About
28
Publications
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Introduction
Dr. Emara is an assistant professor at Faculty of Computer and Information Sciences, Ain Shams University, Egypt. He received his Ph.D. in Computer Science in 2016 from the Technical University of Munich (TUM) with Highest degree of Honor. He was awarded a full scholarship from DAAD to pursue his Ph.D. in Germany. Then, he was a PostDoc Fellow in the Connected Mobility chair at TUM supervised by Prof. Jörg Ott until September 2016. Afterward, he joined the Networking Research Group, in SnT research center in Luxembourg headed by Prof. Thomas Engel. His research interests include mobile networks, in particular, vehicular networks, location privacy and applications of intelligent transportation systems.
Additional affiliations
April 2012 - March 2016
May 2005 - present
Education
April 2012 - March 2016
September 2005 - March 2010
September 2000 - July 2004
Publications
Publications (28)
Preserving location privacy in vehicular ad hoc networks (VANET) is an important requirement for public acceptance of this emerging technology. Many privacy schemes concern changing pseudonyms periodically to avoid linking messages. However, the spatiotemporal information contained in beacons makes vehicles traceable and the driver's privacy breach...
Location privacy in vehicular ad hoc networks has gained considerable attention in the past few years. The majority of studies concern changing pseudonyms to prevent linking messages of the same pseudonym. However, the precise spatiotemporal information included in beacons (i.e., timestamp, position, speed and heading) makes them vulnerable to trac...
Location privacy is one of the main challenges in vehicular ad hoc networks (VANET), which aims to protect vehicles from being tracked. Most of research work concerns changing pseudonyms efficiently to avoid linking messages through them. However, the sensitive information the vehicles send periodically in beacons make them vulnerable to tracking e...
Wireless sensor network (WSN) envisions ubiquitous computing future in many fields, such as environmental monitoring, military surveillance, and inventory tracking. These applications need-in many cases-to interconnect with IP networks. The fact that the WSN has its own application-specific non-standardized protocols presents a significant challeng...
Transfer Learning (TL) has emerged as a powerful approach for improving the performance of Deep Learning systems in various domains by leveraging pre-trained models. It was proven that features learned by deep learning can smoothly be reused across similar domains. Deep transfer learning schemes compensate for limited training data via transfer lea...
Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recog...
There are a myriad of applications where the localization of interior surroundings is vital in the era of smart cities Bluetooth low energy (BLE) technology is designed for short-range wireless communication, low energy consumption, low cost hardware design and simple deployment with respect to other technologies. This paper presents a low cost BLE...
Semantic data integration provides the ability to interrelate and analyze information from multiple heterogeneous resources. With the growing complexity of medical ontologies and the big data generated from different resources, there is a need for integrating medical ontologies and finding relationships between distinct concepts from different onto...
VANET safety applications broadcast cooperative awareness messages (CAM) periodically to provide vehicles with continuous updates about the surrounding traffic. The periodicity and the spatiotemporal information contained in these messages allow a global adversary to track vehicle movements. Many privacy schemes have been proposed for VANET, but on...
Preserving location privacy is an important aspect in vehicular ad-hoc networks. Although location privacy is thoroughly studied in the past decade, it is usually skipped in VANET simulators. In this paper, we propose a location privacy extension, PREXT, for Veins framework. Currently, PREXT supports seven privacy schemes of different approaches in...
Vehicular adhoc networks allow vehicles to share their information for safety and traffic efficiency. However, sharing information may threaten the driver privacy because it includes spatiotemporal information and is broadcast publicly and periodically. In this paper, we propose a context-adaptive pseudonym changing scheme which lets a vehicle deci...
Vehicular adhoc network allows vehicles to exchange their information for safety and traffic efficiency. However, exchanging infor- mation may threaten the driver privacy because it includes spatiotem- poral information and is broadcast publicly on a periodical basis. In this paper, we propose a context-adaptive privacy scheme which lets a vehi- cl...
Location privacy is one of the main challenges in vehicular ad hoc networks (VANET), which aim to protect vehicles from being tracked. Most of research work concern changing pseudonyms effectively to avoid linking messages through them. However, the sensitive information the vehicles send periodically in beacon messages make them vulnerable to trac...
Many applications of wireless sensor network (WSN) require interconnection with IP networks whether for monitoring or control. Such interconnection is not obvious because of the variety of sensor networks architectures and non-standardized protocols. There are many techniques that handle this problem. In this paper, we evaluate a previously propose...
Braille is one of the most important means of written communications between visually-impaired and sighted people, so it gains the research interest. This paper describes a new technique for recognizing Braille characters in Arabic double sided Braille document. The main challenge resolved here is to build up a complete OBR system that is completel...
Questions
Question (1)
Assume we have measurements for a vehicle states retrieved from GPS/INS system which has non-gaussian noise. The vehicle state transition and observation model are linear but the measurement noise here is non-gaussian. Should I use EKF or UKF to have a more optimal results than linear KF? If so, I think the filter matrices will be the same except noise coovariance matrix R, right? If so, how to calculate such matrix if the noise is a gaussian mixture of 2 known gaussian distributions?
