Nima Jafari Navimipour

Jafari Navimipour, Nima

Name Variants

Jafari Navimipour,Nima
JAFARI NAVIMIPOUR, Nima
N. Jafari Navimipour
Jafari Navimipour, Nima
Jafari Navimipour,N.
J.,Nima
JAFARI NAVIMIPOUR, NIMA
Jafari Navimipour, N.
Nima Jafari Navimipour
Nima JAFARI NAVIMIPOUR
Jafari Navimipour, NIMA
Jafari Navimipour N.
NIMA JAFARI NAVIMIPOUR
J., Nima
Nima, Jafari Navimipour
Navimipour, Nima Jafari
Navimipour, N.J.
Navimpour, Nima Jafari
Navımıpour, Nıma Jafarı

Job Title

Doç. Dr.

Email Address

nima.navimipour@khas.edu.tr

Main Affiliation

Computer Engineering

Full item page

Scholarly Output

86

Articles

69

Citation Count

32

Supervised Theses

1 Scholarly Output Search Results

Now showing 1 - 10 of 12

Citation - WoS: 27
Citation - Scopus: 34
A Comprehensive and Systematic Literature Review on the Big Data Management Techniques in the Internet of Things
(Springer, 2023) NaghibnAff, Arezou; Jafari Navimipour, Nima; 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 - WoS: 8
Citation - Scopus: 18
Fault-Tolerant Load Balancing in Cloud Computing: a Systematic Literature Review
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Mohammadian, Vahid; Jafari Navimipour, Nima; 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 - Scopus: 7
A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm
(IEEE Computer Society, 2023) Zanbouri, K.; Jafari Navimipour, Nima; Al-Khafaji, H.M.R.; Jafari Navimipour, N.; Yalcin, S.
The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT. IEEE
Citation - WoS: 3
Citation - Scopus: 2
A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm
(Springer, 2022) Tabrizi, Sara Ghiasi; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Danesh, Amir Seyed; Yalcin, Senay
The Internet of Things (IoT) enables intelligent and heterogeneous things to access the Internet and subsequently interact and share info. A service management methodology is required by growing IoT applications and the number of services supplied by various objects. Nevertheless, making decisions, finding, and choosing a service is complex. Therefore, numerous techniques are explored in this regard. This paper employed Flower Pollination Algorithm (FPA) for service discovery and selection in IoT. The FPA is a nature-inspired algorithm that mimics flowering plant pollination behavior. Through a hand-over probability, it is possible to adjust the balance between local and global search properly. The survival of the fittest and the optimal reproducing plants regarding numbers are parts of an optimum plant reproduction strategy. These elements are optimization-oriented and constitute the FPA's basics. The suggested methodology has an excellent performance in minimizing data access time, energy usage and optimizing cost according to simulation findings.
Citation - WoS: 34
Citation - Scopus: 38
Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green Iot-Edge Scenarios
(Mdpi, 2022) Heidari, Arash; Jafari Navimipour, Nima; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari; Akbarpour, Shahin
The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja's simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.
Citation - WoS: 2
A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm
(Ieee Computer Soc, 2023) Jafari Navimipour, Nima; Al-Khafaji, Hamza Mohammed Ridha; Navimipour, Nima Jafari; Yalcin, Senay
The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This article proposes a novel fog-based multimedia transmission scheme for the IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The quantum genetic algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves the packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like genetic algorithm, particle swarm optimization, heterogeneous earliest finish time, and ant colony optimization. Consequently, it provides a more efficient scheme for multimedia transmission in the IoMT.
Citation - WoS: 8
Citation - Scopus: 13
Cloud Healthcare Services: a Comprehensive and Systematic Literature Review
(Wiley, 2022) Rahimi, Morteza; Jafari Navimipour, Nima; 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 - WoS: 89
Citation - Scopus: 106
Machine Learning Applications in Internet-Of Systematic Review, Recent Deployments, and Open Issues
(Assoc Computing Machinery, 2023) Heidari, Arash; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Unal, Mehmet; Zhang, Guodao
Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles ( UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.
Citation - WoS: 4
Citation - Scopus: 5
A Yarn-Based Energy-Aware Scheduling Method for Big Data Applications Under Deadline Constraints
(Springer, 2022) Shabestari, Fatemeh; Jafari Navimipour, Nima; Rahmani, Amir Masoud; Navimipour, Nima Jafari; Jabbehdari, Sam
Hadoop is a distributed framework for processing big data. One of the critical parts of Hadoop is YARN, which carries out scheduling and resource management. A scheduling algorithm should consider multiple objectives. However, YARN schedulers do not consider the Service Level Agreement (SLA) and the energy-related issues. The present paper proposes an energy-efficient deadline-aware model for the scheduling problem. The scheduling issue is an NP-hard problem regarding the deadline of applications and reducing energy. Hence, an Energy-efficient Deadline-aware Scheduling Algorithm based on the Moth-Flame Optimization algorithm (EDSA-MFO) is suggested to minimize the energy consumption and execute the application within a given soft deadline. Moreover, the earliest deadline first-based (EDF-based) heuristic approach is proposed to decode a moth into a scheduling solution. The algorithm is implemented for both static and dynamic scheduling. To evaluate the performance of the proposed algorithm, extensive simulations are conducted. The outcomes demonstrated that the suggested method could find near-optimal scheduling. It outperforms the YARN default FIFO scheduler, EDF, the energy-aware greedy algorithm (EAGA), and the deadline-aware energy-efficient MapReduce scheduling algorithm for YARN (EMRSAY) in total cluster energy consumption and meeting job deadline.
Citation - WoS: 113
Citation - Scopus: 146
Applications of Ml/Dl in the Management of Smart Cities and Societies Based on New Trends in Information Technologies: a Systematic Literature Review
(Elsevier, 2022) Heidari, Arash; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Unal, Mehmet
The goal of managing smart cities and societies is to maximize the efficient use of finite resources while enhancing the quality of life. To establish a sustainable urban existence, smart cities use some new technologies such as the Internet of Things (IoT), Internet of Drones (IoD), and Internet of Vehicles (IoV). The created data by these technologies are submitted to analytics to obtain new information for increasing the smart societies and cities' efficiency and effectiveness. Also, smart traffic management, smart power, and energy management, city surveillance, smart buildings, and patient healthcare monitoring are the most common applications in smart cities. However, the Artificial intelligence (AI), Machine Learning (ML), and Deep Learning (DL) approach all hold a lot of promise for managing automated activities in smart cities. Therefore, we discuss different research issues and possible research paths in which the aforementioned techniques might help materialize the smart city notion. The goal of this research is to offer a better understanding of (1) the fundamentals of smart city and society management, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that require further investigation and consideration. IoT, cloud computing, edge computing, fog computing, IoD, IoV, and hybrid models are the seven key emerging de-velopments in information technology that, in this paper, are considered to categorize the state-of-the-art techniques. The results indicate that the Conventional Neural Network (CNN) and Long Short-Term Memory (LSTM) are the most commonly used ML method in the publications. According to research, the majority of papers are about smart cities' power and energy management. Furthermore, most papers have concentrated on improving only one parameter, where the accuracy parameter obtains the most attention. In addition, Python is the most frequently used language, which was used in 69.8% of the papers.