Cooperative Intelligent Transport Systems (C-ITS) is an advanced technology for road safety and traffic efficiency over Vehicular Ad Hoc Networks (VANETs) allowing vehicles to communicate with other vehicles or infrastructures. The security of VANETs is one of the main concerns in C-ITS because there may be some attacks in such type of network that may endanger the safety of the passengers. Intrusion Detection Systems (IDS) play an important role to protect the vehicular network by detecting misbehaving vehicles. In general, the works in the literature use the same well-known features in a centralized IDS. In this paper, we propose a Machine Learning (ML) mechanism that takes advantage of three new features, which are mainly related to the sender position, allowing to enhance the performances of IDS for position falsification attacks. Besides, it presents a comparison of two different ML methods for classification, i.e. k-Nearest Neighbor (kNN) and Random Forest (RF) that are used to detect malicious vehicles using these features. Finally, Ensemble Learning (EL) which combines different ML methods, in our case kNN and RF, is also carried out to improve the detection performance. An IDS is constructed allowing vehicles to detect misbehavior in a distributed way, while the detection mechanism is trained centrally. The results demonstrate that the proposed mechanism gives better results, in terms of classification performance indicators and computational time, than the best previous approaches on average.INDEX TERMS misbehavior detection, machine learning, vehicular ad hoc network, intelligent transport systems, dataset.

Summary Electricity, heating and cooling usage at the same time constitute an important part of primary energy consumption. Currently, intense efforts are being devoted to the improvement and development of new technologies to promote energy savings and to mitigate the global warming. Among these technologies, tri‐generation and poly‐generation systems come into prominence due to their high efficiencies, as well as low operating costs and low greenhouse gas emissions. Nevertheless, modelling and sizing these systems is a large‐sized problem itself, and the higher the number of inputs, the higher the computational complexity. This paper aims to give a comprehensive review of the optimisation techniques used in tri‐generation and poly‐generation systems modelling and optimisation. After having reviewed more than a hundred papers, taxonomy is constructed. Self‐organising map is used to cluster the reviewed studies based on the optimisation techniques. Variations of the energy resources and the outputs of implemented systems are summarised. The crowd of clusters demonstrated the widely used optimisation techniques and the gaps in research. This study will certainly pioneer the optimisation studies in the field of tri‐generation/poly‐generation applications. Copyright © 2015 John Wiley & Sons, Ltd.

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Summary Energy consumption is increasing in parallel with the population and technological advancements. Albeit the energy efficiency efforts, industrial consumption, and transportation have quite important roles in this increase. The electric vehicles (EVs) are expected to constitute a positive solution to mitigate energy use in transportation. Increasing penetration of EVs into public transport provides new opportunities both for environmental sustainability and transportation infrastructure. To project the potential operator revenue, we set up a hybrid model (solar and storage), which optimizes scheduling for the EV charging unit and evaluates environmental and economic benefits. Different operational scenarios of PV‐assisted charging stations will be simulated and solved with Rule‐based decision making. Since regulations have an important role in building the optimization model, scenarios are created considering different European regulations. Uncertainties depending on the traffic jam (arrival‐departure times), technological differences of the charging stations are encountered in the model. The results are compared among three different scenarios. Net present value of the system changes for different scenarios and electricity sale price is one of the main factors. This value tends to increase after an annual electricity sale price increase rate of 11%. Novelty statement In this study, the effect of different economic parameters on an electric vehicle charging station were analyzed. A system with electric vehicles, solar panel support, and external battery was mathematically modeled and a case study was done.