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Alqarni MA. Secure UAV adhoc network with blockchain technology. PLoS One 2024; 19:e0302513. [PMID: 38718032 PMCID: PMC11078392 DOI: 10.1371/journal.pone.0302513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 04/05/2024] [Indexed: 05/12/2024] Open
Abstract
Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency.
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Affiliation(s)
- Mohammed A. Alqarni
- Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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2
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Pham CH, Huynh-The T, Sedgh-Gooya E, El-Bouz M, Alfalou A. Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107854. [PMID: 37865060 DOI: 10.1016/j.cmpb.2023.107854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 02/27/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
BACKGROUND AND OBJECTIVE The Internet of medical things is enhancing smart healthcare services using physical wearable sensor-based devices connected to the Internet. Machine learning techniques play an important role in the core of these services for remotely consulting patients thanks to the pattern recognition from on-device data, which is transferred to the central servers from local devices. However, transferring personally identifiable information data to servers could become a source for hackers to steal from, manipulate and perform illegal activities. Federated learning is a new branch of machine learning that creates directly training models from on-device data and aggregates these learned models on the servers without centralized data. Another way to protect data confidentiality on computer systems is data encryption. Data encryption transforms data into another form that only users with authority to a decryption key can read. In this work, we propose a novel method enabling preservation of client privacy and protection of client biomedical data from illegal hackers while transmitting through the Internet. METHODS We propose a method applying 3-dimensional convolutional neural networks for human activity recognition using multiple sensory data. In order to protect the data, we apply the bitwise XOR operator encryption technique. Then, we extend our 3-dimensional convolutional neural network methods to both traditional federated learning and the federated learning based on multi-key homomorphic encryption using the proposed encrypting data. RESULTS Based on leave-one-out-cross-validation, the 3-dimensional method obtains an accuracy of 94.6% and of 94.9% (without data encrypting and without federated learning) tested on two different benchmarked datasets, Sport and DaLiAC respectively. Accuracy is decreased slightly to 89.5% (from 94.6% of the baseline) when we use the proposed encrypting data method. However, the encryption-data-based method still has a potential result compared to the state-of-the-art which only uses raw data. In addition, the proposed full federated learning scheme of this work shows that illegal persons who somehow can get the trained model transmitted via networks cannot infer the private result. CONCLUSIONS This novel method for sensory data representation which translates temporal and frequency bio-signal values to voxel intensities that can encode 3-dimensional activity images. Secondly, the proposed 3-dimensional convolutional neural network methods outperform other deep-learning-based human activity recognition approaches. Finally, extensive experiments show the proposed data-encrypted federated learning approach can achieve feasibility in terms of efficiency in privacy preservation.
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Affiliation(s)
| | - Thien Huynh-The
- Department of Computer and Communication Engineering, Ho Chi Minh City University of Technology and Education, Viet Nam.
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3
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Moulahi W, Jdey I, Moulahi T, Alawida M, Alabdulatif A. A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data. Comput Biol Med 2023; 167:107630. [PMID: 37952305 DOI: 10.1016/j.compbiomed.2023.107630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023]
Abstract
The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field's ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study's findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97.11% and an average federated learning accuracy of 93.95%.
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Affiliation(s)
- Wided Moulahi
- Faculty of sciences and Techniques of Sidi Bouzid, University of Kairouan, Tunisia; REsearch Groups in Intelligent Machines (LR11ES48), Tunisia
| | - Imen Jdey
- Faculty of sciences and Techniques of Sidi Bouzid, University of Kairouan, Tunisia; REsearch Groups in Intelligent Machines (LR11ES48), Tunisia.
| | - Tarek Moulahi
- Department of Information Technology, College of Computer, Qassim University, Kingdom of Saudi Arabia
| | - Moatsum Alawida
- Department of Computer Sciences and Information Technology, Abu Dhabi University, 59911, Abu Dhabi, United Arab Emirates
| | - Abdulatif Alabdulatif
- Department of Computer science, College of Computer, Qassim University, Buraydah, Kingdom of Saudi Arabia
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Hasnain M, Albogamy FR, Alamri SS, Ghani I, Mehboob B. The Hyperledger fabric as a Blockchain framework preserves the security of electronic health records. Front Public Health 2023; 11:1272787. [PMID: 38089022 PMCID: PMC10713743 DOI: 10.3389/fpubh.2023.1272787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.
