Vehicular Ad hoc Networks (VANETs) are special kind of Mobile Ad hoc Networks (MANETs), where vehicles communicate with each other in ad hoc formation. VANETs consist of Vehicles and Road Side Units (RSUs) that assist in the network management. Vehicles communicate with each other and RSUs, with the aim to provide infotainment and safety services on road. Security is an important consideration in VANETs as safety of humans (passengers) is an important issue. Vehicular Public Key Infrastructure (VPKI) is an adapted form of PKI used to achieve the key management and security services in VANETs. Certificate generation and revocation is one of the primary functions of VPKI. Certificate revocation is used for revoking the malicious nodes and terminate its access rights to the network. In this paper we classify revocation schemes in a novel way into centralized and decentralized manners. This paper covers a survey of different certificate revocation schemes, and provides an overview of the research in the area of certificate revocation in VANETs.

Driver's stress detection is a critical research area to help reduce the likelihood of traffic accidents and driver's health complexities due to prolonged stress. Previous work in this area is heavily based on traditional machine learning models to classify the driver's stress levels using handcrafted features extraction techniques. Extracting the best features using these approaches is always a challenging task. Recently, deep learning techniques have emerged for constructing reliable features automatically and classifying the classes with high accuracy. However, large deep learning models face gradient exploding or vanishing problems. Moreover, acquiring a large dataset for training an entire network from scratch is also a challenging task. This paper is based on the deep transfer learning technique to avoid these problems and to reduce computational cost and time. Seven models are proposed for real-world driver's stress levels detection using Electrocardiogram (ECG) signals. Different Convolutional Neural Network (CNN)-based pre-trained networks are used to classify the driver's three stress levels. The time-frequency ECG components for the three stress levels are obtained as scalogram images using a normalized Continuous Wavelet Transform (CWT) filter bank and Morse wavelet. Results show that Model 5 based on Xception outperforms the GoogLeNet, InceptionResNetV2, and InceptionV3 based models by 11.32%, 11.32%, 9.45%, 7.54%, 5.66%, and 1.88% respectively and achieves 98.11% overall validation accuracy. Ranking estimation using fuzzy logic approach shows that Xception based Model 5 achieves the highest rank for driver's high and medium stress levels, while DenseNet-201 based Model 4 achieves the highest rank for low-stress level detection among the other models.

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Nature offers an enormous amount of free energy in the form of Sun [Photovoltaic (PV)]. Therefore, it is interesting to integrate this renewable, everlasting, and echo friendly energy into traditional grid connected power supply. In this study, an energy management system (EMS) is proposed for the proper PV energy integration and utilization into microgrids. For prototype, simulations were performed for 1 kW PV array, 1.5 kV A inverter, and 1.5 kW load employing Lab VIEW. Boolean algebraic operators were incorporated for decisions. A sine wave frequency generator with the desired voltage and frequency measurements was introduced for both grid and PV sources. Numerical indicators are used for current, voltage, and frequency display. The proposed system offers integration and management of PV and national grid power for domestic power utilization. To test the capabilities of the developed EMS, the results were compared with the already established methods. In the developed method, PV is considered as a primary power source for domestic appliances (load) during daytime along with the national grid as a secondary power source. Moreover, to establish its feasibility, economic implications are also discussed.