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.

Mixed RF/FSO Relaying Systems with Hardware Impairments

Abstract: In this work, we provide a detailed analysis of a dual-hop fixed gain (FG) amplify-and-forward relaying system, consisting of a hybrid radio frequency (RF) and free-space optical (FSO) channels. We introduce an impairment model which is the soft envelope limiter (SEL). Additionally, we propose the partial relay selection (PRS) protocol with outdated channel state information (CSI) based on the knowledge of the RF channels in order to select one relay for the communication. Moreover, the RF channels of the first hop experience Rayleigh fading while we propose a unified fading model for the FSO channels, called the unified Gamma Gamma (GG), taking into account the atmospheric turbulence, the path loss and the misalignment between the transmitter and the receiver aperture also called the pointing error. Novel closed-forms of the outage probability (OP), the bit error probability (BEP) and the average ergodic capacity (EC) are derived in terms of Meijer-G and Fox-H functions. Capitalizing on these metrics, we also derive the asymptotical high signal-to-noise ratio (SNR) in order to get engineering insights into the impacts of the hardware impairments and the system parameters as well. Finally, using Monte Carlo simulations, we validate numerically the derived mathematical formulations.

Verifying cloud service-level agreement by a third-party auditor

Abstract: In this paper, we study the important issue of verifying service-level agreement SLA with an untrusted cloud and present an SLA verification framework that utilizes a third-party auditor TPA. A cloud provides users with elastic computing and storage resources in a pay-as-you-go way. An SLA between the cloud and a user is a contract that specifies the computing resources and performances that the cloud should provide to the user. A cloud service provider CSP has incentives to cheat on the SLA, for example, providing a user with less central processing unit and memory resources than specified in the SLA, which allows the CSP to support more users and make more profits. A malicious CSP can easily disrupt the existing SLA monitoring/verification techniques by interfering with the monitoring/measurement process. A TPA resolves the trust dilemma between a CSP and its users. Under the TPA framework and the untrusted-cloud threat model, we design two effective testing algorithms that can detect an SLA violation of the virtual machine memory size. Using real experiments, we demonstrate that our algorithms can detect cloud cheating on a virtual machine's memory size i.e., SLA violations. Furthermore, we show that our testing algorithms can defend various attacks from a malicious CSP, which tries to hide an SLA violation. Copyright © 2013 John Wiley & Sons, Ltd.

DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems

Abstract: In Internet-of-Vehicles (IoV) ecosystems, intelligent toll gates (ITGs) connect nearby metropolitan cities through smart highways. At ITGs, existing solutions integrate blockchain (BC) and deep-learning schemes to leverage trusted and responsive analytics support for connected smart vehicles (CSVs) at ITGs. BC eliminates third-party intermediaries, and secures payments between vehicle owners (VO) and governing authorities (GA). Deep-Learning, on the other hand, facilitates accurate predictions for diverse and complex urban traffic conditions. However, due to fixed toll pricing schemes based on connected smart vehicles (CSV) type, VOs suffer from variable delays at different lanes due to dynamic congestion scenarios. To address the research gaps of such a fixed pricing schemes, we propose a BC-envisioned scheme DwaRa , that operates in three phases. In the first phase, future traffic is predicted based on Markov queues to balance the congestion at different lanes at ITGs efficiently. Then, we propose a novel spatially induced-long-short term memory (SI-LSTM) model to predict current traffic and weather based on historical repositories. Second, based on inputs by the Markov model, SI-LSTM , lane type, and vehicle type, a dynamic pricing algorithm is presented to improve the quality of experience (QoE) of the VO. Finally, based on dynamic price fixation between the VO and the GA, smart contracts (SCs) are executed and transactional data is secured through BC. The proposed scheme is compared against parameters like average mean-squared error (MSE), predicted traffic, scalability, interplanetary file system (IPFS) storage, computation (CC), and communication cost (CCM). At $n$ = 100 test samples, and arrival rate $\beta$ = 80, the obtained MSE is 0.0012, with a peak average value of 0.00526. The overall CC is 45.88 milliseconds (ms) and CCM is 53 bytes that indicate the proposed scheme efficacy against conventional approaches.

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.