scispace - formally typeset
Search or ask a question
Author

Atif Alamri

Bio: Atif Alamri is an academic researcher from King Saud University. The author has contributed to research in topics: Cloud computing & Quality of service. The author has an hindex of 37, co-authored 198 publications receiving 5387 citations. Previous affiliations of Atif Alamri include University of Ottawa & Ottawa University.


Papers
More filters
Journal ArticleDOI
TL;DR: The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
Abstract: The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

682 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor- Cloud platform including its definition, architecture, and applications.
Abstract: Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper.

396 citations

Journal ArticleDOI
17 Apr 2017-Energies
TL;DR: In this paper, the authors proposed an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities.
Abstract: Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

197 citations

Journal ArticleDOI
TL;DR: A cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare is proposed.
Abstract: With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.

195 citations

Journal ArticleDOI
TL;DR: A voice pathology detection system is proposed inside the monitoring framework using a local binary pattern on a Mel-spectrum representation of the voice signal, and an extreme learning machine classifier to detect the pathology.
Abstract: The integration of the IoT and cloud technology is very important to have a better solution for an uninterrupted, secured, seamless, and ubiquitous framework. The complementary nature of the IoT and the could in terms of storage, processing, accessibility, security, service sharing, and components makes the convergence suitable for many applications. The advancement of mobile technologies adds a degree of flexibility to this solution. The health industry is one of the venues that can benefit from IoT–Cloud technology, because of the scarcity of specialized doctors and the physical movement restrictions of patients, among other factors. In this article, as a case study, we discuss the feasibility of and propose a solution for voice pathology monitoring of people using IoT–cloud. More specifically, a voice pathology detection system is proposed inside the monitoring framework using a local binary pattern on a Mel-spectrum representation of the voice signal, and an extreme learning machine classifier to detect the pathology. The proposed monitoring framework can achieve high accuracy of detection, and it is easy to use.

191 citations


Cited by
More filters
01 Jan 2002

9,314 citations

01 Jan 2012

3,692 citations

Journal ArticleDOI
TL;DR: This survey makes an exhaustive review of wireless evolution toward 5G networks, including the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN, and underlying novel mm-wave physical layer technologies.
Abstract: The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

2,624 citations