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Affiliation(s)
- Muhammad Hasnain
- Department of Computer Science, Lahore Leads University, Lahore, Pakistan
| | - Fahad R. Albogamy
- Turabah University College, Computer Sciences Program, Taif University, Taif, Saudi Arabia
| | | | - Imran Ghani
- Department of Computer and Information Sciences, Virginia Military Institute, Lexington, KY, United States
| | - Bilal Mehboob
- Department of Software Engineering, Superior University, Lahore, Pakistan
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Hiwale M, Walambe R, Potdar V, Kotecha K. A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2023; 3:100192. [PMID: 37223223 PMCID: PMC10160179 DOI: 10.1016/j.health.2023.100192] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/18/2023] [Accepted: 04/30/2023] [Indexed: 05/25/2023]
Abstract
The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.
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Affiliation(s)
- Madhuri Hiwale
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
| | - Rahee Walambe
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune 412115, India
| | - Vidyasagar Potdar
- Blockchain R&D Lab, School of Management and Marketing, Curtin University, Perth 6107, Australia
| | - Ketan Kotecha
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune 412115, India
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Khajehali N, Yan J, Chow YW, Fahmideh M. A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods. SENSORS (BASEL, SWITZERLAND) 2023; 23:7235. [PMID: 37631771 PMCID: PMC10459674 DOI: 10.3390/s23167235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/12/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the candidates. This study presents a comprehensive systematic literature review (SLR) that focuses on the challenges of client selection (CS) in the context of federated learning (FL). The objective of this SLR is to facilitate future research and development of CS methods in FL. Additionally, a detailed and in-depth overview of the CS process is provided, encompassing its abstract implementation and essential characteristics. This comprehensive presentation enables the application of CS in diverse domains. Furthermore, various CS methods are thoroughly categorized and explained based on their key characteristics and their ability to address specific challenges. This categorization offers valuable insights into the current state of the literature while also providing a roadmap for prospective investigations in this area of research.
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Affiliation(s)
- Naghmeh Khajehali
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia; (J.Y.); (Y.-W.C.)
| | - Jun Yan
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia; (J.Y.); (Y.-W.C.)
| | - Yang-Wai Chow
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia; (J.Y.); (Y.-W.C.)
| | - Mahdi Fahmideh
- School of Business, University of Southern Queensland (USQ), Brisbane, QLD 4350, Australia;
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Sembay MJ, de Macedo DDJ, Júnior LP, Braga RMM, Sarasa-Cabezuelo A. Provenance Data Management in Health Information Systems: A Systematic Literature Review. J Pers Med 2023; 13:991. [PMID: 37373980 DOI: 10.3390/jpm13060991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/08/2023] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
AIMS This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. METHOD An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. RESULTS Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. CONCLUSION It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.
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Affiliation(s)
- Márcio José Sembay
- Department of Information Science, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil
| | | | - Laércio Pioli Júnior
- Department of Computer Science, Federal University of Santa Catarina, Florianópolis 88040-370, Brazil
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Krishna C, Kumar D, Kushwaha DS. A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges. WIRELESS PERSONAL COMMUNICATIONS 2023; 131:1-48. [PMID: 37360140 PMCID: PMC10235850 DOI: 10.1007/s11277-023-10535-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.
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Affiliation(s)
- Charu Krishna
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
| | - Dinesh Kumar
- Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand 831014 India
| | - Dharmender Singh Kushwaha
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
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Rosta L, Menyhart A, Mahmeed WA, Al-Rasadi K, Al-Alawi K, Banach M, Banerjee Y, Ceriello A, Cesur M, Cosentino F, Firenze A, Galia M, Goh SY, Janez A, Kalra S, Kapoor N, Lessan N, Lotufo P, Papanas N, Rizvi AA, Sahebkar A, Santos RD, Stoian AP, Toth PP, Viswanathan V, Kempler P, Rizzo M. Telemedicine for diabetes management during COVID-19: what we have learnt, what and how to implement. Front Endocrinol (Lausanne) 2023; 14:1129793. [PMID: 37265696 PMCID: PMC10231679 DOI: 10.3389/fendo.2023.1129793] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/28/2023] [Indexed: 06/03/2023] Open
Abstract
The past two decades have witnessed telemedicine becoming a crucial part of health care as a method to facilitate doctor-patient interaction. Due to technological developments and the incremental acquisition of experience in its use, telemedicine's advantages and cost-effectiveness has led to it being recognised as specifically relevant to diabetology. However, the pandemic created new challenges for healthcare systems and the rate of development of digital services started to grow exponentially. It was soon discovered that COVID-19-infected patients with diabetes had an increased risk of both mortality and debilitating sequelae. In addition, it was observed that this higher risk could be attenuated primarily by maintaining optimal control of the patient's glucose metabolism. As opportunities for actual physical doctor-patient visits became restricted, telemedicine provided the most convenient opportunity to communicate with patients and maintain delivery of care. The wide range of experiences of health care provision during the pandemic has led to the development of several excellent strategies regarding the applicability of telemedicine across the whole spectrum of diabetes care. The continuation of these strategies is likely to benefit clinical practice even after the pandemic crisis is over.
