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.

Security in the Internet of Things Supported by Mobile Edge Computing

Abstract: While applications of the heterogeneous Internet of Things (IoT) proliferate, their performance requirements in terms of latency and jitter become more stringent and difficult to be met by the traditional cloud computing paradigm. As a result, a new paradigm called multi-access edge computing has emerged in recent years. It complements the centralized cloud platform by providing additional resources at the edge of radio access networks, and provides better support of IoT applications. One of the challenges of supporting IoT applications in mobile edge computing (MEC) is security. In this article, we will scrutinize the security issues of two IoT applications that can exploit the benefits of MEC. The first one is an environment perception system based on an industrial IoT network. The second one, which is gaining momentum fast, is mobile IoT based on a network of unmanned aerial vehicles.

An enhanced public key infrastructure to secure smart grid wireless communication networks

Abstract: It is expected that the smart grid will radically add new functionalities to legacy electrical power systems. However, we believe that this will in turn introduce many new security risks. With the smart grid's backbone communication networks and subnetworks, there are possible scenarios when these subnetworks can become vulnerable to attacks. Ensuring security in these networks is challenging because most devices are resource constrained. In addition, different protocols that are used in these networks use their own set of security requirements. In this article, the security requirements of smart grid communication networks are firstly identified. We then point out that although public key infrastructure (PKI) is a viable solution, it has some difficulties to satisfy the requirements in availability, privacy preservation, and scalability. To complement the functions of PKI, we introduce some novel mechanisms so that those security requirements can be met. In particular, we propose a mechanism to efficiently resist Denial-of-Service (DoS) attacks, and some suggestions to the security protocol design for different application categories.

Backscatter-Enabled Efficient V2X Communication With Non-Orthogonal Multiple Access

Abstract: Non-orthogonal multiple access (NOMA) and backscatter communications are considered to be promising technologies for beyond the fifth-generation (5G) due to their applications in large-scale Internet-of-things networks for providing low-powered and spectral-efficient communication. NOMA also provides a new way to enable vehicle-to-everything (V2X) networks and improve the achievable rates through cooperation. Motivated by these developments, we provided a novel analysis for NOMA-enabled backscatter-based V2X networks. Specifically, we consider that several vehicles are connected to the base station (BS) via different roadside units (RSUs) and the backscatter tags along the road. These backscatter tags can be considered ultra-low-powered safety sensors that communicate with the vehicles using the same spectrum resources. To optimize the performance of such networks, a joint problem of optimal power allocation at BS and RSUs has been formulated. Subsequently, the problem has been transformed into a convex problem and solved using KKT conditions and sub-gradient methods. To evaluate the performance of the proposed solution, Monte-Carlo simulations have been performed in MATLAB. The acquired results clearly demonstrate that the proposed approach performs better than the conventional suboptimal NOMA scheme and joint optimal TDMA scheme.

Cognitive radio network management

Abstract: The regulatory agencies have been pushed by the increasing demand for wireless ubiquitous connectivity to be ever more aggressive in providing innovative ways to use spectra efficiently. Driven by these new opportunities, the future radio systems should look for the optimized architecture, circuit, and algorithm as a whole. In this article, it has been investigated both the unique merits and the challenging tasks of combining policy-based management philosophy with cognitive radio technologies. Some example analysis have also been discussed. It is highly expected that the appropriate combination of policy-based management and CR will lead future wireless communications.

Aggregate Hardware Impairments Over Mixed RF/FSO Relaying Systems With Outdated CSI

Abstract: In this paper, we propose a dual-hop radio-frequency (RF)/free-space optical system with multiple relays employing the decode-and-forward and amplify-and-forward with a fixed gain relaying scheme. The RF channels are subject to a Rayleigh distribution while the optical links experience a unified fading model emcopassing the atmospheric turbulence that follows the Malaga distribution (or also called the $\mathcal {M}$ -distribution), the atmospheric path loss, and the pointing error. Partial relay selection with outdated channel state information is proposed to select the candidate relay to forward the signal to the destination. At the reception, the detection of the signal can be achieved following either heterodyne or intensity modulation and direct detection. Many previous attempts neglected the impact of the hardware impairments and assumed ideal hardware. This assumption makes sense for low data rate systems but it would no longer be valid for high data rate systems. In this paper, we propose a general model of hardware impairment to get insight into quantifying its effects on the system performance. We will demonstrate that the hardware impairments have small impact on the system performance for low signal-to-noise ratio (SNR), but it can be destructive at high SNR values. Furthermore, analytical expressions and upper bounds are derived for the outage probability and ergodic capacity while the symbol error probability is obtained through the numerical integration method. Capitalizing on these metrics, we also derive the high SNR asymptotes to get valuable insight into the system gains, such as the diversity and the coding gains. Finally, analytical and numerical results are presented and validated by the Monte Carlo simulation.

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.