Mobility Patterns Of Vehicles Research Articles

Characterizing the Capability of Vehicular Fog Computing in Large-scale Urban Environment

The worldwide increase of vehicles is demanding the deployment of an intelligent transportation system for the urban environment. Recently, cloud computing technology is utilized to make the vehicles on the roads smarter and offer better driving experience. However, the intrinsic client-server communication model in the cloud-assisted service cannot meet the increasing demands for intensive computing in vehicles. To solve this challenging issue, we investigate another form of computing service, vehicular fog computing (VFC), which is a group of nearby smart vehicles connected via peer-to-peer communication model. Though VFC can provide computing service to any task initiator, its computational capability, i.e., the ability to provide computing service to the initiator, might be severely constrained by the realistic environments including limited communication ranges, high speeds and unpredictable mobility patterns of vehicles. In this paper, we characterize the computational capability (indicated by the product of processor speed and the time length from receiving the task) of VFC in a practical scenario through studying real-world vehicular mobility traces of Beijing. Specially, we propose a time-varying graph model to access the capability of VFC in such a large-scale urban environment with different scenarios. Based on this model, we reveal the temporal and spatial characteristics of the computational capability with different number of task initiators and portray its distribution of the number of connected vehicles and the computational capability. The distribution of the computational capability is also portrayed. Based on these observations, we define two modes to depict two different models of task distribution. Furthermore, we reveal the relationship between the computational capability and system parameters of computation delay, communication radius, and the number of initiators.

Adaptive mobility-based intelligent decision-making system for driver behaviour prediction with motion nano sensor in VANET

Vehicular ad-hoc network (VANET) offers a dedicated short-range wireless communication among vehicles on the road and to the road side unit. This paper contributes towards an intelligent decision-making system based on the driver behaviour under normal, reckless, fatigue and drunken conditions, and in developing a mobility model to adapt these support vector machine (SVM) classifier behaviours. Attributes such as speed, accelerometer, alcohol and eye blink values are fed to the mega trend diffusion (MTD) function and the attributes are merged and they are classified using SVM. The basic decisions are made by fuzzy interference system (FIS) which is used to find the driver behaviour and is predicted and appropriate decision will be taken for SVM classification. According to the behaviour of the driver, the speed and direction of the vehicle will be affected and it will be reflected in the mobility pattern of other vehicles. In this work, motion nano sensor was used to sense the values to control the driver behaviour.

A Dynamic Hop Choosing Mechanism for Routing in VANETs

Routing in VANETs is challenging because of the frequently changed topology. The dynamic nature is caused by the high speed mobility of vehicles as nodes in the network. Adapting the dynamic topology is the main task during routing in VANETs. Fortunately, the mobility patterns and positons of vehicles in vehicular networks are predicable. Making advantage of these characteristics, this paper presents an improved dynamic hop choosing mechanism. It not only predicts the positions and mobility patterns of vehicles, but also takes into account the vehicular traffic density. Accordingly, the next hop chosen by our mechanism is seldom out of reach even if the topology is quickly changed. Simulation results exhibit that the proposed algorithm improves the packet delivery ratio and reduces the network latency when compared with state of the art protocols.

Adaptive mobility-based intelligent decision-making system for driver behaviour prediction with motion nanosensor in VANET

Vehicular ad-hoc network (VANET) offers a dedicated short-range wireless communication among vehicles on the road and to the road side unit. This paper contributes towards an intelligent decision-making system based on the driver behaviour under normal, reckless, fatigue and drunken conditions, and in developing a mobility model to adapt these support vector machine (SVM) classifier behaviours. Attributes such as speed, accelerometer, alcohol and eye blink values are fed to the mega trend diffusion (MTD) function and the attributes are merged and they are classified using SVM. The basic decisions are made by fuzzy interference system (FIS) which is used to find the driver behaviour and is predicted and appropriate decision will be taken for SVM classification. According to the behaviour of the driver, the speed and direction of the vehicle will be affected and it will be reflected in the mobility pattern of other vehicles. In this work, motion nano sensor was used to sense the values to control the driver behaviour.

Evaluating Location Privacy in Vehicular Communications and Applications

Vehicular ad hoc networks may one day prevent injuries and reduce transportation costs by enabling new safety and traffic management applications, but these networks raise privacy concerns because they could enable applications to perform unwanted surveillance. Researchers have proposed privacy protocols, measuring privacy performance based on metrics such as $k$ -anonymity. Because of the frequency and precision of location of queries in vehicular applications, privacy measurement may be improved by considering additional factors. This paper defines continuous network location privacy ; presents KDT -anonymity, which is a composite metric including average anonymity set size, i.e., $K$ , average distance deviation, i.e., $D$ , and anonymity duration, i.e., $T$ ; derives formulas to calculate theoretical values of $K$ , $D$ , and $T$ ; evaluates five privacy protocols under realistic vehicle mobility patterns using KDT -anonymity; and compares KDT -anonymity with prior metrics.

Efficiency measure of routing protocols in vehicular ad hoc network using freeway mobility model

To move packets among the vehicles mobility pattern of vehicles in a vehicular network performs an imperative factor for creating competent routing protocol. To reproduce the movement features of vehicles in VANET is the main purpose of the mobility model. Manhattan mobility model is conversed by a lot of researchers. Merely very limited research study is prepared on highway and freeway mobility models. In this document Cluster scheme, different routing protocols are used to the freeway mobility based vehicular architecture. The Ns2.34 simulation effect illustrates the effort of cluster scheme over different protocols and standard 802.11p. The competence of the routing protocols in the vehicular communication by means of freeway movement pattern is estimated by different network parameters.

Impact of residential demand response on power system operation: A Belgian case study

The future power system is characterized by more renewable and uncontrollable capacity at the supply side and an electrification of energy at the demand side. Both evolutions increase the need for flexibility in the power system. Although this flexibility can be triggered at the supply and demand side, the latter is often overlooked. In this perspective, this paper assesses the impact of the use of flexibility at the demand side, also referred to as demand response, on power system operation. A two-stage modeling approach is used which combines a day-ahead deterministic unit commitment model and an hourly simulation in real-time. This approach is tested for two alternative Belgian generation technology mix scenarios including a detailed representation of residential demand response. Hereby, realistic cycling patterns of white goods and mobility patterns of battery electric vehicles serve as an input. This approach allows to quantify operational benefits of demand response and to assess a potential introduction of demand response in power system operation. Results show that in general demand response contributes to a lower cost, higher reliability, and lower emission level of power system operation. Moreover, a higher amount of uncontrollable capacity increases these benefits and therefore the societal value of demand response.

DAWN: A density adaptive routing for deadline-based data collection in vehicular delay tolerant networks

Vehicular Delay Tolerant Networks (DTN) use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness (DAWN). DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes' transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

A Cost-Effective Strategy for Road-Side Unit Placement in Vehicular Networks

In this paper, we study the Roadside Unit (RSU) placement problem in vehicular networks. We focus on the highway-like scenario in which there may be multiple lanes with exits or intersections along the road. In our model, each vehicle can access RSUs in two ways: 1) direct delivery, which occurs when the vehicle is in the transmission range of the RSUs, and 2) multi-hop relaying, which takes place when the vehicle is out of RSU transmission range. We account for both access patterns in our placement strategy and formulate this placement problem via an integer linear programming model such that the aggregate throughput in the network can be maximized. We also take into account the impact of wireless interference, vehicle population distribution, and vehicle speeds in the formulation. The performance of the proposed placement strategy is evaluated via ns-2 simulations together with VanetMobisim to generate vehicle mobility patterns. The results show that our strategy leads to the best performance as compared with the uniformly distributed placement and the hot spot placement. More importantly, our solution needs the least number of RSUs to achieve the maximal aggregate throughput in the network, indicating that our scheme is indeed a cost effective yet highly efficient placement strategy for vehicular networks.

Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation

Vehicle-to-vehicle and vehicle-to-infrastructure communication systems enable vehicles to share information captured by their local sensors with other interested vehicles. To ensure that this information is delivered at the right time and location, context-aware routing is vital for intelligent inter-vehicular communication. Traditional network addressing and routing schemes do not scale well for large vehicular networks. The conventional network multicasting and broadcasting cause significant overhead due to a large amount of irrelevant and redundant transmissions. To address these challenges, we first take into account contextual properties such as location, direction, and information interest to reduce the network traffic overhead. Second, to improve the relevancy of the received information we leverage the mobility patterns of vehicles and the road layouts to further optimize the peer-to-peer routing of the information. Third, to ensure our approach is scalable, we propose a context-based grouping mechanism in which relevant information is shared in an intelligent way within and between the groups. We evaluate our approach based on groups with common spatio-temporal characteristics. Our simulation experiments show that our context-based routing scheme and grouping mechanism significantly reduces the propagation of irrelevant and redundant information.

Broadcasting safety information in vehicular networks: issues and approaches

A primary goal of intelligent transportation systems is to improve road safety. The ability of vehicles to communicate is a promising way to alleviate traffic accidents by reducing the response time associated with human reaction to nearby drivers. Vehicle mobility patterns caused by varying traffic dynamics and travel behavior lead to considerable complexity in the efficiency and reliability of vehicular communication networks. This causes two major routing issues: the broadcast storm problem and the network disconnection problem. In this article we review broadcast communication in vehicular communication networks and mechanisms to alleviate the broadcast storm problem. Moreover, we introduce vehicular safety applications, discuss network design considerations, and characterize broadcast protocols in vehicular networks.

Biofuels in the energy transition beyond peak oil.A macroscopic study of energy demand in the Stockholm transport system 2030

The objective of this study is to examine the potential for a full transition to domestically produced biofuels in the Stockholm County transport system in 2030, without exceeding the proportional share of national bioenergy assets. This target is chosen in order to test the potential of biofuel assets in Sweden, facilitating the transition to renewable fuel systems, and to display the potential of transport energy demand at macrolevel under tighter conditions on the energy market after fossil oil production has peaked. The distribution of bioenergy to the transport sector, including conversion losses and relationships to other energy sectors, is analysed explicitly. State-of-the-art traffic forecasting models, complemented with a specially designed energy quantification model, are applied to assess energy quantities needed at different vehicle efficiency levels and mobility patterns. The purpose is not to determine the most energy-efficient transport system possible, or to forecast the optimal distribution of bioenergy set aside for the transport sector in the future. Rather, we try to visualise, at a more conceptual level, energy demand as dependent on principle transport strategies, future technological developments and a type of planning that takes technological interlinkages between evolving components into strategic account. This work highlights the importance of implementing both demand and supply-side policies in order to reduce energy use and greenhouse gas emissions in all energy sectors before making assessments of reasonable distributions of bioenergy between energy sectors and other biomass usage.

Prediction-Based Routing for Vehicular Ad Hoc Networks

Development in short-range wireless LAN (WLAN) and long-range wireless WAN (WWAN) technologies have motivated recent efforts to integrate the two. This creates new application scenarios that were not possible before. Vehicles with only WLAN radios can use other vehicles that have both WLAN and WWAN radios as mobile gateways and connect to the Internet while on the road. The most difficult challenge in the scenario is to deal with frequent route breakages due to dynamic mobility of vehicles on the road. Existing routing protocols that are widely used for mobile ad hoc networks are reactive in nature and wait until existing routes break before constructing new routes. The frequent route failures result in a significant amount of time needed for repairing existing routes or reconstructing new routes. In spite of the dynamic mobility, the motion of vehicles on highways is quite predictable compared to other mobility patterns for wireless ad hoc networks, with location and velocity information readily available. This can be exploited to predict how long a route will last between a vehicle requiring Internet connectivity and the gateway that provides a route to the Internet. Successful prediction of route lifetimes can significantly reduce the number of route failures. In this paper, we introduce a prediction-based routing (PBR) protocol that is specifically tailored to the mobile gateway scenario and takes advantage of the predictable mobility pattern of vehicles on highways. The protocol uses predicted route lifetimes to preemptively create new routes before existing ones fail. We study the performance of this protocol through simulation and demonstrate significant reductions in route failures compared to protocols that do not use preemptive routing. Moreover, we find that the overhead of preemptive routing is kept in check due to the ability of PBR to predict route lifetimes.