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.

A Cooperation Strategy Based on Nash Bargaining Solution in Cooperative Relay Networks

Abstract: This paper proposes a cooperation strategy among rational nodes in a wireless cooperative relaying network as an effort to solve two basic problems, i.e., when to cooperate and how to cooperate. First, a symmetric system model comprising two users and an access point (AP) is presented. In this model, each user plays an equal role and acts as a source as well as a potential relay and has the right to decide the amount of bandwidth it should contribute for cooperation. Second, referring to the cooperative game theory, the above problems are formulated as a two-person bargaining problem. Then, a cooperation bandwidth allocation strategy based on the Nash bargaining solution is proposed, in which if a derived condition is satisfied, users will cooperatively work, and each will share a certain fraction of its bandwidth for relaying; otherwise, they will independently work. Simulation results demonstrate that when cooperation takes place, users benefit from the proposed strategy in terms of utility, and those with longer distance to the AP should spend more bandwidth to cooperate with others.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Abstract: The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Multiple Moving Targets Surveillance Based on a Cooperative Network for Multi-UAV

Abstract: With the development of better links, enhanced coverage, comprehensive data resources, and network system stability, the cooperative network formed by wireless sensor networks and unmanned aerial vehicles is envisioned to provide immediate and long-term benefits in military and civilian fields. Previous works mainly focus on how to use UAVs to assist WSNs in sensing and data collection jobs, or target localization with a single data source in surveillance systems, while the potential of multi-UAV sensor networks has not been fully explored. To this end, we propose a new cooperative network platform and system architecture of multi-UAV surveillance. First we propose the design concepts of a multi- UAV cooperative resource scheduling and task assignment scheme based on the animal colony perception method. Second, we provide the moving small target recognition technique and localization and tracking model using the fusion of multiple data sources. In addition, this article discusses the establishment of suitable algorithms based on machine learning due to the complexity of the monitoring area. Finally, experiments of recognition and tracking of multiple moving targets are addressed, which are monitored by multi- UAV and sensors.

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.