Article
Computer Science, Information Systems
Chandana Roy, Ruelia Saha, Sudip Misra, Dusit Niyato
Summary: This article proposes a software-defined fog architecture, Soft-Health, for IoT-based healthcare applications. By using a wireless body area network for continuous patient monitoring and allocating information to appropriate fog/cloud based on criticality index, the risk of deterioration in patient health can be reduced.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Muhammad Izhar, Syed Asad Ali Naqvi, Adeel Ahmed, Saima Abdullah, Nazik Alturki, Leila Jamel
Summary: This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. It integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing to create a robust and dependable medical ecosystem.
Article
Computer Science, Information Systems
Humberto Jorge De Moura Costa, Cristiano Andre Da Costa, Rodolfo Stoffel Antunes, Rodrigo Da Rosa Righi, Paul Andrew Crocker, Valderi Reis Quietinho Leithardt
Summary: Patient healthcare data is scattered in various locations worldwide, posing a challenge for global identification. A decentralized software model based on blockchain and smart contracts is proposed to address this challenge by providing privacy, unique person identification, and support for multiple document and biometric data combinations. The implementation and evaluation of this model demonstrate its potential in reducing costs, time, and effort in the context of global health threats.
Article
Computer Science, Information Systems
Fan Wu, Chunkai Qiu, Taiyang Wu, Mehmet Rasit Yuce
Summary: The article introduces an edge-based hybrid network system architecture consisting of hybrid routers and an IoT gateway. By utilizing LoRa wireless technology, the coverage of short-range BLE network can be extended to 2.4 km with minimal delay of only 11.5 ms when processing data at the edge.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Mahender Kumar, Satish Chand
Summary: The article proposes an identity-based anonymous authentication and key agreement protocol for WBAN in a cloud-assisted environment, which ensures security and user anonymity. The scheme is proven to be secure under computational diffie-hellman assumption and random oracle model, with low computational and communication costs compared to existing schemes.
IEEE SYSTEMS JOURNAL
(2021)
Article
Computer Science, Information Systems
Kamal Das, Soumen Moulik
Summary: In this article, a method for solving the problem of optimal distribution of slots among sensor devices in wireless body area networks (WBANs) is proposed. WBAN is a fundamental component of IoT-based healthcare, and optimal allocation of data transmission slots in WBANs is a challenge due to diverse resource demands and constraints of the sensors. The proposed cooperative game-theoretic approach based on the Nash bargaining solution (NBS) improves reliability by 26.68% and throughput by 38.07% on average compared to the traditional IEEE 802.15.6 standard.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Multidisciplinary Sciences
Tusharkanta Samal, Manas Ranjan Kabat
Summary: This paper proposes a time-sharing multichannel MAC protocol for wireless body area networks, aiming to achieve real-time and guaranteed delivery of emergency data while maximizing energy efficiency. Simulation results demonstrate that the proposed protocol outperforms existing methods in terms of delay, throughput, and energy efficiency.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Public, Environmental & Occupational Health
Amna Amanat, Muhammad Rizwan, Carsten Maple, Yousaf Bin Zikria, Ahmad S. Almadhor, Sung Won Kim
Summary: Healthcare information is crucial for service providers and patients, and secure sharing and maintenance of Electronic Healthcare Records (EHR) are essential. Blockchain, a distributed ledger technology, can provide secured, validated, and immutable data sharing facilities, ensuring the authenticity and security of healthcare records.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Computer Science, Information Systems
Qinlong Huang, Zhicheng Zhang, Yixian Yang
Summary: The paper introduces a privacy-preserving multi-dimensional media sharing scheme in mobile cloud computing, utilizing attribute-based encryption and multi-level access policy construction to protect media privacy while reducing the complexity of access policies.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Yang Gao, Wenjun Wu, Pengbo Si, Zhaoxin Yang, Fei Richard Yu
Summary: This article introduces a new architecture for resource sharing and transactions in fog computing networks, called B-ReST, enabled by blockchain technology. Key technologies and advantages of B-ReST are discussed, and simulation results demonstrate the benefits of B-ReST in resource sharing and transactions through solving the RPM problem.
