Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.

A Lightweight and Privacy-Preserving Authentication Protocol for Mobile Edge Computing.

Abstract: With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for real-time data processing and analysis has emerged as an essential pre-requite for customers' satisfaction. In this direction, Mobile Edge Computing (MEC) provides seamless services with reduced latency, enhanced mobility, and improved location awareness. Since MEC has evolved from Cloud Computing, it inherited numerous security and privacy issues from the latter. Further, decentralized architectures and diversified deployment environments used in MEC platforms also aggravate the problem; causing great concerns for the research fraternity. Thus, in this paper, we propose an efficient and lightweight mutual authentication protocol for MEC environments; based on Elliptic Curve Cryptography (ECC), one-way hash functions and concatenation operations. The designed protocol also leverages the advantages of discrete logarithm problems, computational Diffie-Hellman, random numbers and time-stamps to resist various attacks namely-impersonation attacks, replay attacks, man-in-the-middle attacks, etc. The paper also presents a comparative assessment of the proposed scheme relative to the current state-of-the-art schemes. The obtained results demonstrate that the proposed scheme incurs relatively less communication and computational overheads, and is appropriate to be adopted in resource constraint MEC environments.

VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach

Abstract: In this paper, we design a multimodal framework for object detection, recognition and mapping based on the fusion of stereo camera frames, point cloud Velodyne LIDAR scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages (BSMs) that are exchanged using Dedicated Short Range Communication (DSRC). We merge the key features of rich texture descriptions of objects from 2D images using Convolutional Neural Networks (CNN). In addition, depth and distance between objects are provided by the 3D LIDAR point cloud and the awareness of hidden vehicles is achieved from BSMs' beacons. We present a joint pixel to point cloud and pixel to V2V correspondence of objects in frames of driving sequences in the KITTI Vision Benchmark Suite. We achieve this by using a semi-supervised manifold alignment approach to achieve camera-LIDAR and camera-V2V mapping of their recognized persons and cars that have the same underlying manifold.

Design of Protograph-LDPC-Based BICM-ID for Multi-Level-Cell (MLC) NAND Flash Memory

Abstract: In this letter, the optimization of protograph-low-density parity-check (LDPC)-based bit-interleaved coded modulation with iterative detection and decoding (BICM-ID) with anti-Gray mapping is investigated over multi-level-cell (MLC) NAND flash-memory channels. Since the existing protograph-based extrinsic information transfer (PEXIT) algorithm is not applicable to the BICM-ID MLC flash-memory channels, a voltage-sensing PEXIT (VS-PEXIT) algorithm is proposed to facilitate the threshold analysis of protograph codes. Exploiting the proposed VS-PEXIT algorithm, we find that the optimal AR4JA code over AWGN channels cannot preserve its superiority over BICM-ID MLC flash-memory channels. To tackle this problem, we further propose a design scheme to construct a high-rate protograph code, referred to as optimized accumulate-repeat-accumulate (OARA) code , tailored for such scenarios. Theoretical analyses and simulation results illustrate that the proposed OARA-based BICM-ID appears to be an excellent storage scheme over MLC flash-memory channels.

How to Govern the Non-Cooperative Amateur Drones?

Abstract: In recent years, amateur UAVs have become more and more popular. This is due to the decreasing cost and growing range of applications such as weather monitoring, cargo transport and recreational purposes. However, non-cooperative UAVs may cause abuses of low-altitude airspace with potential security and safety problems, posing increasing challenges for low-altitude airspace management. In this article, we analyze the mechanisms to counter UAVs. Then GNSS spoofing on a UAV is described and our experimental results are reported. Also, a novel method to build a no-fly zone for non-cooperative drones is proposed.

Privacy and incentive mechanisms in people-centric sensing networks

Abstract: Leveraging on the ubiquity and increasing number of smartphone users, people-centric sensing is a new computing paradigm that enables distributed data collection by voluntary participants, using the rich sensing capabilities of smartphones. In this article we identify the challenges on two important issues for a successful people-centric sensing system, i.e., privacy and incentives, because if either there is no incentive to participate or participants' privacy is invaded, smartphone owners will be reluctant to participate. Then we review some recent works that address these two issues. Finally, we suggest directions for future work on people-centric sensing by describing several open issues.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.