scispace - formally typeset
Search or ask a question
Author

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
More filters
Proceedings ArticleDOI
08 Dec 2008
TL;DR: A new cooperative mobility model is presented, developing a cost- benefit framework which enables the impact of cooperation in MANETs where nodes are, to varying extents, willing to move for the common good, and the design of CoopSim, a platform for conducting simulation experiments to evaluate theimpact of parameter, policy and algorithm choices on any system based on the proposed model.
Abstract: Modern mobile ad-hoc networks (MANETs) frequently consist of nodes which exhibit a wide range of autonomy needs. This is particularly true in the settings where MANETs are most compelling, i.e. battlefield, response & rescue, and contexts requiring rapid deployment of mobile users. The time-critical nature of the underlying circumstances frequently requires deployment of both manned and unmanned nodes, and a coordination structure which provides prioritized tasking to them. Unlike consumer MANETs, these settings bring with them a common group purpose, making inter-node cooperation plausible. In this paper, we focus on how cooperation can improve MANET communications. We begin by taxonomizing all prior approaches and noting that no existing approach adequately captures networks where nodes exhibit a wide range of autonomy with respect to their mobility. To this end we present a new cooperative mobility model, developing a cost- benefit framework which enables us to explore the impact of cooperation in MANETs where nodes are, to varying extents, willing to move for the common good. In the second half of the paper, we describe the design of CoopSim, a platform for conducting simulation experiments to evaluate the impact of parameter, policy and algorithm choices on any system based on the proposed model. Finally, we present a small but illustrative case study and use the experimental evidence derived from it to give an initial evaluation of the merits of the proposed model and the efficacy of the CoopSim software.

4 citations

Journal ArticleDOI
01 Feb 2023
TL;DR: C-HealthIER as discussed by the authors is a cooperative health intelligent emergency response system that aims to reduce the time of receiving the first emergency treatment for passengers with abnormal health conditions, and conducts cooperative behavior in response to health emergencies by vehicletovehicle and vehicle-to-infrastructure information sharing to find the nearest treatment provider.
Abstract: The advancement of wireless connectivity in smart cities will enhance connections between their various key elements. Federated intelligent health monitoring systems inside autonomous vehicles will achieve smart cities’ goal of improving the quality of life. This paper proposes a novel cooperative health emergency response system within Cooperative Intelligent Transportation Environment, namely, C-HealthIER. C-HealthIER is a cooperative health intelligent emergency response system that aims to reduce the time of receiving the first emergency treatment for passengers with abnormal health conditions. C-HealthIER continuously monitors passengers’ health and conducts cooperative behavior in response to health emergencies by vehicle-to-vehicle and vehicle-to-infrastructure information sharing to find the nearest treatment provider. A conducted simulation that integrates three different tools (Veins, SUMO, and OMNET++) to simulate the proposed system showed that C-HealthIER reduces the total time to receive the emergency treatment by at least 92.5% and the time to receive the first emergency treatment by at least 73.2% compared to the time taken by AutoPilot mode in self-driving cars. C-HealthIER also reduces the travel distance to the first emergency treatment place by 40.9% and thus reduces the travel time by 43.8% compared to receiving the treatment at the same hospital in the AutoPilot mode.

4 citations

Journal ArticleDOI
TL;DR: The measurement results and channel models provide reference for the design and deployment of the 5G system to exploit the spatial and polarization diversities in the UMa O2I scenario and propose the lifted- superposed Laplace distribution and lifted-superposed normal distribution function to model the APS and EPS.
Abstract: Outdoor-to-indoor (O2I) coverage in urban areas by using the sub-6 GHz (sub-6G) band is important in the fifth generation (5G) mobile communication system. The spatial-temporal propagation characteristics in different polarizations in the 5G spectrum are crucial for the network coverage. In this paper, we measured the urban macrocell (UMa) O2I channels at 3.5 GHz in the space, time, and polarization domains simultaneously. The channel sounder utilized two ±45° polarized antenna arrays. The transmitter (TX) was placed on the rooftop of a five-storey building to emulate a base station and the receiver (RX) was moved in the corridors on different floors in another building to emulate user equipments (UEs). We obtained the small-scale parameters of excess delay, power, and azimuth/elevation of arrival (AoA/EoA) of individual multipath components (MPCs), the propagation profiles of azimuth/elevation power spectrum (APS/EPS) and power delay profile (PDP), and the large-scale parameters including azimuth/elevation spread of arrival (ASA/ESA) and delay spread (DS). Based on the measurement results, we propose the lifted-superposed Laplace distribution (LS-Laplace) function and lifted-superposed normal distribution (LS-Normal) function to model the APS and EPS, respectively, and a three-phase model for the PDP. We find that the ASA and ESA follow the lognormal distribution and the DS has a Rayleigh distribution. We also reveal the impact of surrounding environments and polarization on the channel propagation profiles and statistical characteristics. The measurement results and channel models in this paper provide reference for the design and deployment of the 5G system to exploit the spatial and polarization diversities in the UMa O2I scenario.

4 citations

Proceedings ArticleDOI
01 Dec 2018
TL;DR: A new access control method and forensic framework for user-intent monitoring of cloud-based networked applications in cognitive networks and can function correctly in untrusted environments and is transparent to applications, systems and communication environments is proposed.
Abstract: The cognitive network system learns from the past (situations, plans, decisions, actions) and uses this knowledge to improve the decisions in the future. Scenarios in which data resources for configuring radio-system parameters are stored in the cloud for easily sharing and exchanging between nodes are foreseeable. Due to the physical inaccessibility and limited control, it is always a tough topic to formulate appropriate access control strategies for the cloud data and data access requests submitted by applications are not always correct and credible. Cloud servers cannot clearly confirm that these requests are consistent with a user's original intent. In this paper, we propose a new access control method and forensic framework for user-intent monitoring of cloud-based networked applications in cognitive networks. Our framework has two main functions. Firstly, it makes sure that every data access request submitted by applications is correct. This means that it accurately shows what it wants. Monitoring user-intent can also help the cognitive engine to make decisions in turn. Secondly, it can offer adequate details to help forensic analysts reconstruct a precise view of user interaction with applications and understand system conditions. Our framework can function correctly in untrusted environments and is transparent to applications, systems and communication environments. It incurs no discernible performance overhead.

4 citations

Journal ArticleDOI
01 Sep 2021

4 citations


Cited by
More filters
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