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.

Efficient rekeying algorithms for WiMAX networks

Abstract: In this paper, we study the rekeying issue in IEEE 802.16e WiMAX networks. The existing rekeying scheme—the Multicast and Broadcast Rekeying Algorithm (MBRA) unicasts new keys to each subscriber station (SS). This scheme does not scale well since it incurs large communication overheads when the number of SSs increase. In our work, first we propose a general tree-based rekeying scheme, which is more efficient than the MBRA. We also formulate an optimization problem to determine the optimal tree structure for given number of SSs. Furthermore, we present a novel and efficient rekeying scheme for WiMAX networks. Our new rekeying scheme utilizes efficient security schemes and the WiMAX network application feature. Both analysis and performance evaluation show that our rekeying scheme can significantly reduce the communication overheads. Copyright © 2009 John Wiley & Sons, Ltd.

Novel Hybrid Physical Layer Security Technique in RFID Systems

Abstract: In this paper, we propose a novel PHY layer security technique in radio frequency identification (RFID) backscatter communications system. In order to protect the RFID tag information confidentiality from the eavesdroppers attacks, the proposed technique deploys beam steering (BS) using a one dimensional (1-D) antenna array in the tag side in addition to noise injection from the reader side. The performance analysis and simulation results show that the new technique outperforms the already-existing noise injection security technique and overcomes its design limitations.

Cross-Layer Aware Routing Approaches for Lifetime Maximization in Rate-Constrained Wireless Sensor Networks

Abstract: This paper proposes medium contention aware routing schemes for rate-constrained traffic in wireless sensor networks (WSNs) that maximize network lifetime. Three sufficient conditions, referred to as rate-based, degree-based, and mixed constraints, are incorporated into the routing formulations to guarantee medium access feasibility of the routing solutions. Simulations show that the mixed condition based approach always achieves better network lifetimes than the other two approaches for larger networks, but at the cost of larger execution times. Our results show that the rate-based condition approach is the best choice in terms of balancing between solution quality and complexity.

MDBV: Monitoring Data Batch Verification for Survivability of Internet of Vehicles

Abstract: Along with the development of vehicular sensors and wireless communication technology, Internet of Vehicles (IoV) is emerging that can improve traffic efficiency and provide a comfortable driving environment. However, there is still a challenge how to ensure the survivability of IoV. Fortunately, this goal can be achieved by quickly verifying real-time monitoring data to avoid network failure. Aggregate signature is an efficient approach to realize quick data verification. In this paper, we propose a monitoring data batch verification scheme based on an improved certificateless aggregate signature for IoV, named MDBV. The size of aggregated verification message is remaining roughly constant even as the increasing number of vehicles in MDBV. Additionally, MDBV is proved to be secure in the random oracle model assuming the intractability of the computational Diffie–Hellman problem. In consideration of the network survivability and performance, the proposed MDBV can decrease the computation overhead and is more suitable for IoV.

A cross-layer design for QoS support in the 3GPP2 wireless systems

Abstract: Due to the non-stationary wireless links and the rapid increase of new types of applications that require different levels of QoS parameters, the support for better QoS for the emerging 3GPP2 multimedia wireless applications is strongly needed. These parameters include: priority, delay, data loss, and data rate. Many possible combinations of system parameters have been specified in the current 1/spl times/EV-DV standard. However, there are no specifications about the dynamic resource allocations, i.e. the selection of optimal combination of system parameters such as the number of Walsh codes, the number of time slots, the modulation scheme, and the channel code rate. In this paper, a suite of cross-layer design modules for QoS support in the 1/spl times/EV-DV system is proposed. Our proposed design is composed of several modules build on top of each other. They include the priority admission control which admits users according to their QoS priority profiles, the resource allocation control module which is responsible for allocating the optimal combination of all system parameters, and the resource scheduling control module which aims at achieving a better overall throughput gain and guarantees the QoS requested by different users' service levels. The proposed dynamic resource allocation scheme is based on the effective capacity concept. Performance evaluation of the proposed cross-layer design has been carried out under a mix of traffic types.

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.