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Affiliation(s)
| | - Adrienn Menyhart
- Department of Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Wael Al Mahmeed
- Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates
| | | | - Kamila Al-Alawi
- Department of Training and Studies, Royal Hospital, Ministry of Health, Muscat, Oman
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology , Medical University of Lodz (MUL), Lodz, Poland
- Department of Medicine, Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Lodz, Poland
- Cardiovascular Research Centre, University of Zielona Gora, Zielona Gora, Poland
| | - Yajnavalka Banerjee
- Department of Biochemistry, Mohammed Bin Rashid University, Dubai, United Arab Emirates
| | - Antonio Ceriello
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) MultiMedica, Milan, Italy
| | - Mustafa Cesur
- Clinic of Endocrinology, Ankara Güven Hospital, Ankara, Türkiye
| | - Francesco Cosentino
- Unit of Cardiology, Karolinska Institute and Karolinska University Hospital, University of Stockholm, Stockholm, Sweden
| | - Alberto Firenze
- Unit of Research and International Cooperation, University Hospital of Palermo, Palermo, Italy
| | - Massimo Galia
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (Bind), University of Palermo, Palermo, Italy
| | - Su-Yen Goh
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Andrej Janez
- Department of Endocrinology, Diabetes and Metabolic Diseases, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Sanjay Kalra
- Department of Endocrinology, Bharti Hospital, Karnal, India
| | - Nitin Kapoor
- Department of Endocrinology, Diabetes and Metabolism, Christian Medical College, Vellore, India
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Nader Lessan
- The Research Institute, Imperial College London Diabetes Centre, Abu Dhabi, United Arab Emirates
| | - Paulo Lotufo
- Center for Clinical and Epidemiological Research, University Hospital, University of São Paulo, Sao Paulo, Brazil
| | - Nikolaos Papanas
- Diabetes Center, Second Department of Internal Medicine, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
| | - Ali A. Rizvi
- Department of Medicine, University of Central Florida College of Medicine, Orlando, FL, United States
| | - Amirhossein Sahebkar
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Raul D. Santos
- Heart Institute (InCor), University of Sao Paulo Medical School Hospital, Sao Paulo, Brazil
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | - Anca Pantea Stoian
- Faculty of Medicine, Diabetes, Nutrition and Metabolic Diseases, Carol Davila University, Bucharest, Romania
| | - Peter P. Toth
- Cicarrone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | | | - Peter Kempler
- Department of Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Manfredi Rizzo
- Department of Biochemistry, Mohammed Bin Rashid University, Dubai, United Arab Emirates
- Faculty of Medicine, Diabetes, Nutrition and Metabolic Diseases, Carol Davila University, Bucharest, Romania
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (Promise), School of Medicine, University of Palermo, Palermo, Italy
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Johns M, Meurers T, Wirth FN, Haber AC, Müller A, Halilovic M, Balzer F, Prasser F. Data Provenance in Biomedical Research: Scoping Review. J Med Internet Res 2023; 25:e42289. [PMID: 36972116 PMCID: PMC10132013 DOI: 10.2196/42289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/14/2022] [Accepted: 12/23/2022] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research. OBJECTIVE The aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption. METHODS Following a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures. RESULTS We identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV. CONCLUSIONS The heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.
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Affiliation(s)
- Marco Johns
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Thierry Meurers
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix N Wirth
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Anna C Haber
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Armin Müller
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Mehmed Halilovic
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Balzer
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Prasser
- Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
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11
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Sirohi D, Kumar N, Rana PS, Tanwar S, Iqbal R, Hijjii M. Federated learning for 6G-enabled secure communication systems: a comprehensive survey. Artif Intell Rev 2023; 56:1-93. [PMID: 37362891 PMCID: PMC10008151 DOI: 10.1007/s10462-023-10417-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2023] [Indexed: 03/14/2023]
Abstract
Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches' future directions and existing drawbacks are discussed in detail.
