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.

Utility function design for strategic radio resource management games: An overview, taxonomy, and research challenges

Abstract: Radio resource management is important for wireless communication networks. Game theory has been extensively used to model, analyze, and design interactive behaviors and the strategic decision-making for radio resource management. It is known that utility function is one of the critical elements in a game, which characterizes the preferred relationship of the rational players and is a function of the action of players and their opponents. We first overview the basics of game theory and utility functions. We then present a taxonomy of utility functions with respect to different types of players, the nature of actions, and preferences in terms of the fairness, quality of service, and quality of experience. We provide some insights based on the taxonomy of utility functions, which provides the readers with a comprehensive view. Following that, we also discuss other types of traffic-aware utility functions with different fairness and the potential and super modular game-theoretic utility functions. In addition, we summarize the desired properties and observations for the design of suitable utility functions. Finally, we investigate impacts of the pricing in utility functions. This article ends with the conclusions and a promising view on open problems and challenges with possible future research directions.

LCD: Low Latency Command Dissemination for a Platoon of Vehicles

Abstract: In a vehicular platoon, a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control commands to following vehicles based on vehicle-to-vehicle communications. However, reducing command dissemination latency with multiple vehicles while ensuring successful message delivery to the tail vehicle is challenging. We propose a new linear dynamic programming algorithm using backward induction and interchange arguments to minimize the dissemination latency of the vehicles. Furthermore, a closed form of dissemination latency in vehicular platoon is obtained by utilizing Markov chain with M/M/1 queuing model. Simulation results confirm that the proposed dynamic programming algorithm improves the dissemination rate by at least 50.9%, compared to similar algorithms in the literature. Moreover, it also approximates the best performance with the maximum gap of up to 0.2 second in terms of latency.

Discovering communities of malapps on Android-based mobile cyber-physical systems

Abstract: Android-based devices like smartphones have become ideal mobile cyber-physical systems (MCPS) due to their powerful processors and variety of sensors. In recent years, an explosively and continuously growing number of malicious applications (malapps) have posed a great threat to Android-based MCPS as well as users’ privacy. The effective detection of malapps is an emerging yet crucial task. How to establish relationships among malapps, discover their potential communities, and explore their evolution process has become a challenging issue in effective detection of malapps. To deal with this issue, in this work, we are motivated to propose an automated community detection method for Android malapps by building a relation graph based on their static features. First, we construct a large feature set to profile the behaviors of malapps. Second, we propose an E-N algorithm for graph construction by combining epsilon graph and k-nearest neighbor (k-NN) graph. It solves the problem of an incomplete graph led by epsilon method and the problem of noise generated by k-NN graph. Finally, a community detection method, Infomap, is employed to explore the underlying structures of the relation graph, and obtain the communities of malapps. We evaluate our community detection method with 3996 malapp samples. Extensive experimental results show that our method outperforms the traditional clustering methods and achieves the best performance with rand statistic of 94.93% and accuracy of 79.53%.

Channel Measurement and Resource Allocation Scheme for Dual-Band Airborne Access Networks

Abstract: The aerial base stations (ABSs) can be quickly deployed to provide emergency communications and airborne network infrastructures. How to ensure wide coverage, reliable links, and high throughput for ground users under the conditions of limited onboard power supply, large propagation distance, and restricted frequency resource is a critical and challenging issue. In this work, we propose a hybrid-spectrum scheme for ABS-based airborne access networks, named dual-band aerial access (DBAA) where the ABS employs both the UHF and S-bands to provide connectivity for ground users. The DBAA can improve coverage range and reliability by taking advantage of the preferable radio propagation characteristics of the low-frequency band and meanwhile improve network throughput by exploiting the large spectrum bandwidth in the high-frequency band. Following the cross-layer approach, we first conducted a measurement campaign on the large-scale fading of the air-to-ground (A2G) channels at 785 and 2160 MHz simultaneously. We installed an ABS with two antennas on an airship that hovered at several altitudes from 50 to 950 m. We measured the signal power attenuation from the ABS to a ground terminal that moved in rural, suburban, and urban scenarios with the horizontal distance up to 70 km from the airship. Based on the measurement data, we establish the large-scale fading channel model for ABS at different operating frequencies. Then, we design the joint spectrum-and-power allocation algorithm to maximize the network throughput for the dual-band airborne access network. We evaluate the performance of the optimal resource allocation based on the proposed channel model. The simulation results show that the DBAA scheme with the optimal resource allocation can achieve substantial performance improvement in comparison with the single-band solution given the total spectrum bandwidth and onboard power supply.

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.