Browsing by Author "Hosseinzadeh, Mehdi"

Now showing 1 - 5 of 5

Citation Count: 7
Cloud healthcare services: A comprehensive and systematic literature review
(Wiley, 2022) Rahimi, Morteza; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Moattar, Mohammad Hossein; Darwesh, Aso
Over the last decade, the landscape of cloud computing has been significantly changed. It has been known as a paradigm in which a shared pool of computing resources is accessible for users. The rapid growth of the healthcare environment provides better medical services to reduce costs and increase competition among healthcare providers. Despite its crucial role in the cloud, no thorough study exists in this domain. This article presents a systematic study for healthcare services in the cloud environment. A well-organized overview of all the databases has been explored. By clustering the research goals of the found papers, we have derived four main research groups. We have further evaluated the papers concerning the background of the paper, QoS parameters, application area, or methods used for applying and formulating the main ideas presented in the works. This survey emphasizes the challenges, needs, benefits of using cloud computing in healthcare systems and provides a comprehensive and detailed study on cloud healthcare services, strengths, and weaknesses of the existing methods. Highlighting cloud health services can be the major focus of research for developing the urban healthcare system.
Citation Count: 10
A comprehensive and systematic literature review on the big data management techniques in the internet of things
(Springer, 2023) NaghibnAff, Arezou; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Sharifi, Arash
The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.
Citation Count: 11
An Energy-Aware IoT Routing Approach Based on a Swarm Optimization Algorithm and a Clustering Technique
(Springer, 2022) Sadrishojaei, Mahyar; Navimipour, Nima Jafari; Reshadi, Midia; Hosseinzadeh, Mehdi
The Internet of Things (IoT) comprises many nodes dispersed around a particular target region, and it has lately been applied in a variety of sectors such as smart cities, farming, climatology, smart metering, waste treatment, and others. Even though the IoT has tremendous potential, some difficulties must be addressed. When building the clustering and routing protocol for huge-scale IoT networks, uniform energy usage and optimization are two significant concerns. Clustering and routing are well-known NP-hard optimization challenges applied to the IoT. The ease with which chicken can be implemented has garnered much interest compared to other population-based metaheuristic algorithms in solving optimization problems in the IoT. Aiming to reduce and improve node energy consumption in the IoT network by choosing the most suitable cluster head, the current effort seeks to extend the life of a network by selecting the most appropriate cluster head. A new cost function for homogenous dispersion of cluster heads was proposed in this research, and a good balance among exploration and exploitation search skills to create a node clustering protocol based on chicken search. This procedure is a big step forward from previous state-of-the-art protocols. The number of packets received, the total power consumption, the number of active nodes, and the latency of the suggested integrated clustered routing protocol are all used to evaluate the protocol's overall performance. The proposed strategy has been demonstrated to improve power consumption by at least 16 percent.
Citation Count: 4
Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Mohammadian, Vahid; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Darwesh, Aso
Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to identify under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, makespan, reliability, resource utilization, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.
Citation Count: 0
A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering
(Pergamon-elsevier Science Ltd, 2024) Zhong, Chongzhou; Darbandi, Mehdi; Nassr, Mohammad; Latifian, Ahmad; Hosseinzadeh, Mehdi; Navimipour, Nima Jafari
Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.