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Affiliation(s)
- Deepika Sirohi
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab India
| | - Neeraj Kumar
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab India
- Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon
- Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, 248001, India
| | - Prashant Singh Rana
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat India
| | | | - Mohammad Hijjii
- Computer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia
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12
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Rasha AH, Li T, Huang W, Gu J, Li C. Federated Learning in Smart Cities: Privacy and Security Survey. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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13
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Malik H, Naeem A, Naqvi RA, Loh WK. DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020743. [PMID: 36679541 PMCID: PMC9864925 DOI: 10.3390/s23020743] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 05/14/2023]
Abstract
Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Rizwan Ali Naqvi
- Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (R.A.N.); (W.-K.L.)
| | - Woong-Kee Loh
- School of Computing, Gachon University, Seongnam 13120, Republic of Korea
- Correspondence: (R.A.N.); (W.-K.L.)
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14
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Bahbouh NM, Compte SS, Valdes JV, Sen AAA. An empirical investigation into the altering health perspectives in the internet of health things. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:67-77. [PMID: 35874858 PMCID: PMC9294750 DOI: 10.1007/s41870-022-01035-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 06/28/2022] [Indexed: 02/06/2023]
Abstract
Healthcare is on top of the agenda of all governments in the world as it is related to the well-being of the people. Naturally, this domain has attracted the attention of many researchers globally, who have studied the development of its different phases, including E-Health and the Internet of Health Things (IoHT). In this paper, the difference between the recent concepts of healthcare (E-health, M-Health, S-Health, I-Health, U-Health, and IoHT/IoMT) is analyzed based on the main services, applications, and technologies in each concept. The paper has also studied the latest developments in IoHT, which are linked to existing phases of development. A classification of groups of services and constituents of IoHT, linked to the latest technologies, is also provided. In addition, challenges, and future scope of research in this domain concerning the wellbeing of the people in the face of ongoing COVID-19 and future pandemics are explored.
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Affiliation(s)
- Nour Mahmoud Bahbouh
- Department of Information and Communication Sciences, Granada University, Granada, Spain
| | | | - Juan Valenzuela Valdes
- Department of Signal Theory, Telematics and Communications, Granada University, Granada, Spain
| | - Adnan Ahmed Abi Sen
- Faculty of Computer and Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
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15
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Adil M, Alshahrani H, Rajab A, Shaikh A, Song H, Farouk A. QoS Review: Smart Sensing in Wake of COVID-19, Current Trends and Specifications With Future Research Directions. IEEE SENSORS JOURNAL 2023; 23:865-876. [PMID: 36913223 PMCID: PMC9983689 DOI: 10.1109/jsen.2022.3170055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/21/2022] [Indexed: 05/12/2023]
Abstract
Smart Sensing has shown notable contributions in the healthcare industry and revamps immense advancement. With this, the present smart sensing applications such as the Internet of Medical Things (IoMT) applications are elongated in the COVID-19 outbreak to facilitate the victims and alleviate the extensive contamination frequency of this pathogenic virus. Although, the existing IoMT applications are utilized productively in this pandemic, but somehow, the Quality of Service (QoS) metrics are overlooked, which is the basic need of these applications followed by patients, physicians, nursing staff, etc. In this review article, we will give a comprehensive assessment of the QoS of IoMT applications used in this pandemic from 2019 to 2021 to identify their requirements and current challenges by taking into account various network components and communication metrics. To claim the contribution of this work, we explored layer-wise QoS challenges in the existing literature to identify particular requirements, and set the footprint for future research. Finally, we compared each section with the existing review articles to acknowledge the uniqueness of this work followed by the answer of a question why this survey paper is needed in the presence of current state-of-the-art review papers.