IEEE WIRELESS COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Sudip Misra, Pradyumna Kumar Bishoyi, Subhadeep Sarkar
Summary: This article proposes an energy-efficient medium access control (MAC) protocol for IEEE 802.15.6 standard compliant in-body sensor-based WBAN, addressing the issues of collision between emergency and regular events. A modified superframe structure is introduced with separate access phases for emergency and regular events, along with an emergency event handling scheme and priority assignment protocol. Scheduled access mechanism is proposed to minimize collision and improve the performance of in-body sensor MAC in terms of latency and overall power consumption in various event scenarios.
IEEE SYSTEMS JOURNAL
(2021)
Article
Computer Science, Information Systems
Zhengliang Jiang, Wei Liu, Ruijiang Ma, Syed Hamad Shirazi, Yong Xie
Summary: With the rapid economic development and increasing pressure of work and life, there is a rising demand for real-time intelligent healthcare through Healthcare Wireless Body Area Network (HWBAN). However, security, performance, and availability have been identified as deficiencies in existing HWBAN schemes. To address these issues, a lightweight and enhanced secure scheme for HWBAN has been proposed, utilizing Elliptic Curve Cryptography (ECC) operations and Physically Unclonable Function (PUF) to improve security and efficiency. Real-time health status monitoring through mobile phones without additional requests to the medical server is enabled for better availability, with a formal security proof provided to demonstrate compliance with security and reliability requirements. Detailed comparative analysis confirms the scheme's advantages in computing, communication, and security.
Article
Computer Science, Hardware & Architecture
Xiuhua Li, Luxi Cheng, Chuan Sun, Kwok-Yan Lam, Xiaofei Wang, Feng Li
Summary: This article investigates the issue of collaborative data sharing in vehicular edge networks using AI-empowered MEC servers and proposes a novel scheme utilizing deep Q-network and federated learning. Evaluation results demonstrate the effectiveness of the proposed scheme in reducing latency of vehicular data sharing.
Article
Computer Science, Interdisciplinary Applications
Justin A. Reyes, Dan C. Marinescu, Eduardo R. Mucciolo
Summary: This paper explores the exact computation of tensor network contractions on two-dimensional geometries and presents a heuristic improvement to reduce computing time, memory usage, and communication time. The results demonstrate that cloud computing is a viable alternative to supercomputers for scientific applications of this nature.
COMPUTER PHYSICS COMMUNICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Engin Zeydan, Josep Mangues-Bafalluy, Yekta Turk
Summary: This study investigates the management of software defined networking-based transport network and edge cloud service orchestration through a machine learning-based design that effectively manages transport and edge cloud resources of a mobile network. Using the Graphical Network Simulator-3 (GNS3) emulator environment to generate and use real-world data inside the ML platform, the results show that trained ML models can accurately select the correct edge clouds with high test accuracy under the considered two scenarios when transport and EC network parameters are considered.
