The main vision of the Internet of Things (IoT) is to equip real-life physical objects with computing and communication power so that they can interact with each other for the social good. As one of the key members of IoT, Internet of Vehicles (IoV) has seen steep advancement in communication technologies. Now, vehicles can easily exchange safety, efficiency, infotainment, and comfort-related information with other vehicles and infrastructures using vehicular ad hoc networks (VANETs). We leverage on the cloud-based VANETs theme to propose cyber-physical architecture for the Social IoV (SIoV). SIoV is a vehicular instance of the Social IoT (SIoT), where vehicles are the key social entities in the machine-to-machine vehicular social networks. We have identified the social structures of SIoV components, their relationships, and the interaction types. We have mapped VANETs components into IoT-A architecture reference model to offer better integration of SIoV with other IoT domains. We also present a communication message structure based on automotive ontologies, the SAE J2735 message set, and the advanced traveler information system events schema that corresponds to the social graph. Finally, we provide the implementation details and the experimental analysis to demonstrate the efficacy of the proposed system as well as include different application scenarios for various user groups. INDEX TERMSSocial network of vehicles, Cyber-physical systems, Internet of Things, Internet of Vehicles, IoT architecture reference model, Intelligent transport systems, SAE J2735.

Vehicular Ad-hoc Networks (VANETs) are seen as the key enabling technology of Intelligent Transportation Systems (ITS). In addition to safety, VANETs also provide a cost-effective platform for numerous comfort and entertainment applications. A pragmatic solution of VANETs requires synergistic efforts in multidisciplinary areas of communication standards, routings, security and trust. Furthermore, a realistic VANET simulator is required for performance evaluation. There have been many research efforts in these areas, and consequently, a number of surveys have been published on various aspects. In this article, we first explain the key characteristics of VANETs, then provide a meta-survey of research works. We take a tutorial approach to introducing VANETs and gradually discuss intricate details. Extensive listings of existing surveys and research projects have been provided to assess development efforts. The article is useful for researchers to look at the big picture and channel their efforts in an effective way.

There has been a large amount of work on smart classrooms spanning over a wide range of research areas including information communication technology, machine learning, sensor networks, mobile computing, and hardware. Consequently, there have been several disparate reviews on various aspects of smart classrooms. Such piecemeal development is not sufficient for a pragmatic smart classroom solution. This article complements the literature by providing a consolidated review of interdisciplinary works under a common nomenclature and taxonomy. This multi-field review has exposed new research opportunities and challenges that need to be addressed for the synergistic integration of interdisciplinary works.

Privacy is a big concern in current video surveillance systems. Due to privacy issues, many strategic places remain unmonitored leading to security threats. The main problem with existing privacy protection methods is that they assume availability of accurate region of interest (RoI) detectors that can detect and hide the privacy sensitive regions such as faces. However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. Further, there are many implicit privacy leakage channels that have not been considered by researchers for privacy protection. We block both implicit and explicit channels of privacy leakage. Experimental results show that the proposed method incurs 38% less distortion of the information needed for surveillance in comparison to earlier methods of global transformation; while still providing near-zero privacy loss.

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In a remote surveillance system, a high resolution surveillance camera streams its video to a user's handheld device. Such devices are unable to make use of the high resolution video due to their limited display size and bandwidth. In this paper, we propose a method to assist the mobile operator of the surveillance camera in focusing on sensitive regions of the video. Our system automatically identifies relevant regions. We introduce a pan and zoom strategy to ensure that the operator is able to see fine details in these areas while maintaining contextual knowledge. Regions of interest are identified using foreground detection as well as face and body detection. The efficacy of the proposed method is demonstrated through a user study. Our proposed method was reported to be more useful than two comparable approaches for getting an understanding of the activities in a surveillance scene while maintaining context.

Social Internet of Things (SIoT) has gained much interest among different research groups in recent times. As a key member of a smart city, the vehicular domain of SIoT (SIoV) is also undergoing steep development. In the SIoV, vehicles work as sensor-hub to capture surrounding information using the in-vehicle and Smartphone sensors and later publish them for the consumers. A cloud centric cyber-physical system better describes the SIoV model where physical sensing-actuation process affects the cloud based service sharing or computation in a feedback loop or vice versa. The cyber based social relationship abstraction enables distributed, easily navigable and scalable peer-to-peer communication among the SIoV subsystems. These cyber-physical interactions involve a huge amount of data and it is difficult to form a real instance of the system to test the feasibility of SIoV applications. In this paper, we propose an analytical model to measure the workloads of various subsystems involved in the SIoV process. We present the basic model which is further extended to incorporate complex scenarios. We provide extensive simulation results for different parameter settings of the SIoV system. The findings of the analyses are further used to design example adaptation strategies for the SIoV subsystems which would foster deployment of intelligent transport systems.

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