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In this paper, we introduce an open-source model MOVESTAR to calculate the fuel consumption and pollutant emissions of motor vehicles. This model is developed based on U.S. Environmental Protection Agencys (EPA) Motor Vehicle Emission Simulator (MOVES), which provides an accurate estimate of vehicle emissions under a wide range of user-defined conditions. Originally, MOVES requires users to specify many parameters through its software, including vehicle types, time periods, geographical areas, pollutants, vehicle operating characteristics, and road types. In this paper, MOVESTAR is developed as a simplified version, which only takes the second-by-second vehicle speed data and vehicle type as inputs. To enable easy integration of this model, its source code is provided in various languages, including Python, MATLAB and C++. A case study is introduced in this paper to illustrate the effectiveness of the model in the development of advanced vehicle technology.
We introduce a hash chain-based secure cluster. Here, secure cluster refers to a set of vehicles having vehicular secrecy capacity of more than a reference value. Since vehicle communication is performed in such a secure cluster, basically secure veh
Wireless networks have been widely deployed for many Internet-of-Things (IoT) applications, like smart cities and precision agriculture. Low Power Wide Area Networking (LPWAN) is an emerging IoT networking paradigm to meet three key requirements of I
We develop and analyze a measure-valued fluid model keeping track of parking and charging requirements of electric vehicles in a local distribution grid. We show how this model arises as an accumulation point of an appropriately scaled sequence of st
By new advancements in vehicle manufacturing; evaluation of vehicle quality assurance has got a more critical issue. Today noise and vibration generated inside and outside the vehicles are more important factors for customers than previous. So far se
Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper