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.

Cooperative joint power splitting and allocation approach for simultaneous energy delivery and data transfer

Abstract: In this paper, we propose to minimize the total energy consumption cost of a simultaneous data transmission and power delivery from different sources The receiver is designed to simultaneously process information and harvest energy from the received signal through a power splitter We derive an optimal power allocation and splitting ratios for each source node that minimizes the total power cost while ensuring the required data rates for each link The solution profits from the variability between the channel gains and data requirements Numerical simulations allow to analyze the performance of the proposed solution

Realizing the Tactile Internet through Intelligent Zero Touch Networks

Abstract: The Internet of Things is expected to evolve and create numerous technological advances, paving the road for solutions that were once considered impossible. The Tactile Internet (TI), which is envisioned to enable real-time transmission of haptic and conventional data traffic, is creating that paradigm shift toward control-based communication between end users and machines. Tele-operation, augmented/virtual reality vehicle platooning, and industrial automation are all applications that can be supported by TI. The realization of TI over beyond fifth generation creates challenges for the current wireless communication and networking infrastructure. Network and communication reliability, ultra-high data rate connectivity, ultra-low latency, and stringent quality of service/experience (QoS/QoE) requirements must all be achieved for TI to effectively operate. Existing network infrastructures that require manual means of management and control cannot accommodate stringent TI constraints. With that said, the adaptation of advanced intelligent and cooperative solutions at the edge of the network is a crucial step toward guaranteed availability of resources and TI services. This article identifies and analyzes some of the technical issues that TI faces, then highlights and proposes potential solutions toward zero touch networks. We pay particular attention to the reliability performance gains achieved from cooperative edge devices, and their role in the provisioning of zero touch networking infrastructures that support TI applications.

DWES: A Dynamic Weighted Evaluation System for Scratch based on Computational Thinking

Abstract: Scratch is a programming environment, which is widely adopted as the first language to enter the programming world. Since Scratch projects are rich in multimedia resources, it can be time-consuming to judge them by hand, requiring manual execution with the understanding of each project. Auto-judgment tools come into being, they can rapidly evaluate the project in some way. Unfortunately, the existing tools are too rigid and they ignore the diversity of Scratch projects. To address this issue, we propose a new dynamic weighted evaluation system (DWES). First, we define a new computational thinking (CT) evaluation criteria from eight aspects. Second, based on the proposed criteria, we present an analysis tool that automatically assesses the CT skills of the learners' projects. Third, considering the characteristics of projects, we dynamically adjust the evaluation results according to types, so that a single standard is no longer applied roughly to all. This process is data-driven, we consider the CT performance and scripts of projects. We prove the rationality of the evaluation criteria from the aspect of program complexity. Through the correlation analysis between DWES CT scores and experts' ratings, we find that compared with Dr. Scratch, the correlation coefficient has increased.

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

Abstract: To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review of the impact of DTs and digitalization on cities is made to assess the progression of cities and standardization of their management mode. Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. Relevant literature covering the full range of DTs technologies and their applications is collected, evaluated, and collated, relevant studies are concatenated, and relevant accepted conclusions are summarized by modules. First, the historical process and construction content of a Digital City (DC) under modern demand are analyzed, and the main ideas of a DC design and construction are discussed in combination with the key technology of DTs. Then, the metaverse is the product of the combination of various technologies in different scenes. It is a key component to promote the integration of the real world and the digital world and can provide more advanced technical support in the construction of the DC. DTs urban technology architecture is composed of an infrastructure terminal information center terminal and application server end. Urban intelligent management is realized through physical urban data collection, transmission, processing, and digital urban visualization. The construction of DTs urban platform can improve the city’s perception and decision-making ability and bring a broader vision for future planning and progression. The interactive experience of the virtual world covered by the metaverse can effectively support and promote the integration of the virtual and real, and will also greatly promote the construction of SCs. In summary, this work is of important reference value for the overall development and practical adoption of DTs cities, which improves the overall operation efficiency and the governance level of cities.

A Secure Energy Efficient Scheme for Cooperative IoT Networks

Abstract: A secure energy efficient approach is proposed to connect Internet of Things (IoT) sensors that operate with limited power resources. This is done by optimizing simultaneously the energy efficiency, the communication rate and the network security while limiting the potential data leakage and tracking the finite battery status evolution. The proposed model uses spatial diversity in addition to artificial jamming introduced by an intermediate device to forward the data from the sensors to the destination and to secure the communication links without draining the rechargeable batteries. The energy harvested by the source is also maximized without affecting the security level of the network. The outage secrecy capacity is derived to evaluate the security level. Furthermore, the system power stability is analyzed using Markov chains and statistical approaches to validate the efficiency of the proposed technique in maintaining the system in a self-sufficient mode and making it operate without the assistance of external power resources.

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.