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.

Towards Dynamic Coordination Among Home Appliances Using Multi-Objective Energy Optimization for Demand Side Management in Smart Buildings

Abstract: In this paper, we propose a home energy management system that employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique, which helps in balancing the load during ON-peak and OFF-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our real-time rescheduling problem as knapsack problem and solve it through dynamic programming. This paper also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing, and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.

Derivation of PAPR Distribution for OFDM Wireless Systems Based on Extreme Value Theory

Abstract: It has been widely known that one of the key design parameters in orthogonal frequency division multiplexing (OFDM) systems is the distribution of peak-to-average power ratio (PAPR). Recently some theoretical approaches to determine the PAPR distribution have been proposed based on the assumption that all subcarriers are allocated with equal power. However, this assumption may not be valid due to the following facts. First, in all realistic OFDM systems, usually only a subset: of subcarriers are used to carry information (active subcarriers) and the rest (inactive subcarriers) are set to zero. Second, due to the efficiency concerns transmission power should be allocated to active subcarriers. Third, power allocation may vary depending on different constellations used by different active subcarriers and their signal-to-noise-ratios. In this paper, we propose a general approach to identify PAPR distribution in OFDM systems. Specifically, a more accurate analytical expression of the PAPR distribution is derived with the help of Extreme Value Theory for Chi-squared-2 process in OFDM systems with unequal power distribution strategy. To validate the analytical results, extensive simulations have been conducted, showing a very good match between the identified PAPR distribution and that of real OFDM systems.

Cross-layer-based modeling for quality of service guarantees in mobile wireless networks

Abstract: In this article we propose a cross-layer approach to investigate the impact of the physical-layer infrastructure on the data-link-layer QoS performance in mobile wireless networks. At the physical layer, we take the MIMO diversity schemes as well its AMC into account. At the data-link layer, our focus is on how this physical-layer infrastructure influences the real-time multimedia QoS provisioning performance such as delay-bound violation and buffer-overflow probabilities. To achieve this goal, we first model the physical-layer service process as a finite state Markov chain. Based on this FSMC model, we then characterize the QoS performance at the data-link layer using the effective capacity approach, which turns out to be critically important for the statistical QoS guarantees in mobile wireless networks. The numerical and simulation results obtained demonstrate that the proposed cross-layer model can efficiently characterize the interaction between the physical layer infrastructure and upper layer protocols' QoS provisioning performance.

A Blockchain-SDN-Enabled Internet of Vehicles Environment for Fog Computing and 5G Networks

Abstract: The goal of intelligent transport systems (ITSs) is to enhance the network performance of vehicular ad hoc networks (VANETs). Even though it presents new opportunities to the Internet of Vehicles (IoV) environment, there are some security concerns including the need to establish trust among the connected peers. The fifth-generation (5G) communication system, which provides reliable and low-latency communication services, is seen as the technology to cater for the challenges in VANETs. The incorporation of software-defined networks (SDNs) also ensures an effective network management. However, there should be monitoring and reporting services provided in the IoV. Blockchain, which has decentralization, transparency, and immutability as some of its properties, is designed to ensure trust in networking platforms. In that regard, this article analyzes the combination of blockchain and SDN for the effective operation of the VANET systems in 5G and fog computing paradigms. With managerial responsibilities shared between the blockchain and the SDN, it helps to relieve the pressure off the controller due to the ubiquitous processing that occurs. A trust-based model that curbs malicious activities in the network is also presented. The simulation results substantially guarantee an efficient network performance, while also ensuring that there is trust among the entities.

Routing protocols for underwater wireless sensor networks

Abstract: Recently, underwater wireless sensor networks (UWSNs) have emerged as a promising networking technique for various underwater applications. An energy efficient routing protocol plays a vital role in data transmission and practical applications. However, due to the specific characteristics of UWSNs, such as dynamic structure, narrow bandwidth, rapid energy consumption, and high latency, it is difficult to build routing protocols for UWSNs. In this article we focus on surveying existing routing protocols in UWSNs. First, we classify existing routing protocols into two categories based on a route decision maker. Then the performance of existing routing protocols is compared in detail. Furthermore, future research issues of routing protocols in UWSNs are carefully analyzed.

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.