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.

Enabling Mobile and Wireless Technologies for Smart Cities: Part 2

Abstract: The articles in this special section focus on communications technologies for use in smart cities. Due to advancements in communication and computing technologies, smart cities have become the main innovation agenda of research organizations, technology vendors, and governments. To make a city smart, a strong communications infrastructure is required for connecting smart objects, people, and sensors. Smart cities rely on wireless and mobile technologies for providing services such as healthcare assistance, security and safety, real-time traffic monitoring, and managing the environment, to name a few. Such applications have been a main driving force in the development of smart cities. Without the appropriate communication networks, it is really difficult for a city to facilitate its citizens in a sustainable, efficient, and safer manner/environment. Considering the significance of mobile and wireless technologies for realizing the vision of smart cities, there is a need to conduct research to further investigate the standardization efforts and explore different issues/challenges in wireless technologies, mobile computing, and smart environments.

Rethinking Wireless Communication Security in Semantic Internet of Things

Abstract: Semantic communication is an important component in the next generation of wireless networking. Enabled by this novel paradigm, the conventional Internet-of-Things (IoT) is evolving toward the semantic IoT (SIoT) to achieve significant system performance improvements. However, traditional wireless communication security techniques for bit transmission cannot be applied directly to the SIoT that focuses on semantic information transmission. One key reason is the lack of new security performance indicators. Thus, we have to rethink the wireless communication security in the SIoT. As such, in this article, we analyze and compare classical security techniques, such as physical layer security, covert communications, and encryption, from the perspective of semantic information security. We highlight the differences among these security techniques when applied to the SIoT. Novel performance indicators, such as semantic secrecy outage probability (for physical layer security techniques) and detection failure probability (for covert communication techniques) are proposed. Considering that semantic communications can raise new security issues, we then review attack and defense methods at the semantic level. Lastly, we present several promising directions for future secure SIoT research.

Optimal power allocation for smart-grid powered point-to-point cognitive radio system

Abstract: This paper proposes an optimal power allocation analysis for a point-to-point wireless system when powered by a smart grid. We propose to minimize the total power consumption cost while ensuring individual and total throughput constraints. The power cost is computed based on different dynamic pricing models of the power consumption. Analytical solutions are derived for each pricing model. The derived solutions are shown to be modified versions of the water-filling solution. Water-filling based algorithms are proposed for the resource allocation with each pricing model. Performance comparison and pricing effect are shown through simulations.

BIRD-VNE: Backtrack-avoidance virtual network embedding in polynomial time

Abstract: The virtual network embedding (VNE) problem is known to be NP-hard, and as a result, several heuristic approaches have been proposed to solve it These heuristics find sub-optimal solutions in polynomial time, but have practical limitations, low acceptance rates, and high embedding costs In this paper, we first propose two heuristics that exploit the constraint propagation properties of the VNE problem to ensure both topological and capacity disjoint consistencies, thereby avoiding backtracking while increasing acceptance rates Then, combining these two heuristics, we design a polynomial-time VNE algorithm (we term it BIRD-VNE) that, in addition to avoiding backtracking and increasing acceptance rates, incurs a low embedding cost when compared to existing approaches

Bayesian-Based Game Theoretic Model to Guarantee Cooperativeness in Hybrid RF/FSO Mesh Networks

Abstract: In this paper we describe an effective new technique by which to guarantee cooperativeness in Hybrid Radio-Frequency/Free Space Optics (RF/FSO) networks. Our approach is based on a novel Bayesian game-theoretic model, and uses a pricing scheme in which each destination node pays some amount of virtual money to the source node in order to acquire a reliable connection. We describe both single-stage and multi-stage solutions for the game in terms of its Nash and Perfect Bayesian Equilibriums. Pure strategies are found when the required conditions are met; otherwise the game is played as a mixed-strategy. Our numerical results quantify the inherent tradeoffs involved in changing the game's parameters vis-a-vis the equilibrium player strategies and game's outcomes.

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.