end-to-end delay and communication overhead.