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 Friendly and Low-Cost Technique for Capturing Non-Cooperative Civilian Unmanned Aerial Vehicles

Abstract: As a result of continuous cost reduction and device miniaturization, small UAVs are now more easily accessible to the public. Consequently, numerous new applications in the civilian and commercial domains have emerged. However, despite regulations, non-cooperative UAVs have started to abuse low-altitude airspace with potential security and safety problems. In this work, we present a new GNSS spoofing based counter- UAV defense system, which is able to flexibly, friendly, and remotely control a non-cooperating UAV to fly to a location we specify for capture. Our simulation and field study show the effectiveness of such a defense technique.

LPTD: Achieving Lightweight and Privacy-Preserving Truth Discovery in CIoT

Abstract: In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and one-way hash chain techniques. This scheme not only protects devices' privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes.

Intersection Fog-Based Distributed Routing for V2V Communication in Urban Vehicular Ad Hoc Networks

Abstract: Due to the characteristics of urban vehicular ad hoc networks (VANETs), many difficulties exist when designing routing protocols. In this paper, we focus on designing an efficient routing strategy for vehicle-to-vehicle (V2V) communication in urban VANETs. Because, the characteristics of urban VANET routing performance are affected mainly by intersections, traffic lights, and traffic conditions, we propose an intersection-based distributed routing (IDR) strategy. In view of the fact that traffic lights are used to cause vehicles to stop at intersections, we propose an intersection vehicle fog (IVF) model, in which waiting vehicles dynamically form a collection or fog of vehicles at an intersection. Acting as infrastructure components, the IVFs proactively establish multihop links with adjacent intersections and analyze the traffic conditions on adjacent road segments using fuzzy logic. This approach offloads a large part of the routing work. During routing, the IVFs adjust the routing direction based on the real-time position of the destination, thus avoiding rerouting. Each time an IVF makes a distributed routing decision, the IDR model employs the ant colony optimization (ACO) algorithm to identify an optimal routing path whose connectivity is based on the traffic conditions existing in the multihop links between intersections. Because of the high connectivity of the routing path, the model requires only packet forwarding and not carrying when transmitting along the routing path, which reduces the transmission delay and increases the transmission ratio. The presented mathematical analyses and simulation results demonstrate that our proposed routing strategy is feasible and that it achieves relatively high performance.

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.