Article
Computer Science, Artificial Intelligence
Jianfeng Cui, Lixin Wang, Xiangmin He, Victor Hugo C. De Albuquerque, Salman A. AlQahtani, Mohammad Mehedi Hassan
Summary: Feature extraction plays a crucial role in arrhythmia classification. This paper presents a feature extraction method that combines traditional approaches and 1D-CNN to improve the accuracy of arrhythmia classification. Experimental results show that the proposed method achieves an average classification accuracy of 98.35%, surpassing the latest methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Dan Xia, Chun Jiang, Jiafu Wan, Jiong Jin, Victor C. M. Leung, Miguel Martinez-Garcia
Summary: This article provides a survey on heterogeneous networks in smart factories, focusing on access control, fusion, and management in the context of expanding IIoT connectivity. It explores the challenges posed by the contradiction between high QoS requirements and limited network bandwidth in smart factory networks, and discusses existing and future network technologies that can address these challenges. Additionally, it analyzes current network fusion architecture and identifies areas for improvement.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Hardware & Architecture
Md Golam Rabiul Alam, Abde Musavvir Khan, Myesha Farid Shejuty, Syed Ibna Zubayear, Md Nafis Shariar, Meteb Altaf, Mohammad Mehedi Hassan, Salman A. AlQahtani, Ahmed Alsanad
Summary: This paper proposes an automated Ejection Fraction estimation system from 2D echocardiography images using deep semantic segmentation neural networks. Two parallel pipelines of deep semantic segmentation neural network models have been proposed for efficient left ventricle segmentation, and three different neural networks, UNet, ResUNet, and Deep ResUNet, have been implemented. The most accurate model achieved high Dice scores for left ventricle segmentation in both systolic and diastolic states. The proposed system can remove the eyeball estimation practice and reduce inter-observer variability.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad Mehedi Hassan, Salman A. AlQahtani, Abdulhameed Alelaiwi, Joao P. Papa
Summary: This manuscript presents a comparative study of three explainable artificial intelligence (XAI) approaches for COVID-19 diagnosis using deep networks, aiming to improve the interpretability of automated decision-making mechanisms. The authors hope that this work can serve as a basis for further research on XAI and COVID-19 diagnosis, considering the positive and negative aspects of each approach.
NEURAL COMPUTING & APPLICATIONS
(2023)
Retraction
Computer Science, Artificial Intelligence
Ramani Selvanambi, Jaisankar Natarajan, Marimuthu Karuppiah, S. K. Hafizul Islam, Mohammad Mehedi Hassan, Giancarlo Fortino
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Hamza Ghazanfar Toor, Faraz Akram, Saeid Jafari, Md. Zia Uddin, Mohammad Mehedi Hassan
Summary: This article explores the combination of the intuitionistic hesitant fuzzy set (IHFS) and set pair analysis (SPA) theories in multi-attribute decision making (MADM) and presents a hybrid model named intuitionistic hesitant fuzzy connection number set (IHCS). A few averaging and geometric aggregation operators are developed on IHCS to facilitate the design of a novel MADM algorithm. Additionally, the benefits of the proposed work are highlighted through a comparative examination with other models and a graphical interpretation of the devised attempt.
Article
Chemistry, Multidisciplinary
Wajid Ali, Tanzeela Shaheen, Hamza Ghazanfar Toor, Faraz Akram, Md. Zia Uddin, Mohammad Mehedi Hassan
Summary: In today's fast-paced business environment, investment decision making has become more complex due to the uncertainty and ambiguity of financial data. Traditional decision-making models are no longer sufficient, leading to the popularity of fuzzy logic-based models. However, these models have limitations in dealing with complex, multi-criteria decision-making problems. To address this, a novel three-way group decision model is proposed in this paper, incorporating decision-theoretic rough sets and intuitionistic hesitant fuzzy sets. The model provides a more robust and accurate approach for selecting investment policies. Mathematical modeling confirms the effectiveness of the proposed model in comparison to existing methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Jieyu Xie, Jiafu Wan
Summary: This paper introduces the key technology of digital twins in intelligent manufacturing and proposes a digital twin four-dimensional fusion modeling method to solve the application problems of digital twin technology in discrete manufacturing. The proposed method can describe the geometric and physical characteristics of a physical entity, map its behavior mechanism, and reveal the control logic and virtual-real mapping rules, providing important support for virtual-real intelligent mutual control.
