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 lightweight privacy-preserving protocol using chameleon hashing for secure vehicular communications

Abstract: Many services and applications in vehicular ad-hoc networks (VANETs) require preserving and secure data communications. To improve driving safety and comfort, the traffic-related status information will be broadcasted regularly and shared among drivers. Without the security and privacy guarantee, attackers could track their interested vehicles by collecting and analyzing their traffic messages. Hence, anonymous message authentication is an essential requirement of VANETs. On the other hand, when a vehicle is involved in a dispute event of warning message, the certificate authority should be able to recover the real identity of this vehicle. To deal with this issue, we propose a new privacy-preserving authentication protocol with authority traceability using elliptic curve based chameleon hashing. Compared with existing schemes, our approach possesses the following features: (1) mutual and anonymous authentication, (2) unlinkability, (3) authority tracking capability and (4) high efficiency. We also demonstrate the merits of our proposed scheme through extensive security analysis and performance evaluation.

Architecture of an all-optical circuit-switched multistage interconnection network

Abstract: An architecture of an all-optical multistage interconnection network is proposed. The network supports a circuit-switching model of communication and can provide parallel optical paths among input and output ports. It uses an address-based routing algorithm for path setup which, due to its decentralized nature, makes this network suitable for designing high-speed switching systems. These switches are commonly used in telephony and multiprocessor systems. The proposed architecture uses bistable optical devices, such as interference filters, as essential components of its switching modules. Since these devices can be easily fabricated, the implementation of this architecture is feasible. Various design issues related to optical clock generation, its distribution, data synchronization, and intensity restoration are also discussed. >

A Novel Control Plane Optimization Strategy for Important Nodes in SDN-IoT Networks

Abstract: One of the crucial technologies for future Internet of Things (IoT) is software defined networks, which provides a centralized and programmable control ability for operators. However, the current single control plane deployed on the remote IoT gateway may incur a bottleneck with the continuous growth of IoT devices and applications. In this paper, we propose a two-level hierarchy (the master and slave) control framework to resolve this problem. Additionally, a novel slave controller placement strategy (SCPS) is presented to further optimize the control performance. In SCPS, we first design a synthetic IoT node importance assessment model based on an improved analytic hierarchy process and fuzzy integral. It considers the device attributes, service attributes, and control frequency. Then we formulate the slave controller placement as a binary integer program problem. It is solved by a modified binary particle swarm optimization algorithm to optimize the control delay and control cost of critical IoT nodes. Finally, we carry out extensive experiments to evaluate the performance of our strategy. The results show that, compared to other competing methods, it approximately reduces the control delay of important IoT nodes by 30.56%.

Advanced Activity-Aware Multi-Channel Operations1609.4 in VANETs for Vehicular Clouds

Abstract: The Dedicated Short Range Communication (DSRC) technology has been used in Vehicular communication to enable short-lived safety and non-safety applications based on Vehicle to Vehicle (V2V) communications over the Control Channel (CCH) and Vehicle to Infrastructure (V2I) communications over the Service Channels (SCHs) in Vehicular Ad hoc Networks (VANETs). With the under-utilized advanced computation, communication and storage resources in On-Board Units (OBUs) of modern vehicles, Vehicular Clouds (VCs) are used to manage coalitions of affordable resources in Vehicles in order to host infotainment applications used by other vehicles on the move. In this paper, we introduce an Advanced Activity-Aware (AAA) scheme for Multi-Channel Operations based on 1609.4 in MAC Protocol in Wireless Access in Vehicular Environments (WAVE). The AAA aims at dynamically achieving an optimal setup of Control Channel Interval (CCHI) and Service Channel Interval (SCHI) by reducing the inactivity interval while maintaining a default Synchronization Interval (SI) between all vehicles. We evaluate the performance of our proposed scheme through real-time simulation of vehicular cloud load and VANET communications using NS3. The simulation results indicate that our proposed scheme increases significantly the throughput and reduces the average delay of uploaded packets of non-safety applications from the VC to the Road Side Unit (RSU) while maintaining a V2V communication for safety similar to that of the 1609.4 standard.

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.