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.

ATM Architectures Using Optical Technology: An Overview of Switching, Buffering and Multiplexing

Abstract: This overview presents recent studies on photonic switches and discusses the existing different types, such as space-division switches, free-space switches, time-division switches, wavelength division switches, and frequency division switches. The architectures and applications of these switches are also discussed. © 1997 John Wiley & Sons, Ltd.

Joint Multi-Task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT

Abstract: For multi-task mobile edge computing (MEC) systems in satellite Internet of Things (IoT), there are dependencies between different tasks, which need to be collected and jointly offloaded. It is crucial to allocate the computing and communication resources reasonably due to the scarcity of satellite communication and computing resources. To address this issue, we propose a joint multi-task offloading and resource allocation scheme in satellite IoT to improve the offloading efficiency. We first construct a novel resource allocation and task scheduling system in which tasks are collected and decided by multiple unmanned aerial vehicles (UAV) based aerial base stations, the edge computing services are provided by satellites. Furthermore, we investigate the multi-task joint computation offloading problem in the framework. Specifically, we model tasks with dependencies as directed acyclic graphs (DAG), then we propose an attention mechanism and proximal policy optimization (A-PPO) collaborative algorithm to learn the best offloading strategy. The simulation results show that the A-PPO algorithm can converge in 25 steps. Furthermore, the A-PPO algorithm reduces cost by at least 8.87$\%$ compared to several baseline algorithms. In summary, this paper provides a new insight for the cost optimization of multi-task MEC systems in satellite IoT.

A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking

Abstract: Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer and adaptive layer, is initiated at the data layer and the control layer of SDN, respectively. The proposed model is reactive and lightweight for DoS mitigation in SDNs.

Full-Duplex Quadrature Spatial Modulation for Multi-Antenna Systems

Abstract: Devices with full-duplex (FD) radios are able to transmit and receive at the same time without requiring orthogonal resources, thereby creating strong self-interference (SI) that results from their own transmissions. Spatial modulation (SM), on the other hand, is a multi-antenna scheme that activates only one transmit antenna to send a data symbol, where the index of the activated antenna depends on the input bits. In this manner, additional data can be conveyed implicitly via the selection of the active transmit antenna. Quadrature spatial modulation (QSM) is a variant of SM, in which the quadrature components of the constellation symbol are separately modulated on the indices of the transmit antenna, leading to an increase in the data rate by implicitly encoding on the spatial domain. This article provides an overview of the research activity on FD-SM and proposes a novel FD-QSM scheme that exploits multiple antennas to achieve antenna cancellation at the receiving side to mitigate the SI signal. Assuming active cancellation mechanisms are also in place, the performance of FD-QSM is studied in the presence of residual SI (RSI). The results reveal that FD-QSM can provide more than 40 percent capacity gain over its half-duplex (HD) counterpart in the presence of strong RSI and roughly the same gain over HD spatial multiplexing (SMX)-based multiple-input-multiple-output (MIMO) systems with moderate RSI. When applied to the downlink of a cellular network, FD-QSM provides 2dB gain over FD-SM and 5dB gain over FD-MIMO, operating at the same spectral efficiency, while huge gains are observed when FD-QSM is applied to non-orthogonal multiple access (NOMA)-aided FD relay network.

Quantifying caching effects in urban VANETs

Abstract: Most applications in urban Vehicular Ad hoc NETworks (VANETs) rely on information sharing, such as real-time traffic information queries, advertisements, etc. However, existing data dissemination techniques cannot guarantee satisfactory performance when a lot of information requests come from all around the network. Because these pieces of information are useful for multiple users located in various positions, it is beneficial to spread the cached copies around. Existing work proposed caching mechanisms and conducted simulations for validation, but there is a lack of theoretical analysis on the explicit caching effects. In this paper, we present the cache coverage ratio as the metric to quantify the caching effects, and theoretical analysis is given based on reasonable assumptions for urban VANETs, through which we find the affecting factors include vehicle density, transmission range, ratio of caching vehicles, etc. We deduce the quantitative relationship among them, which have similar forms as the cumulative density function of an exponential distribution. We conduct intensive simulations, which verify the theoretical analysis results match quite well with the simulated reality under different scenarios.

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.