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.

Energy-aware rate and description allocation optimized video streaming for mobile D2D communications

Abstract: The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

Zero-Chain: A Blockchain-Based Identity for Digital City Operating System

Abstract: The challenges of population management as urban density increase globally have compelled researchers and developers to consider more efficient means of managing resources in cities. Consequently, the smart city concept has emerged as a response to addressing the challenge of optimal resource utilization in urban centers. However, with digital technologies proliferating as key components of the solution, it is necessary to develop a digital identity solution for all components of the smart city environment. For completeness, the solution must encompass all entities, including physical and intangible assets, processes, and most importantly, its residents. Consequently, a unified, distributed data integration and efficient analysis platform is required: the digital city operating system. In this article, we focus on a key component of digital city management in the form of secure identification of individual residents. We collect user attributes and securely transmit them to other system components for verification. Upon successful completion of the verification process, a digital identity is created for the applying resident and the set of transactions leading to the ID creation are stored in the blockchain. Our system is secure and can serve as the basis for the development of a digital infrastructure for smart city management.

Key-Policy Attribute-Based Encryption With Keyword Search in Virtualized Environments

Abstract: Cloud computing is a model for convenient, on-demand network access to virtualized environments of configurable computing resources. It is challenging to search data encrypted and stored in cloud storage servers. Searchable encryption enables data users to search on ciphertext without leaking any information about keywords and the plaintext of the data. Currently, a number of searchable encryption schemes have been proposed, but most of them provide unlimited search privileges to data users, which is not desirable in certain scenarios. In this paper, we propose a new construction of searchable encryption with fine-grained access control by using key-policy attribute-based cryptography to generate trapdoors to support AND, OR and threshold gates. The main idea is that the data owner encrypts the index keywords according to the specified access policy. The data user can generate a trapdoor to search on data, if and only if the attributes of the data user satisfy the access policy. We provide formal security proofs for the scheme, including the indistinguishability of ciphertexts and the indistinguishability of trapdoors, which are used to resist the chosen keyword attack and the keyword guessing attack of external adversaries. Comprehensive security analysis and implementation results show that the proposed scheme is provably secure and feasible in real-world applications.

Toward Intelligent Network Optimization in Wireless Networking: An Auto-Learning Framework

Abstract: In wireless communication systems (WCSs), the network optimization problems (NOPs) play an important role in maximizing system performance by setting appropriate network configurations. When dealing with NOPs by using conventional optimization methodologies, there exist the following three problems: human intervention, model invalidity, and high computation complexity. As such, in this article we propose an auto-learning framework to achieve intelligent and automatic network optimization by using machine learning (ML) techniques. We review the basic concepts of ML, and propose their rudimentary employment models in WCSs, including automatic model construction, experience replay, efficient trial and error, RL-driven gaming, complexity reduction, and solution recommendation. We hope these proposals can provide new insights and motivation in future research for dealing with NOPs in WCSs by using ML techniques.

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.