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.

Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Abstract: In a 5G ultra-dense network, dynamic network topology and traffic patterns lead to excessive system overhead and complex radio resource conflicts. The cloud radio access network and the fog computing have the advantages of high computation capabilities and low transmission delays. Therefore, by taking full advantage of these two characteristics, this study proposes a novel radio resource coordination and scheduling scheme in an ultra-dense cloud-based small cell network. Interference among small cells (or remote radio heads) can be avoided by implementing centralized cooperative processing in the base band unit pool in advance. Resource sharing in coordination and transfer depend on fog computing to relieve the overloaded cloud processing platform and reduce transmission delays, thereby maximizing resource utilization and minimizing system overhead when the network topology and number of users change dynamically. The simulation shows that the proposed scheme can increase the system throughput by 20% compared with the clustering-based algorithm; it can also increase system throughput by 33% compared with the graph coloring algorithm, decrease the signaling overhead by about 50%, and improve network’s quality of service.

Secure Authentication and Key Agreement Protocol for Tactile Internet-based Tele-Surgery Ecosystem

Abstract: With the recent advancements in wireless communications, Tactile Internet (TI) has witnessed a major blow. TI is considered the next big evolution that will provide real-time control in industrial setups, particularly in the domain of tele-surgery. However, in remote-surgery ecosystems the transmission of data is prone to different attack vectors. Thus, to realize the true potential of secure tele-surgery under the umbrella of TI, it is required to design a secure authentication and key agreement protocol for tele-surgery. In this paper, we present an effective and secure mutual authentication and session establishment protocol for TI-driven remote surgery setups. The designed protocol enables secure communications between the surgeon, robotic arm, and the trusted authority (TA); where the protocol leverages the advantages of Elliptic Curve Cryptography (ECC) and biometrics. The protocol operates along the following three phases: i) setup phase, ii) registration phase, and iii) mutual authentication and key agreement phase. During the third phase, the surgeon and the robotic arm mutually authenticate each other with the help of the TA. Further, the security features of the designed protocol have been established using formal and informal means. The obtained results indicate the resiliency of the protocol against offline password guessing attacks, replay attacks, impersonation attacks, man-in-the-middle attacks, denial of service attacks, etc.

Security and privacy in emerging networks: Part II [Guest Editorial]

Abstract: This is the second part of the "Security and Privacy in Emerging Networks" Feature Topic. In Part I, which was published in April 2015, we selected those contributions that dealt with the theory behind the security and privacy of such networks. In Part II, we present articles that overview new security schemes for emerging networks such as vehicular, biomedical, underwater, crowdsourcing, and mobile networks. We feel that even though these emerging networks have attracted many research efforts lately, the security and privacy aspects have not been investigated well. Thus, it is important to provide ways to protect such networks from various security and privacy attacks. The aim of this FT is to promote further research interests in security and privacy in emerging networks by providing a vehicle for researchers and practitioners to discuss research challenges and open issues, and disseminate their latest research results. This can pave the way to implementing emerging networks with the necessary protection from major vulnerabilities. We received a large number of submissions but were obliged to accept only the best 13 papers. Part I was composed of six contributions that dealt with the theory of security/privacy threats, while this issue (Part II) is composed of seven articles addressing security challenges in a specific set of emerging networks.

RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything

Abstract: While the widespread use of ubiquitously connected devices in Internet of Everything (IoE) offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one of the promising solutions to alleviate such problems, is considered as capable of performing data training without exposing raw data that kept by multiple devices. However, either malicious attackers or untrusted servers can deduce users’ privacy from the local updates of each device. Previous studies mainly focus on privacy-preserving approaches inside the servers, which require the framework to be built on trusted servers. In this article, we propose a privacy-enhanced federated learning scheme for IoE. Two mechanisms are adopted in our approach, namely the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism. RR is adopted to prevent the server from knowing whose updates are collected in each round. LADP enables devices to add noise adaptively to its local updates before submitting them to the server. Experiments demonstrate the feasibility and effectiveness of our approach.

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.