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Affiliation(s)
- Muhammad Adil
- Global Foundation for Cyber Studies and Research Washington DC 20015 USA
| | - Hani Alshahrani
- College of Computer Science and Information SystemsNajran University Najran 61441 Saudi Arabia
| | - Adel Rajab
- College of Computer Science and Information SystemsNajran University Najran 61441 Saudi Arabia
| | - Asadullah Shaikh
- College of Computer Science and Information SystemsNajran University Najran 61441 Saudi Arabia
| | - Houbing Song
- Department of Electrical Engineering and Computer ScienceEmbry-Riddle Aeronautical University Daytona Beach FL 32114 USA
| | - Ahmed Farouk
- Department of Computer ScienceFaculty of Computers and Artificial IntelligenceSouth Valley University Hurghada 84511 Egypt
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16
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Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. CLUSTER COMPUTING 2022; 26:1-41. [PMID: 35996680 PMCID: PMC9385101 DOI: 10.1007/s10586-022-03658-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/10/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
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Affiliation(s)
- Anichur Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Sazzad Hossain
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dipanjali Kundu
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Tanoy Debnath
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Muaz Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Md. Saikat Islam Khan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan
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17
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Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges. Foods 2022; 11:foods11152262. [PMID: 35954029 PMCID: PMC9367899 DOI: 10.3390/foods11152262] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/21/2022] [Accepted: 07/23/2022] [Indexed: 01/14/2023] Open
Abstract
Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made.
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18
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A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology. INFORMATION 2022. [DOI: 10.3390/info13050263] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device’s data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies.
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19
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Pinto F, Ferreira da Silva C, Moro S. People-Centered Distributed Ledger Technology-IoT Architectures: A Systematic Literature Review. TELEMATICS AND INFORMATICS 2022. [DOI: 10.1016/j.tele.2022.101812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Data Protection and Privacy of the Internet of Healthcare Things (IoHTs). APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041927] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importance in monitoring, processing, storing, and transmitting sensitive information. It is experiencing novel challenges regarding data privacy protection. This article discusses different components of IoHT and categorizes various healthcare devices based on their functionality and deployment. This article highlights the possible points and reasons for data leakage, such as conflicts in laws, the use of sub-standard devices, lack of awareness, and the non-availability of dedicated local law enforcement agencies. This article draws attention to the escalating demand for a suitable regulatory framework and analyzes compliance problems of IoHT devices concerning healthcare data privacy and protection regulations. Furthermore, the article provides some recommendations to improve the security and privacy of IoHT implementation.
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21
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Antunes RS, da Costa CA, Küderle A, Yari IA, Eskofier B. Federated Learning for Healthcare: Systematic Review and Architecture Proposal. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3501813] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.
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Affiliation(s)
| | | | | | | | - Björn Eskofier
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
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22
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Kamruzzaman MM, Yan B, Sarker MNI, Alruwaili O, Wu M, Alrashdi I. Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9957888. [PMID: 35126961 PMCID: PMC8808208 DOI: 10.1155/2022/9957888] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/05/2021] [Accepted: 12/23/2021] [Indexed: 11/18/2022]
Abstract
Nowadays, technology has been evolving rapidly. Due to the consequent impact of smart technologies, it becomes a ubiquitous part of life. These technologies have led to the emergence of smart cities that are geographic areas driven by advanced information and communication technologies. In the context of smart cities, IoT, blockchain, and fog computing have been found as the significant drivers of smart initiates. In this recognition, the present study is focused on delineating the impact and potential of blockchain, IoT, and fog computing on healthcare services in the context of smart cities. In pursuit of this objective, the study has conducted a systematic review of literature that is most relevant to the topic of the paper. In order to select the most relevant and credible articles, the researcher has used PRISMA and AMSTAR that have culminated in the 10 most relevant articles for the present study. The findings revealed that IoT, blockchain, and fog computing had become drivers of efficiency in the healthcare services in smart cities. Among the three technologies, IoT has been found to be widely incorporated. However, it is found to be lacking in terms of cost efficiency, data privacy, and interoperability of data. In this recognition, blockchain technology and fog computing have been found to be more relevant to the healthcare sector in smart cities. Blockchain has been presented as a promising technology for ensuring the protection of private data, creating a decentralized database, and improving the interoperability of data while fog computing has been presented as the promising technology for low-cost remote monitoring, reducing latency and increasing efficiency.
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Affiliation(s)
- M. M. Kamruzzaman
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia
| | - Bingxin Yan
- School of Public Administration, Sichuan University, Chengdu, China
| | - Md Nazirul Islam Sarker
- School of Political Science and Public Administration, Neijiang Normal University, Neijiang, China
| | - Omar Alruwaili
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia
| | - Min Wu
- School of Public Administration, Sichuan University, Chengdu, China
| | - Ibrahim Alrashdi
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia
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23
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Abstract
Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.
