Yasser Morgan

Managing DSRC and WAVE Standards Operations in a V2V Scenario

The Dedicated Short-Range Communications (DSRC) standards suite is based on multiple cooperating standards mainly developed by the IEEE. In particular, we focus this paper on the core design aspects of DSRC which is called Wireless Access in Vehicular Environment (WAVE). WAVE is highlighted in IEEE 1609.1/.2/.3/.4. The DSRC and WAVE standards have been the center of major attention in both research and industrial communities. In 2008, WAVE standard was the third best seller standards in the history of the IEEE. This attention reflects the potential of WAVE to facilitate much of the vehicular safety applications. In this paper we present a fairly detailed tutorial of the WAVE standards. We extend the paper by describing some of the lessons learned from particular design approaches. We direct the reader to the landmark research papers in relevant topics. We alert the reader about major open research issues that might lead to future contribution to the WAVE design.

Evaluation of Base Station Placement Scenarios for Mobile Node Localization

The growing interest in utilizing short-range wireless for object positioning is compounded by the inherent GPS limitations. In order to harness the highest accuracy out of short-range positioning systems, we need to optimize the placement of Base-Stations (BS) in a given area. Hence, we need to study the impact of different influential attributes on the positioning problem as we alter the BS placement approach. In this paper we investigate different scenarios for short-range BS placement that maximize coverage and minimize the offset and strength of positioning error. Simulation results demonstrate that the optimal layout tends to be uniform placement of BSs in the area.

Accurate positioning using Short-Range Communications

Satellite-based positioning systems such as GPS have been used for positioning by the transportation industry for decades. Accurate positioning (≪1 cm error) requires the availability of multiple (5+) GPS signals which is ‘possible’ only in suburban communities but utterly unachievable in urban areas. We are motivated by the inevitably and imperative need to resolve accurate positioning issues for transportation and industrial applications. In this research we focus on developing positioning scheme based on short-range wireless signals. To achieve that, we develop a system assuming the availability of multiple fixed access points (5+) and employ Time-Difference-of-Arrival (TDoA) with Kalman Filter. We also discuss multi/tri-lateration and evaluate some of the root causes for Dilution of Precision (DoP) of calculated positioning. We use fixed calibrated 2.4 GHz access points. We use realtime observed data while applying our model off-line. We extended the model to mimic the 5.9 GHz Dedicated Short Range Communications (DSRC) signals as defined in IEEE 1609.x. Results are compared in each case and compared to accurately calibrated Differential Global Positioning System (DGPS) record captured on the same testing. We maintained clear Line-of-Sight (LoS) throughout our evaluation and used low speed of the moving vehicles (≪ 60 Km/h).

A new base stations placement approach for enhanced vehicle position estimation in parking lot

In the recent decades, short-range wireless has become of great importance in the applications of Intelligent Transportation Systems (ITS); especially, that serve traffic management and safety. Various approaches are concerned with optimal positioning of a vehicle and Base-Stations (BSs) placement within a specific area. The optimization problem stands as an exhaustive challenge. This paper discusses different approaches for short-range BSs placement with respect to maximizing signal coverage and minimizing error in positioning within a parking lot. An implementation of a number of sample patterns is illustrated. A comparison based on the utilization of the patterns, shows coverage in percentage, average, standard deviation and maximum value of error in estimated positions of vehicles. We propose a new approach which results in improved accuracy and highest performance in positioning and then enhanced tracking of vehicles.

Vehicle Make and Model classification system using bag of SIFT features

Vehicle Make and Model classification is an important part of Intelligent Transportation System. In this work, we have proposed a method based on Linear Support vector machine to solve this problem. Scale Invariant Transform Feature (SIFT) algorithm is used in this work to extract and represent local interest points. Bag of words model is used to represent the local features as fixed length vector to represent an image. The proposed method is evaluated on a publicly available vehicle make and model dataset and promising results are achieved with this method.

