11,058 research outputs found
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Orion Routing Protocol for Delay-Tolerant Networks
In this paper, we address the problem of efficient routing in delay tolerant
network. We propose a new routing protocol dubbed as ORION. In ORION, only a
single copy of a data packet is kept in the network and transmitted, contact by
contact, towards the destination. The aim of the ORION routing protocol is
twofold: on one hand, it enhances the delivery ratio in networks where an
end-to-end path does not necessarily exist, and on the other hand, it minimizes
the routing delay and the network overhead to achieve better performance. In
ORION, nodes are aware of their neighborhood by the mean of actual and
statistical estimation of new contacts. ORION makes use of autoregressive
moving average (ARMA) stochastic processes for best contact prediction and
geographical coordinates for optimal greedy data packet forwarding. Simulation
results have demonstrated that ORION outperforms other existing DTN routing
protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio,
hop count and first packet arrival
Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review
Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.
ABEONA monitored traffic: VANET-assisted cooperative traffic congestion forecasting
The existing mechanisms to monitor vehicular traffic, such as the use of induction loops and cameras, are expensive to deploy and maintain. Vehicular communications opens up a new world of optimization opportunities as each vehicle can be used as a sensor to measure the fundamental variables defining the traffic state (flow, density, and speed). In this article, we propose ABEONA, a beacon-based traffic congestion algorithm and also the name of the Roman goddess of journey, which captures the current and recent-past traffic trends to forecast the near-future road conditions. Compared to the existing monitoring approaches, ABEONA allows for the estimation of the vehicular density and reduces installation and maintenance costs. ABEONA's algorithm incurs low overhead and enables drivers to use forecast traffic congestion events to replan their route accordingly.Publicad
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Development of a power monitoring and control system to provide demand side management of electric vehicle charging activity.
Due to the recent inflow of Electric Vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. Increased electricity consumption due to EV charging activity results in higher electricity costs due to differences in the billing structures between residential and C&I electric ratepayers. Therefore, it is beneficial to the EVSE charging provider to minimize charging activity around peak demand periods which would result in lower electrical costs overall. A solution is developed that can provide this control without creating a nuisance to electric vehicle owners since EV charging demand is somewhat inelastic due to range anxiety. The primary objective of the research detailed in this dissertation is to develop a novel demand side management system for monitoring the peak demand of commercial time-of-day electric ratepayers that cost effectively predicts and controls electric vehicle charging during peak demand periods. This objective is achieved, therefore confirming the hypothesis that such a system can provide cost and demand benefits to forward-thinking commercial electric ratepayers that provide daytime charging capabilities. This work proposes and evaluates a novel Power Monitoring and Control System (PMCS) that can be implemented at C&I EV charging locations to minimize or eliminate the negative impacts of charging electric vehicles at the workplace in C&I environments. Operation of the PMCS begins by forecasting electrical demand in advance of every 15 minute demand interval throughout the day. The forecast is generated using an artificial neural network and a number of input data streams. Electrical demand has been shown to correlate well with weather data such as temperature and dew point. Therefore, using those measurements along with a date and time stamp, and historical electrical demand measurements, a highly accurate forecast for the following 15-minute demand interval was achieved. From that forecast, the number of EV charging stations that may be active, without the chance of creating new electrical demand peaks, is calculated. Finally, the forecast is then used to properly schedule EV charging activity so that electrical demand peaks can be avoided but charging activity is maximized. The avoidance of charging activity at or near peaks in electrical demand results in lower total electric costs associated with the charging process. The final design was implemented in an EV charging testbed at the University of Louisville and data was collected to verify the operation and performance of the PMCS. With a properly designed scheduling and prioritization control algorithm, increases in electrical demand and associated costs are limited to the error in the forecasting algorithm used for predicting electrical demand levels. The final design of the forecasting algorithm results in a mean absolute percent error of 0.02% to 0.08% in the electrical demand forecast. This corresponds to approximately 3 to 10 kVA of error in electrical demand. Taking this error into account, total cost of charging several EVs is reduced by nearly 90%. Furthermore, for scenarios where there are several more electric vehicles requiring charge than there are charging stations available, several scheduling algorithms are presented in an attempt to minimize the total processing time required for completing all charging transactions
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