GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Navigation strategies for cooperative localization based on a particle-filter approach
Integrated Computer-Aided Engineering
Morphological neural networks and vision based simultaneous localization and mapping
Integrated Computer-Aided Engineering - Artificial Neural Networks
Ensemble with neural networks for bankruptcy prediction
Expert Systems with Applications: An International Journal
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
A survey of geocast routing protocols
IEEE Communications Surveys & Tutorials
Integrated Computer-Aided Engineering
A transferable belief model applied to LIDAR perception for autonomous vehicles
Integrated Computer-Aided Engineering
An intelligent route management system for electric vehicle charging
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
Information dependability in distributed systems: The dependable distributed storage system
Integrated Computer-Aided Engineering
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Recently, the attention on electric vehicle EV/plug-in hybrid electric vehicle PHEV has been growing. The EV/PHEV will be one of important electric loads from the viewpoint of smart grid in near future. It is anticipated that the EV/PHEV will affect the load pattern of power grids. For this reason, the effective management of the EV/PHEV based on the information and communications technologies will be a major function of smart grid. For EV/PHEV applications, a user interface device equipped on EVs/PHEVs allows the driver to receive instructions or seek advice to manage EV's/PHEV's battery charging/discharging process. In this paper, we present a design of vehicle-grid communications system. To improve the performance of the system, we customize our communication protocol for distributing EV/PHEV's charging information reliably. Also, we model a one-step ahead nonlinear predictor of the charge or discharge price using a neural network ensemble technique. In the experiments, we verify the performance of our protocol with respect to the data delivery ratio and the number of message forwarding. We also compare the price prediction accuracy using the real energy price data, compared with the conventional methods.