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.

Partial Relay Selection For Hybrid RF/FSO Systems with Hardware Impairments

Abstract: In this paper, we investigate the performance analysis of dual hop relaying system consisting of asymmetric Radio Frequency (RF)/Free Optical Space (FSO) links. The RF channels follow a Rayleigh distribution and the optical links are subject to Gamma-Gamma fading. We also introduce impairments to our model and we suggest Partial Relay Selection (PRS) protocol with Amplify-and-Forward (AF) fixed gain relaying. The benefits of employing optical communication with RF, is to increase the system transfer rate and thus improving the system bandwidth. Many previous research attempts assuming ideal hardware (source, relays, etc.) without impairments. In fact, this assumption is still valid for low-rate systems. However, these hardware impairments can no longer be neglected for high-rate systems in order to get consistent results. Novel analytical expressions of outage probability and ergodic capacity of our model are derived taking into account ideal and non-ideal hardware cases. Furthermore, we study the dependence of the outage probability and the system capacity considering, the effect of the correlation between the outdated CSI (Channel State Information) and the current source-relay link, the number of relays, the rank of the selected relay and the average optical Signal to Noise Ratio (SNR) over weak and strong atmospheric turbulence. We also demonstrate that for a non-ideal case, the end-to-end Signal to Noise plus Distortion Ratio (SNDR) has a certain ceiling for high SNR range. However, the SNDR grows infinitely for the ideal case and the ceiling caused by impairments no longer exists. Finally, numerical and simulation results are presented.

An Efficient and Privacy-Preserving Energy Trading Scheme Based on Blockchain

Abstract: Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy protection. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transaction called PP-BCTS (Privacy- Preserving Blockchain Trading Scheme). It can achieve fine-grained access control through transaction arbitration in ciphertext form. This design can maximize the protection of private information and can greatly improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCTS, which can greatly improve the operational efficiency. Security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.

Cluster-based Cooperative Multicast for Multimedia Data Dissemination in Vehicular Networks

Abstract: With the development of communication technologies, vehicular network applications have evolved from basic traffic safety and efficiency applications to information and entertainment applications The implementation of emerging vehicular applications is based on the efficient dissemination of multimedia data In view of the dynamic topology changes, severe channel fading and limited spectrum resources of vehicular networks, how to achieve efficient multimedia data dissemination in the harsh network environment is an urgent problem Based on the hybrid cellular-D2D vehicular network, this paper proposes a cluster-based cooperative multicast scheme The scheme combines multicast transmission with D2D-assisted relay technology to provide high-quality data dissemination for vehicle users under limited spectrum resources In this paper, we innovatively present a communication quality index that considers multiple performance factors and formulate the relay selection problem as the anti p-center problem in graph theory Then we propose a heuristic method to solve the problem The results show that the proposed scheme can effectively improve the utilization of wireless resources and the success rate of data dissemination

Space-Ground Integrated Information Network Enabled Internet of Vehicles: Architecture and Key Mechanisms

Abstract: Despite the emerging network technologies such as 5G, edge computing, and cloud computing growing vigorously, it is difficult for terrestrial networks to provide quality services for IoV and other daily communications at the same time. With the advancement of satellite communication technology, more and more people are paying attention to the integration of satellite networks and ground networks. In this article, we propose a space-ground integrated information network based Internet of Vehicles (SGIIN-IoV) architecture supporting multilink access, which provides a good example for the application of the SGIIN in the IoV. In SGIIN-IoV, high delay-tolerant data will be transmitted through satellite links, avoiding using ultra low-latency links. Afterward, we propose a multilayer resource awareness (MLRA) mechanism to achieve full-dimensional monitoring in SGIIN-IoV, which consists of an intravehicle service layer, a link detection layer, and a global monitor layer. Further, we design an adaptive multilink data transmission (AMDT) mechanism to improve utilization of heterogeneous links in SGIIN-IoV. Finally, we verify the two mechanisms, MLRA and AMDT, and the results show that the proposed design can provide higher reliability for data transmission in time.

A Simple Approach for Securing IoT Data Transmitted over Multi-RATs

Abstract: In an mHealth remote patient monitoring scenario, usually control units/data aggregators receive data from the body area network (BAN) sensors then send it to the network or “cloud” The control unit would have to transmit the measurement data to the home access point (AP) using WiFi for example, or directly to a cellular base station (BS), eg, using the long-term evolution (LTE) technology, or both (eg, using multi- homing to transmit over multiple radio access technologies (Multi-RATs) Fast encryption or physical layer security techniques are needed to secure the data In fact, during normal conditions, monitoring data can be transmitted using best effort transmission However, when real-time processing detects an emergency situation, the current monitoring data should be transmitted real-time to the appropriate medical personnel in emergency response teams In this paper, a fast and secure approach for transmitting monitoring data over multi-RATs is proposed The presented approach consists of benefiting of the presence of multi-RATs in order to exchange the secrecy information more efficiently while optimizing the transmission time

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.