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Speed forecasting has numerous applications in intelligent transport systems' design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles' speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles' characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.

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We propose in this article, a novel compact reconfigurable patch antenna with frequency diversity. This antenna can be tuned by changing the switching mode of two PIN diodes from the ON–ON state to OFF–OFF state to switch it over two different mobile communication technologies, namely 3G and 4G. The antenna has a simple geometry and a small structure, its dimensions are 45 × 38 × 1.5 mm3 and it is printed on an FR4 substrate with a thickness of 1.5 mm. The prototype of this antenna has been fabricated and tested. The measurement results confirmed the simulation predictions in terms of frequency diversity performances. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1622–1626, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27649

MapReduce is becoming a powerful parallel data processing model and is adopted by many cloud services providers to build cloud computing framework. However, in public cloud systems, several service providers may come from different administration domain out of the user control and may be untrustworthy. Hence, security of MapReduce computation is essential in public cloud systems. Additionally, MapReduce dataprocessing services are long-running, which increases the possibility that an attacker is able to compromise some workers and make them misbehave to corrupt the integrity of all computations allocated to these workers. Thus, the computation integrity is a major concern for Mapreduce user in public cloud environment. In this paper, we propose a new mechanism to ensure the computation integrity of MapReduce in public cloud computing environment. By using replication-based voting method and reputation-based trust management system, our mechanism can efficiently detect both collusive and non-collusive malicious workers and guarantee high computation accuracy with an acceptable overhead.