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.

Cooperative Secondary Encryption for Primary Privacy Preserving in Cognitive Radio Networks

Abstract: Aiming at protecting primary privacy messages and supporting secondary quality-of-service (QoS), we propose a secondary encrypted secure strategy for cognitive radio networks. In this scheme, a primary system directly transmits privacy messages or employs pre-transmitted secure secondary messages to encrypt the primary privacy information, and the secondary system acquires a fraction of the interference-free licensed spectrum. Following this idea, we consider two secure communication scenarios: the non-buffer scenario and the buffer-aided scenario. For the non-buffer scenario, the primary system first evaluates the channel quality of the direct transmission link. Then, the primary transmitter adaptively chooses to directly transmit the privacy messages or employ the encryption of the secure secondary messages according to the evaluation results. For this scenario, we investigate the primary ergodic secrecy performance and the secondary average performance. For the buffer-aided scenario, the secure secondary messages can be stored in the buffers at both the primary transmitter and receivers. According to the buffer states and channel quality, the primary system adaptively chooses to directly transmit the primary privacy information, permit the secondary secure transmission, or utilize the encryption of the stored secure secondary information. For this scenario, we also investigate the performances of both the primary and secondary systems, and derive the closed-form expression of the primary information delay. Numerical results are given to prove that the proposed scheme can provide privacy preserving for the primary information and acquire high secondary average transmission rate.

Optimal Spectral Resource Allocation and Pricing for 5G and Beyond: A Game Theoretic Approach

Abstract: Optimal allocation of the available spectrum is a crucial requirement of 5G and Beyond (B5G) for achieving higher Quality of Service (QoS) and low-latency. However, in 5G and Beyond, this requirement presents a potential need for dimensioning and managing the spectral resource in the cellular services. In this article, we address the issues of spectral distribution using DAG and Vickrey Clarke Groves (VCG) mechanisms by evaluating with a derived parameter for sustainable revenue and social welfare of the entire network. In particular, we modelled a framework to optimize social welfare of the users and the revenue of the cellular operator by proposing an efficient spectral allocation and pricing mechanism.

Failure Prediction Based on Multi-Scale Frequent Anomalous Behavior Identification in Support of Autonomic Networks

Abstract: In this paper, we present a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of individual network parameters. The proposed Frequent Anomalous Behavior Mining (FABM) algorithm utilizes multiple levels of time-scale analysis to reveal the frequent anomalous behaviors. This makes the proposed algorithm robust to unreliable, redundant, incomplete and contradictory information. FABM is simple, has low order polynomial computational complexity of O(n2), the patterns identified by FABM require space complexity of O(n) to be stored in the knowledge base of the prediction engine, provides quick and accurate response and can be easily adapted to a distributed environment. Moreover, the empirical results gathered show that using FABM an efficient prediction engine can be realized with high true positive and true negative rates.

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

Abstract: A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the DT-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92 and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for DT-based surgical implementation.

Energy efficient antenna selection for a MIMO relay using RF energy harvesting

Abstract: Energy harvesting has emerged as a promising technique which helps to increase the sustainability of wireless networks. In this paper, we consider a network with a single source, single destination and a single relay equipped with multiple antennas. We develop an optimization framework that exploits the energy harvested from the source radio frequency signals with smart antenna selection schemes at the relay node. Our main target is to minimize the source power and the antennas' circuit power jointly subject to quality of service constraints on the rate. To overcome the computational complexity of the optimization problem, we propose two special case schemes, namely Fixed Source Power Antenna Selection (FSP-AS) and All Receive Antenna Selection (AR-AS). Also, we suggest two sub-optimal heuristic schemes with low complexity and compare their performance with the optimization problem solutions numerically. The simulation results show the gain of our optimal scheme in terms of energy efficiency, which can be up to 80% as compared to solutions proposed in the literature.

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.