Search for: All records

Wireless Charging Highways (WCHs) have been introduced by industry and academia to enable charging-while-driving for electric vehicles (EVs) and to combat range anxiety. While detailed planning and performance evaluation of such systems are crucial due to high cost and long life expectancy, most existing works assume a perfect communication environment. In this paper, we introduce a joint capacity model that takes into account both power and communication resources for WCH construction planning, and optimal day-to-day operation. The vehicle-to-infrastructure (V2I) communication and grid power capacities, along with the EV’s average service rate are formulated following technology requirements, EV speed-density characteristics, and the EV’s energy needs and consumption. In addition, a two-dimension Markov chain-based model is designed to capture the WCH power and connectivity dynamics. The proposed model can be used to calculate the system’s Quality of Service (QoS) and profit, provide design insights, and assess the impact of speed regulation, or admission control on the WCH lane. Finally, the performance of the proposed model is evaluated using real US highway data with the results demonstrating its ability to accurately capture the service provision dynamics, and to identify trade-offs between system parameters. 

Internet of Things (IoT) deployments are becoming the backbone of all future Smart City (SC) environments. They can, therefore, act as massive crowd-sourced data aggregators, driven by device-to-device interactions with SC users' mobile devices and their wireless interfaces. Provided that, our research focuses on developing probabilistic and machine learning models to (a) enable knowledge discovery from passive user interactions with the wireless IoT infrastructure and (b) apply the collected intelligence to increase the energy-efficiency and resiliency of the Smart City's IoT network. In this extended abstract we elaborate on the motivation behind our work, and the related challenges, while pointing to the solutions developed so far.