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Charles WM, Delgado BM. Health Datasets as Assets: Blockchain-Based Valuation and Transaction Methods. BLOCKCHAIN IN HEALTHCARE TODAY 2022; 5:185. [PMID: 36779021 PMCID: PMC9907414 DOI: 10.30953/bhty.v5.185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 05/13/2023]
Abstract
There is increasing recognition about health-oriented datasets that could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets - particularly health records - are ascribed monetary value on the black market, there are few established methods for assessing the value for legitimate research and business purposes. The emergence of blockchain has created new commercial opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. For authorized individuals to verify their transactions, blockchain methodologies offer security, auditability, and transparency. The authors share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Furthermore, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
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25
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Lu CYJY, Wu HT. A Hierarchical Clustering Federated Learning System Based on Industry 4.0. J ORGAN END USER COM 2022. [DOI: 10.4018/joeuc.313194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study proposes using dendrogram clustering as the basis to construct a federated learning system for A.I. model parameter updating. The authors adopted a private blockchain to accelerate downloads of the latest parameters corresponding to the computation results of an A.I. model. This study reduced the computational complexity of the backend server with the A.I. model to elevate backend server performance. Furthermore, the authors propose a hash function to determine whether the machines added new training data. The experimental results revealed that the proposed method could reduce the computational complexity of federated learning and that private blockchains can be applied to ensure parameter confidentiality and completeness. In summary, this research uses software computing methods to save machine learning data transmission, reduce network load, and provide privacy protection for parameter data without updating existing production equipment so that small-cost enterprises can import Industry 4.0.
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26
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Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft comput 2022; 26:4423-4440. [PMID: 34840525 PMCID: PMC8605788 DOI: 10.1007/s00500-021-06496-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2021] [Indexed: 01/12/2023]
Abstract
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL.
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27
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Thenmozhi R, Shridevi S, Mohanty SN, García-Díaz V, Gupta D, Tiwari P, Shorfuzzaman M. Attribute-Based Adaptive Homomorphic Encryption for Big Data Security. BIG DATA 2021. [PMID: 34898266 DOI: 10.1089/big.2021.0176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.
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Affiliation(s)
- R Thenmozhi
- Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
| | - S Shridevi
- Centre of Advanced Data Science, Vellore Institute of Technology, Vellore, Chennai, India
| | - Sachi Nandan Mohanty
- Department of Computer Science & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, India
| | | | - Deepak Gupta
- Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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Ng WY, Tan TE, Movva PVH, Fang AHS, Yeo KK, Ho D, Foo FSS, Xiao Z, Sun K, Wong TY, Sia ATH, Ting DSW. Blockchain applications in health care for COVID-19 and beyond: a systematic review. Lancet Digit Health 2021; 3:e819-e829. [PMID: 34654686 PMCID: PMC8510632 DOI: 10.1016/s2589-7500(21)00210-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/03/2021] [Accepted: 08/16/2021] [Indexed: 01/04/2023]
Abstract
The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.
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Affiliation(s)
- Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore
| | - Prasanth V H Movva
- Certis Commercial and Industrial Security Corporation Security, Singapore
| | - Andrew Hao Sen Fang
- Duke-NUS Medical School, National University of Singapore, Singapore,SingHealth Polyclinics, Singapore
| | - Khung-Keong Yeo
- Duke-NUS Medical School, National University of Singapore, Singapore,National Heart Centre Singapore, Singapore
| | - Dean Ho
- Institute for Digital Medicine and Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore,N.1 Institute for Health, National University of Singapore, Singapore,Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Fuji Shyy San Foo
- Certis Commercial and Industrial Security Corporation Security, Singapore
| | - Zhe Xiao
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Kai Sun
- Data Science Institute, Imperial College London, London, UK
| | - Tien Yin Wong
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore
| | - Alex Tiong-Heng Sia
- Duke-NUS Medical School, National University of Singapore, Singapore,KK Women's and Children's Hospital, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore,Correspondence to: Dr Daniel Ting, Duke-NUS Medical School, National University of Singapore, Singapore 168751
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29
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Mamdouh M, Awad AI, Khalaf AA, Hamed HF. Authentication and Identity Management of IoHT Devices: Achievements, Challenges, and Future Directions. Comput Secur 2021. [DOI: 10.1016/j.cose.2021.102491] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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30
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Chang Y, Fang C, Sun W. A Blockchain-Based Federated Learning Method for Smart Healthcare. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:4376418. [PMID: 34868289 PMCID: PMC8635913 DOI: 10.1155/2021/4376418] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 11/18/2022]
Abstract
The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.
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Affiliation(s)
- Yuxia Chang
- Department of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou 450001, China
- Key Laboratory of Nursing Medicine of Henan Province, Zhengzhou 450001, China
- People's Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Chen Fang
- Information Engineering University, Zhengzhou 450001, China
| | - Wenzhuo Sun
- Department of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou 450001, China
- Key Laboratory of Nursing Medicine of Henan Province, Zhengzhou 450001, China
- People's Hospital of Zhengzhou University, Zhengzhou 450001, China
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31
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Li D, Luo Z, Cao B. Blockchain-based federated learning methodologies in smart environments. CLUSTER COMPUTING 2021; 25:2585-2599. [PMID: 34744493 PMCID: PMC8561346 DOI: 10.1007/s10586-021-03424-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 08/30/2021] [Accepted: 09/19/2021] [Indexed: 06/12/2023]
Abstract
Blockchain technology is an undeniable ledger technology that stores transactions in high-security chains of blocks. Blockchain can solve security and privacy issues in a variety of domains. With the rapid development of smart environments and complicated contracts between users and intelligent devices, federated learning (FL) is a new paradigm to improve accuracy and precision factors of data mining by supporting information privacy and security. Much sensitive information such as patient health records, safety industrial information, and banking personal information in various domains of the Internet of Things (IoT) including smart city, smart healthcare, and smart industry should be collected and gathered to train and test with high potential privacy and secured manner. Using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy. Finally, blockchain-based FL mechanisms are very hot topics and cut of scientific edge in data science and artificial intelligence. This research proposes a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue. According to the analytical results of this research, blockchain-based FL has been grown significantly during these 5 years and blockchain technology has been used more to solve problems related to patient healthcare records, image retrieval, cancer datasets, industrial equipment, and economical information in the field of IoT applications and smart environments.
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Affiliation(s)
- Dong Li
- BULL, 32 Sanhai Road, Guanhaiwei Town, Cixi, Ningbo City, 315300 Zhejiang China
- College of Metrology & Measurement Engineering, China Jiliang University, 258 Xueyuan Street, Xiasha Higher Education Park, Hangzhou, 310018 Zhejiang China
| | - Zai Luo
- College of Metrology & Measurement Engineering, China Jiliang University, 258 Xueyuan Street, Xiasha Higher Education Park, Hangzhou, 310018 Zhejiang China
| | - Bo Cao
- BULL, 32 Sanhai Road, Guanhaiwei Town, Cixi, Ningbo City, 315300 Zhejiang China
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32
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Masud M, Gaba GS, Alqahtani S, Muhammad G, Gupta BB, Kumar P, Ghoneim A. A Lightweight and Robust Secure Key Establishment Protocol for Internet of Medical Things in COVID-19 Patients Care. IEEE INTERNET OF THINGS JOURNAL 2021; 8:15694-15703. [PMID: 35782176 PMCID: PMC8791439 DOI: 10.1109/jiot.2020.3047662] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 12/22/2020] [Indexed: 05/05/2023]
Abstract
Due to the outbreak of COVID-19, the Internet of Medical Things (IoMT) has enabled the doctors to remotely diagnose the patients, control the medical equipment, and monitor the quarantined patients through their digital devices. Security is a major concern in IoMT because the Internet of Things (IoT) nodes exchange sensitive information between virtual medical facilities over the vulnerable wireless medium. Hence, the virtual facilities must be protected from adversarial threats through secure sessions. This article proposes a lightweight and physically secure mutual authentication and secret key establishment protocol that uses physical unclonable functions (PUFs) to enable the network devices to verify the doctor's legitimacy (user) and sensor node before establishing a session key. PUF also protects the sensor nodes deployed in an unattended and hostile environment from tampering, cloning, and side-channel attacks. The proposed protocol exhibits all the necessary security properties required to protect the IoMT networks, like authentication, confidentiality, integrity, and anonymity. The formal AVISPA and informal security analysis demonstrate its robustness against attacks like impersonation, replay, a man in the middle, etc. The proposed protocol also consumes fewer resources to operate and is safe from physical attacks, making it more suitable for IoT-enabled medical network applications.
