Social Network Analysis

A survey on vehicular cloud computing

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The Essential Physics Of Medical Imaging

With the rapid development of the Intelligent Transportation System (ITS), vehicular communication networks have been widely studied in recent years. Dedicated Short Range Communication (DSRC) can provide efficient real-time information exchange among vehicles without the need of pervasive roadside communication infrastructure. Although mobile cellular networks are capable of providing wide coverage for vehicular users, the requirements of services that require stringent real-time safety cannot always be guaranteed by cellular networks. Therefore, the Heterogeneous Vehicular NETwork (HetVNET), which integrates cellular networks with DSRC, is a potential solution for meeting the communication requirements of the ITS. Although there are a plethora of reported studies on either DSRC or cellular networks, joint research of these two areas is still at its infancy. This paper provides a comprehensive survey on recent wireless networks techniques applied to HetVNETs. Firstly, the requirements and use cases of safety and non-safety services are summarized and compared. Consequently, a HetVNET framework that utilizes a variety of wireless networking techniques is presented, followed by the descriptions of various applications for some typical scenarios. Building such HetVNETs requires a deep understanding of heterogeneity and its associated challenges. Thus, major challenges and solutions that are related to both the Medium Access Control (MAC) and network layers in HetVNETs are studied and discussed in detail. Finally, we outline open issues that help to identify new research directions in HetVNETs.

Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions

Today, vehicles are increasingly being connected to the Internet of Things which enable them to provide ubiquitous access to information to drivers and passengers while on the move. However, as the number of connected vehicles keeps increasing, new requirements (such as seamless, secure, robust, scalable information exchange among vehicles, humans, and roadside infrastructures) of vehicular networks are emerging. In this context, the original concept of vehicular ad-hoc networks is being transformed into a new concept called the Internet of Vehicles (IoV). We discuss the benefits of IoV along with recent industry standards developed to promote its implementation. We further present recently proposed communication protocols to enable the seamless integration and operation of the IoV. Finally, we present future research directions of IoV that require further consideration from the vehicular research community.

Internet of Vehicles: Architecture, Protocols, and Security

A comprehensive survey on vehicular Ad Hoc network

Vehicular ad hoc networks (VANETs) have emerged as an application of mobile ad hoc networks (MANETs), which use dedicated short-range communication (DSRC) to allow vehicles in close proximity to communicate with each other or to communicate with roadside equipment. Applying wireless access technology in vehicular environments has led to the improvement of road safety and a reduction in the number of fatalities caused by road accidents through development of road safety applications and facilitation of information sharing between moving vehicles regarding the road. This paper focuses on developing a novel and nonintrusive driver behavior detection system using a context-aware system in VANETs to detect abnormal behaviors exhibited by drivers and to warn other vehicles on the road to prevent accidents from happening. A five-layer context-aware architecture is proposed, which is able to collect contextual information about the driving environment, to perform reasoning about certain and uncertain contextual information, and to react upon that information. A probabilistic model based on dynamic Bayesian networks (DBNs) in real time, inferring four types of driving behavior (normal, drunk, reckless, and fatigue) by combining contextual information about the driver, the vehicle, and the environment, is presented. The dynamic behavior model can capture the static and the temporal aspects related to the behavior of the driver, thus leading to robust and accurate behavior detection. The evaluation of behavior detection using synthetic data proves the validity of our model and the importance of including contextual information about the driver, the vehicle, and the environment.

Context-Aware Driver Behavior Detection System in Intelligent Transportation Systems

Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.

Social Internet of Vehicles for Smart Cities

Due to the advancement in technologies and excessive usability of smartphones in various domains (e.g., mobile banking), smartphones became more prone to malicious attacks.Typing on the soft keyboard of a smartphone produces different vibrations, which can be abused to recognize the keys being pressed, hence, facilitating side-channel attacks. In this work, we develop and evaluate AlphaLogger- an Android-based application that infers the alphabet keys being typed on a soft keyboard. AlphaLogger runs in the background and collects data at a frequency of 10Hz/sec from the smartphone hardware sensors (accelerometer, gyroscope and magnetometer) to accurately infer the keystrokes being typed on the soft keyboard of all other applications running in the foreground. We show a performance analysis of the different combinations of sensors. A thorough evaluation demonstrates that keystrokes can be inferred with an accuracy of 90.2% using accelerometer, gyroscope, and magnetometer.

AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes

This paper presents the development of a smart fall detector to minimise accidental falls which occur among elderly people, especially for indoor situations. A video-based detection system was utilised, as this can preserve privacy and monitor the physical activities of elderly people. In order to identify the correct situation among a set of predetermined situations, which consisted of praying, sitting, standing, bending, kneeling and lying down, a neural network system was incorporated in the fall detection computation algorithm. The neural network analysed the binary map image of the person and then identified which plausible situation the person was in at any particular instant in time. The fall detector's performance in successfully detecting falls was then evaluated using two statistical metrics: specificity and sensitivity. The performance of this fall detection system in identifying falls was also evaluated during two non-normal gait movements, stumbling and limping, so as to mimic the motions of a good proportion of the elderly people having these types of walking gait movements. It was shown that the implemented video-based fall detection system could be a promising solution for detecting indoor falls among senior citizens.

Ali H. Al-Bayatti

Papers

A comprehensive survey on vehicular Ad Hoc network

Context-Aware Driver Behavior Detection System in Intelligent Transportation Systems

Social Internet of Vehicles for Smart Cities

AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes

The implementation of an intelligent and video-based fall detection system using a neural network