The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one’s car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloud-based applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking.

Vehicular ad hoc Networks (VANETs) are an emerging technology with robust applications in Intelligent Transport System. It consists of smart vehicles and roadside infrastructure which communicate through open access wireless networks. The rapid growth in vehicles results in VANETs becoming large-scale, dynamic, heterogeneous and it is possible for the attacker to harm vehicular communication which leads to life-endangering situations. VANETs must ensure secured vehicular communication using strong privacy-preserving and authentication mechanisms. In addition, efficiency is also a major concern in VANETs. Numerous studies have been discussed in literature for VANETs privacy and security. Nevertheless, no one covered the privacy and security issues as a holistic view. In this paper, we have given a detailed background overview of VANETs. Details of different possible attacks in VANET are also given in this paper. We have classified privacy and authentication schemes into four major groups with their security mechanisms, security requirements, strength, limitations, attacks countermeasures and performance measures. Finally, we have discussed some open issues in the field of VANETs security.

Accurate recognition of patients’ physical activities leads to correct diagnosis and treatments. However, currently deployed approaches are deficient in recognizing the activities requiring frequent interposture transitions, such as jogging, jumping, turning left, and going upstairs. The reason is that with the change in position and rotation, different activity signals are generated, which are difficult to distinguish from other activities and can therefore mislead the physicians. Therefore, we propose to employ a methodology that utilizes the energy expenditure for each activity and reduces the dimensions of the feature space to differentiate among the activities. In this regard, we employ a feature descriptor called local energy-based shape histogram to preserve the maximum information of local energy. Considering the high volumes of continuously generated data, our methodology integrates cloud computing services with the body area networks. We also investigate the effects of on-body sensors’ location on the activity recognition accuracy and also identify the best sensor position for a certain activity with the maximum accuracy. We used the wearable action recognition database dataset to perform the experiments. Our analysis shows that for each activity to be recognized at a decent level, it is imperative to observe the activity recognition performance by simultaneously applying different combinations of sensors.