Hierarchical networks: Routing and clustering (A concise survey)

The network topologies enhancements are necessary in correspond to the growing of data networks. In this paper, large network problem is described. It includes the routing update cost. The concept of hierarchical network is explained as a considered solution. The hierarchical routing algorithms working methodology and protocols are presented and compared. In addition, it provides a sufficient description for the pros and cons of each protocol. The hierarchical network problems of cluster number, size distribution and number of levels, and solving approaches are discussed. This paper discusses how to identify the best clustering algorithm for wired, ad-hoc and wireless sensor networks. The optimization guidelines are explained.

Real-time Support Vector Machine based Network Intrusion Detection system using Apache Storm

Network intrusion detection is critical component of network management for security, quality of service and other purposes. These systems allow early detection of network intrusion and malicious activities; based on this detection, appropriate actions can be applied to manage these attacks. Several network intrusion detection systems are proposed and evaluated and many of them are currently in use to provide better security. Currently, computer networks are generating high volume of data traffic which cannot be analyzed by most network intrusion detection systems. This situation requires new techniques that can handle huge volume of real time data traffic and it must maintain the high throughput. We have proposed to network intrusion system based on support vector machine in this work. We also propose to use Apache Storm framework; which is a real-time distributed stream processing framework. This network intrusion system is tested for KDD 99 network intrusion dataset.

Security against passive attacks on network coding system – A survey

Abstract Network coding is a progressive information dissemination technology for network communications. Since its emergence at the turn of the millennium, network coding has reached more and more applications. In this survey article, we present a comprehensive review of the researches on secure network coding against passive attack. Studies in this area address problems such as: “If an eavesdropper can wiretap a few packets from a network coding system, how should the system be designed to protect useful information from being divulged?” According to protection strength, we establish a security hierarchy with four grades for network coding systems. For each grade, we collect and investigate existing schemes extensively. The essence of each scheme is expounded, such as key ideas, encryptions, or precoding matrix constructions, etc. Especially, the hardship of cryptanalysis is emphasized. Moreover, advantages and disadvantages are evaluated and compared between these schemes. In spite of single source networks, studies on theoretical possibilities and practical techniques for multisource networks are covered. Countermeasures against traffic analysis attacks are included. Finally, we suggest several open problems and promising methods for the future study.

Support Vector Machine based Vehicle Make and Model Recognition System

A R T I C L E I N F O A B S T R A C T Article history: Received: 01 May, 2017 Accepted: 15 June, 2017 Online: 16 July, 2017 Vehicle analysis is a very useful component in various real world applications. In this paper, we have developed a Vehicle Make and Model Recognition (VMMR) system using Support Vector Machine (SVM). Scale Invariant Feature Transform (SIFT) and Speed-Up Robust Transform (SURF) are used to extract local features from an image. Bag-ofFeatures (BoF) model is used to create visual dictionaries and convert the local image features into global image feature representation. Multiple dictionaries of different sizes are created for both features; SIFT and SURF and the dataset is coded using these dictionaries to determine the best size for the visual dictionary. NTOU-MMR is a publicly available vehicle dataset which we have used to evaluate the performance of proposed VMMR system. 92% recognition rate is achieved by using the proposed VMMR system.

Analysis of Positioning Uncertainty in Vehicular Environment

Applications like automatic safe lane change and adaptive forward collision avoidance are increasingly based on connected vehicles approach. At the heart of communications-based automotive safety applications lies the need for reliable position estimation. Augmenting GPS positioning by utilizing short-range wireless and other on-board sensors improve positioning accuracy. However, the introduction of short-range signals changes the traditional GPS problem configuration and introduces new challenges. In this paper, we investigate issues relevant to positioning accuracy. We show that the lateral component of positioning error presents the majority of the overall error. We analyze the uncertainty space and describe the factors leading to lower accuracy of lateral positioning component aiming at better understanding of components forming the total error.