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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.


Papers
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Journal ArticleDOI
TL;DR: Analysis and simulation results indicate that both PFD and PAD schemes can achieve a much higher throughput and a lower packet-collision ratio than IEEE 802.11 distributed coordination function (DCF), conservative CTS reply (CCR), and dual busy-tone multiple-access (DBTMA) schemes.
Abstract: The IEEE 802.11 medium-access control (MAC) protocol is usually considered to be a default standard in multihop wireless networks. However, in a multihop network with a large interference range, the request-to-send/clear-to-send (RTS/CTS) handshake and virtual carrier sensing mechanism may not be able to eliminate interference or solve hidden- and exposed-terminal problems. This paper proposes two new MAC protocols, i.e., the power-fixed dual (PFD) and power-aware dual (PAD) busy-tone schemes, both of which are able to effectively prevent collision of data/acknowledgment (ACK) packets and are applicable in various open-space environments with different path-loss characteristics. Analytical models are developed to evaluate their performance in terms of the blocking area, saturation throughput, and capability of mitigating aggregate interference of simultaneous transmissions. Numerical examples are presented to show the effectiveness of the proposed MAC protocols and the interaction between performance metrics and key parameters. Analysis and simulation results indicate that both PFD and PAD schemes can achieve a much higher throughput and a lower packet-collision ratio than IEEE 802.11 distributed coordination function (DCF), conservative CTS reply (CCR), and dual busy-tone multiple-access (DBTMA) schemes.

12 citations

Journal ArticleDOI
TL;DR: In this paper, the authors show the benefits of reinforcement learning based techniques for resource provisioning in the vehicular cloud, which can perceive long term benefits and are ideal for minimizing the overhead of resources provisioning for vehicular clouds.
Abstract: This article presents a concise view of vehicular clouds that incorporates various vehicular cloud models, which have been proposed, to date. Essentially, they all extend the traditional cloud and its utility computing functionalities across the entities in the vehicular ad hoc network (VANET). These entities include fixed road-side units (RSUs), on-board units (OBUs) embedded in the vehicle and personal smart devices of the driver and passengers. Cumulatively, these entities yield abundant processing, storage, sensing and communication resources. However, vehicular clouds require novel resource provisioning techniques, which can address the intrinsic challenges of (i) dynamic demands for the resources and (ii) stringent QoS requirements. In this article, we show the benefits of reinforcement learning based techniques for resource provisioning in the vehicular cloud. The learning techniques can perceive long term benefits and are ideal for minimizing the overhead of resource provisioning for vehicular clouds.

12 citations

Proceedings ArticleDOI
30 Nov 2009
TL;DR: A novel scheme to effectively provide a Video-on-Demand (VoD) using P2P-based mesh overlay networks is envisioned that aims at dynamically selecting the required contents from the available peers and minimizing the startup latency and sustaining the playback rate to an acceptable level.
Abstract: Due to their ability to overcome many shortcomings associated with the contemporary client-server paradigm, Peer-to-Peer (P2P) networks have attracted phenomenal interests from researchers in both academia and industry. Interactive and multi-media streaming applications using P2P networks are, however, often prone to long startup delays, which disrupt the smooth playback and undermine users' perceived quality of service. In addition, P2P networks must be able to support a potential number of users while ensuring that the resources are efficiently utilized. In this paper, by addressing these shortcomings in the traditional P2P framework, we envision a novel scheme to effectively provide a Video-on-Demand (VoD) using P2P-based mesh overlay networks. The proposed scheme covers two main phases, namely requesting and scheduling modes. The former aims at dynamically selecting the required contents from the available peers. On the other hand, in the scheduling mode, the incoming requests are scheduled in a priority-based manner for minimizing the startup latency and sustaining the playback rate to an acceptable level. Computer simulations have been conducted to verify the effectiveness of the proposed scheme. The obtained results demonstrate the scalability of our envisioned scheme in addition to its capability to reduce the startup delay and provide a sustainable playback rate.

12 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: This paper analyzes the communication scheme implemented in the wireless devices to develop a new protocol that can be used in the remote control-implantable device communication, and that will rely on plain text messages to avoid encryption implementation.
Abstract: Implantable medical devices are being increasingly used to treat or monitor different medical conditions. For such purposes, wireless is the most desired communication scheme to be implemented in these devices. On the other hand, the wireless scheme increases security threats on these electronic devices, and any possibility of attack on the medical device may have lethal consequences. The patients usually have their implantable medical devices configured and monitored by their doctors. But for practical purposes, most of the time they possess a remote control for daily non-critical operations. This remote control can be considered as an open gate for attackers to target those medical devices and cause major harm. Motivated by this, we analyze in this paper the communication scheme implemented in the wireless devices, having as a starting point an Implantable Insulin Pump to develop a new protocol that can be used in the remote control-implantable device communication, and that will rely on plain text messages to avoid encryption implementation. Finally, we will analyze how the novelties introduced with this protocol can secure such a wireless link.

12 citations

Journal ArticleDOI
TL;DR: A prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting and proposes a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment.

12 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

01 Jan 2002

9,314 citations