Mahmood Fathy

Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area

Analytical Model for Connectivity in Vehicular Ad Hoc Networks

We investigate connectivity in the ad hoc network formed between vehicles that move on a typical highway. We use a common model in vehicular traffic theory in which a fixed point on the highway sees cars passing it that are separated by times with an exponentially distributed duration. We obtain the distribution of the distances between the cars, which allows us to use techniques from queuing theory to study connectivity. We obtain the Laplace transform of the probability distribution of the connectivity distance, explicit expressions for the expected connectivity distance, and the probability distribution and expectation of the number of cars in a platoon. Then, we conduct extensive simulation studies to evaluate the obtained results. The analytical model that we present is able to describe the effects of various system parameters, including road traffic parameters (i.e., speed distribution and traffic flow) and the transmission range of vehicles, on the connectivity. To more precisely study the effect of speed on connectivity, we provide bounds obtained using stochastic ordering techniques. Our approach is based on the work of Miorandi and Altman, which transformed the problem of connectivity distance distribution into that of the distribution of the busy period of an equivalent infinite server queue. We use our analytical results, along with common road traffic statistical data, to understand connectivity in vehicular ad hoc networks.

Enhancing AODV routing protocol using mobility parameters in VANET

VANET is new generation of ad hoc networks that implement between vehicles on a road. Because of high mobility, routing in VANET has more problems than MANET. Thereby, in this paper we propose a modification on AODV as MANET routing protocol to make it adaptive for VANET. When a node is mobile, it has three mobility parameters: position, direction and speed. In our method, we have used direction as most important parameter to select next hop during a route discovery phase. With respect to mobility model, if nodes has same direction with source and/or destination nodes, our solution might selects them as a next hop. Position is another parameter that we used for next hop selection.

A window-based image processing technique for quantitative and qualitative analysis of road traffic parameters

Traffic information is an important tool in the planning, maintenance, and control of any modern transport system. Of special interest to traffic engineers are parameters of traffic flow such as volume, speed, type of vehicle, queue parameters, traffic movements at junctions, etc. Various algorithms, mainly based on background differencing techniques, have been applied for this purpose. Since background-based algorithms are very sensitive to ambient lighting conditions, they have not yielded the expectative results. In this paper, we describe a novel approach to measure traffic parameters. This approach is based on applying edge-detection techniques to the key regions or windows. This method of measuring road traffic parameters eliminates the need of a background frame, which is an essential, but unreliable technique for background-based image-detection methods. A dynamic threshold selection technique has also been introduced to select the threshold value automatically. The image process algorithm has been applied to measure basic traffic parameters such a traffic volume, types of vehicles, as well as the complex traffic parameters such as queue parameters and movements of vehicles at a traffic junction.

Short Communication: Improving connectivity in vehicular ad hoc networks: An analytical study

Connectivity in vehicular ad hoc networks may degrade dramatically in sparse traffic and also high speed highways. In this paper we study a way to improve the connectivity by adding some extra nodes with higher transmission range which we call mobile base-stations. These nodes can also offer commercial services (e.g. advertisement, video, audio, etc.) to the vehicles on roads. Besides, the financial profit of those services also depends on a satisfactory connectivity. We use an equivalent M/G/~ queuing model in order to investigate the connectivity. We further take into account the case when some vehicles do not participate in the network either because they are not equipped with wireless transceivers or some other reasons like security concerns. Moreover, connectivity in presence of fixed Road Side Units (RSUs) is also studied. Our proposed analytical model can be used to find the optimum values of the number of base-stations as well as their transmission range in order to achieve a desired degree of connectivity.

A Window-based Edge Detection Technique for Measuring Road Traffic Parameters in Real-Time

The real-time measurement and analysis of various traffic parameters such as volume, speed and types of vehicles are increasingly required for traffic control and management. Image processing technique is now considered as an attractive and flexible method for automatic analysis and data collections in traffic engineering. Various algorithms based on background differencing techniques have been applied to vehicle detection. However, these techniques have not yielded good results due to inefficiency of background updating techniques. This paper describes a new method for vehicle detection based on edge detection techniques. This method eliminates the background updating and has been implemented in real-time on a low-cost 80386 based microcomputer system. This new vehicle detection algorithm works under various lighting and weather conditions and measures other traffic parameters in real-time as well.

Metrics for Performance Evaluation of Safety Applications in Vehicular Ad Hoc Networks

Abstract In the recent years, direct message exchange between vehicles in order to improve the safety of road traffic has been attracting lots of interest in both networking and road safety communities. While travelling on a road, vehicles form an ad hoc network called Vehicular Ad hoc NETwork (VANET) and deploy life safety applications. Evaluating the performance of these applications is primordial for realizing VANETs in real life. Current literature lacks efficient ways to evaluate the performance of safety applications and mostly leverages on classical networking metrics like delay, delivery rate etc. In this paper, we consider both networking and safety concerns simultaneously to come up with more efficient methods. In particular, we first point out the significance of fairness and coverage from safety viewpoint. Then, we introduce two new metrics called beaconing rate and effective range aiming at providing more facilities for safety performance evaluation in VANET s research. Furthermore, realizing...

A Reliable Method for Disseminating Safety Information in Vehicular Ad Hoc Networks Considering Fragmentation Problem

Many of applications in vehicular ad hoc networks (VANET) are based on dissemination of information, so broadcasting is a fundamental service in these networks. Broadcasting is a difficult task since vehicular networks often lack continuous end-to-end connectivity and have large variations in node densities. In this paper we present a method that improves the reception rates of broadcast messages by overcoming problem of connectivity gaps. In our method, every forwarder of message when facing a gap sends small messages periodically. These methods are used to understand entrance of a new vehicle that can be selected as the next forwarder of message. We have evaluated the performance of proposed protocol in NS2 under various network conditions.

An Iranian License Plate Recognition System Based on Color Features

In this paper, an Iranian vehicle license plate recognition system based on a new localization approach, which is modified to reflect the local context, is proposed, along with a hybrid classifier that recognizes license plate characters. The method presented here is based on a modified template-matching technique by the analysis of target color pixels to detect the location of a vehicles license plate. A modified strip search enables localization of the standard color-geometric template utilized in Iran and several European countries. This approach uses periodic strip search to find the hue of each pixel on demand. In addition, when a group of target pixels is detected, it is analyzed to verify that its shape and aspect ratio match those of the standard license plate. In addition to being scale and rotation invariant, this method avoids time-consuming image algorithms and transformations for the whole image pixels, such as resizing and Hough, Fourier, and wavelet transforms, thereby cutting down the detection response time. License plate characters are recognized by a hybrid classifier that comprises a decision tree and a support vector machine with a homogeneous fifth-degree polynomial kernel. The performance detection rate and the overall system performance achieved are 96% and 94%, respectively.

A Driver Face Monitoring System for Fatigue and Distraction Detection

Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection; the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction.