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.

Graph Mapping Offloading Model Based On Deep Reinforcement Learning With Dependent Task

Abstract: In order to solve the problem of task offloading with dependent subtasks in mobile edge computing (MEC), we propose a graph mapping offloading model based on deep reinforcement learning (DRL). We model the user's computing task as directed acyclic graph (DAG), called DAG task. Then the DAG task is converted into a topological sequence composed of task vectors according to the custom priority. And the model we proposed can map the topological sequence to offloading decisions. The offloading problem is formulated as a Markov decision process (MDP) to minimize the trade-off between latency and energy consumption. The evaluation results demonstrate that our DRL-based graph mapping offloading model has better decision-making ability, which proves the availability and effectiveness of the model.

Comparative simulation for physical layer key generation methods

Abstract: The paper cogitates about a comparative simulation for various distillation, reconciliation, and privacy amplification techniques that are used to generate secure symmetric physical layer keys. Elementary wireless model of two mobile nodes in the presence of a passive eavesdropper is used to perform the comparison process. Important modifications are proposed to some phases' techniques in order to increase the performance of the generation process as a whole. Different metrics were used for comparison in each phase, in the distillation phase, we use the Bit Mismatch Rate (BMR) for different SNR values to compare various extracted random strings of the two intended nodes. On the other hand, the messaging rate and process complexity is exploited to estimate the performance of the compared techniques in both reconciliation and privacy amplification phases. The randomness and entropy properties of the keys are verified using the NIST suite, all the generated keys are 128 bits, it is shown that the success rate of the keys passing the randomness tests depends strongly on the techniques that are used through the three generation phases.

Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks

Abstract: Recently, the growing demand for rich multimedia content such as Video on Demand (VoD) has made the data transmission from content delivery networks (CDN) to end-users quite challenging. Edge networks have been proposed as an extension to CDN networks to alleviate this excessive data transfer through caching and to delegate the computation tasks to edge servers. To maximize the caching efficiency in the edge networks, different Mobile Edge Computing (MEC) servers assist each others to effectively select which content to store and the appropriate computation tasks to process. In this paper, we adopt a collaborative caching and transcoding model for VoD in MEC networks. However, unlike other models in the literature, different chunks of the same video are not fetched and cached in the same MEC server. Instead, neighboring servers will collaborate to store and transcode different video chunks and consequently optimize the limited resources usage. Since we are dealing with chunks caching and processing, we propose to maximize the edge efficiency by studying the viewers watching pattern and designing a probabilistic model where chunks popularities are evaluated. Based on this model, popularity-aware policies, namely Proactive caching policy (PcP) and Cache replacement Policy (CrP), are introduced to cache only highest probably requested chunks. In addition to PcP and CrP, an online algorithm (PCCP) is proposed to schedule the collaborative caching and processing. The evaluation results prove that our model and policies give better performance than approaches using conventional replacement policies. This improvement reaches up to 50% in some cases.

Power allocation analysis for dynamic power utility in cognitive radio systems

Abstract: The focus of this paper is to investigate the fundamental limits of power allocation when taking into account a dynamic power pricing scheme. This paper proposes an optimal power allocation analysis for wireless systems when real time power pricing is available. We propose to minimize the total power consumption cost while ensuring minimum individual and total throughput limits. We consider different models for the power pricing function. Analytic solutions for the power allocation are derived for each model. The derived solutions are shown to be modified versions of the water-filling solution. Low-complexity algorithms are proposed for the resource allocation with each pricing model. Performance comparison and pricing effect are shown through simulations.

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.