BIG DATA AND COGNITIVE COMPUTING
(2023)
Article
Computer Science, Information Systems
Pengfei Du, Xiang He, Haotong Cao, Sahil Garg, Georges Kaddoum, Mohammad Mehedi Hassan
Summary: In the context of Industry 5.0, AI-based logistics UAVs are widely used in intelligent transportation systems due to their advantages. However, most existing logistics UAV delivery models do not consider energy consumption and mixed time windows, making them impractical. This study proposes a cooperative path planning algorithm to minimize the total energy cost of multiple logistics UAVs. The algorithm combines genetic algorithm and large neighborhood search to optimize the route and service allocation. Simulation results show significant reduction in energy cost compared to other optimization algorithms.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Chenjing Tian, Haotong Cao, Yinjin Fu, Sahil Garg, Georges Kaddoum, Mohammad Mehedi Hassan
Summary: The implementation of intelligent device-free sensing (IDFS) requires a large number of edge IoT devices for extended service coverage. Prior to large-scale deployment, a reliable service provision scheme is essential. Network function virtualization (NFV) technologies have facilitated fast and convenient service provision in distributed cloud networks, but ensuring resilient service provision remains a challenge. This paper proposes a mathematical model and a multi-mode VNF backup scheme to optimize resource consumption and guarantee reliability requirements. Additionally, a novel SFC Online Reliability Protection (SORP) scheme is introduced to handle dynamic SFC requests, demonstrating its efficacy in mitigating service failures and reducing bandwidth consumption in high latency scenarios.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Theory & Methods
Xiaobo Yang, Daosen Zhai, Ruonan Zhang, Haotong Cao, Sahil Garg, Mohammad Mehedi Hassan
Summary: In this paper, a human-to-human interaction behaviors sensing method based on the complex-valued neural network (CVNN) is proposed, which can sense human-to-human interaction behaviors through the channel state information (CSI) of Wi-Fi signals. The method analyzes the changing relationship between the position or posture of the human body and the wireless signal and investigates the impacts of different human interaction behaviors on Wi-Fi signal propagation characteristics. A sensing model based on CVNN is designed to realize the sensing of human-to-human interaction behaviors through the CSI. The proposed method can sense 12 kinds of human-to-human interaction behaviors with an overall accuracy of 82%.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Dan Xia, Jianhua Shi, Ke Wan, Jiafu Wan, Miguel Martinez-Garcia, Xin Guan
Summary: This article proposes a DT-based system architecture and a mobile-enhanced edge computing-cloud collaborative mechanism for intelligent planning and deployment of 6G networks, aiming to improve network performance and reduce operational costs.
IEEE WIRELESS COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Jiahui Chen, Hang Xiao, Yushan Zheng, Mohammad Mehedi Hassan, Michele Ianni, Antonella Guzzo, Giancarlo Fortino
Summary: In this paper, a Decentralized Kerberos Secure Service-Management Protocol (DKSM) based on blockchain technology and Ciphertext-policy Attribute-based Encryption (CP-ABE) schema is proposed. Compared with existing protocols, DKSM fulfills decentralization, fine-grained access control with effective cost, and scalability simultaneously. The security of DKSM is also discussed and the protocol's efficiency and cost-effectiveness are demonstrated through tests on the Ethereum testnet and FISCO consortium platform.
INTERNET OF THINGS
(2023)
Article
Engineering, Civil
Prabhat Kumar, Govind P. Gupta, Rakesh Tripathi, Sahil Garg, Mohammad Mehedi Hassan
Summary: The recent growth of IoT technologies in the maritime industry has digitalized Maritime Transportation Systems (MTS), but also introduced cybersecurity threats. Cyber Threat Intelligence (CTI) is an effective security strategy, but existing solutions have low detection rates and high false alarm rates. To address these challenges, an automated framework called DLTIF has been proposed, which can accurately identify threat types.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Xiaoding Wang, Sahil Garg, Hui Lin, Georges Kaddoum, Jia Hu, Mohammad Mehedi Hassan
Summary: This paper proposes a hierarchical trust evaluation strategy based on heterogeneous blockchain, utilizing federated deep learning technology for Intelligent Transportation Systems (ITS) security.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