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Affiliation(s)
- Mehedi Masud
- Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia
| | - Gurjot Singh Gaba
- Department of Electronics and Electrical EngineeringLovely Professional UniversityPhagwara144411India
| | - Salman Alqahtani
- Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia
| | - Ghulam Muhammad
- Chair of Pervasive and Mobile ComputingSaudi Arabia
- Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia
| | - B. B. Gupta
- Department of Computer EngineeringNational Institute of Technology KurukshetraHaryana136119India
- Department of Computer Science and Information EngineeringAsia UniversityTaichung41354Taiwan
| | - Pardeep Kumar
- Department of Computer ScienceSwansea UniversitySwanseaSA1 8ENU.K.
| | - Ahmed Ghoneim
- Department of Software EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh51178Saudi Arabia
- Department of Mathematics and Computer ScienceFaculty of ScienceMenoufia UniversityShebin El-Koom32511Egypt
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33
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Rahman MA, Hossain MS. An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19. IEEE INTERNET OF THINGS JOURNAL 2021; 8:15847-15854. [PMID: 35782185 PMCID: PMC8769000 DOI: 10.1109/jiot.2021.3051080] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 12/26/2020] [Accepted: 01/06/2021] [Indexed: 05/11/2023]
Abstract
Capturing psychological, emotional, and physiological states, especially during a pandemic, and leveraging the captured sensory data within the pandemic management ecosystem is challenging. Recent advancements for the Internet of Medical Things (IoMT) have shown promising results from collecting diversified types of such emotional and physical health-related data from the home environment. State-of-the-art deep learning (DL) applications can run in a resource-constrained edge environment, which allows data from IoMT devices to be processed locally at the edge, and performs inferencing related to in-home health. This allows health data to remain in the vicinity of the user edge while ensuring the privacy, security, and low latency of the inferencing system. In this article, we develop an edge IoMT system that uses DL to detect diversified types of health-related COVID-19 symptoms and generates reports and alerts that can be used for medical decision support. Several COVID-19 applications have been developed, tested, and deployed to support clinical trials. We present the design of the framework, a description of our implemented system, and the accuracy results. The test results show the suitability of the system for in-home health management during a pandemic.
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Affiliation(s)
- Md. Abdur Rahman
- Department of Cyber Security and Forensic ComputingCollege of Computing and Cyber SciencesUniversity of Prince MugrinMadinah41499Saudi Arabia
| | - M. Shamim Hossain
- Chair of Pervasive and Mobile ComputingSaudi Arabia
- Department of Software EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia
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34
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An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications. SENSORS 2021; 21:s21196346. [PMID: 34640666 PMCID: PMC8512885 DOI: 10.3390/s21196346] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/25/2022]
Abstract
The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.
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35
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Alharbi A, Abdur Rahman MD. Review of Recent Technologies for Tackling COVID-19. SN COMPUTER SCIENCE 2021; 2:460. [PMID: 34549196 PMCID: PMC8444512 DOI: 10.1007/s42979-021-00841-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/26/2021] [Indexed: 01/09/2023]
Abstract
The current pandemic caused by the COVID-19 virus requires more effort, experience, and science-sharing to overcome the damage caused by the pathogen. The fast and wide human-to-human transmission of the COVID-19 virus demands a significant role of the newest technologies in the form of local and global computing and information sharing, data privacy, and accurate tests. The advancements of deep neural networks, cloud computing solutions, blockchain technology, and beyond 5G (B5G) communication have contributed to the better management of the COVID-19 impacts on society. This paper reviews recent attempts to tackle the COVID-19 situation using these technological advancements.
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Affiliation(s)
- Ayman Alharbi
- Department Of Computer Engineering, College of Computer and Information systems, Umm AL-Qura University, Mecca, Saudi Arabia
| | - MD Abdur Rahman
- Department of Cyber Security and Forensic Computing, College of Computer and Cyber Sciences, University of Prince Mugrin, Madinah, 41499 Saudi Arabia
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36
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Rebirth of Distributed AI-A Review of eHealth Research. SENSORS 2021; 21:s21154999. [PMID: 34372236 PMCID: PMC8348246 DOI: 10.3390/s21154999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/21/2022]
Abstract
The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.
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37
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Madhavan MV, Khamparia A, Gupta D, Pande S, Tiwari P, Hossain MS. Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning. Neural Comput Appl 2021; 35:13907-13920. [PMID: 34127892 PMCID: PMC8188748 DOI: 10.1007/s00521-021-06171-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 05/25/2021] [Indexed: 12/31/2022]
Abstract
Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.
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Affiliation(s)
- Mangena Venu Madhavan
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Aditya Khamparia
- Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Amethi, India
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Rohini, India
| | - Sagar Pande
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - M. Shamim Hossain
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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38
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Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues. FUTURE INTERNET 2021. [DOI: 10.3390/fi13020048] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.
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