security issues in cloud computing research paper 2021

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

IEEE Access

Related Papers

Innovative Research Publications , Dinesh Taneja

Cloud computing is an evolutionary outgrowth of prior computing approaches which build upon existing and new technologies. Cloud computing is a model for on demand network access to a shared pool of resources such as servers, storage, applications and related services. Cloud computing can be provisioned and released with minimum interaction and preferably without intervention of cloud service provider. The rising awareness and implementations of cloud services and its underlying technologies cause the need for security requirements being up to date. These developments have created new security vulnerabilities, including security issues whose full impressions are still rising. This paper presents an overview and study of cloud computing, with several security threats, security issues, currently used cloud technologies and countermeasures. The security challenges in cloud computing are formidable, especially for public clouds whose infrastructure and computational resources are owned by an outside party that sells those services to the general public. Cloud security requirements have been addressed in publications earlier, but it is still difficult to estimate what kinds of requirements have been researched most, and which are still under-researched. This paper carries out a systematic literature review by identifying cloud computing security requirements from publications. Along with security issues, upside of information security in cloud computing has also been part of this work.

International advanced research journal in science, engineering and technology

vishal Thakur Som

International Journal of Scientific & Technology Research

Mubashir Ali

Aarti Patel

https://www.researchgate.net/publication/332159316_Cloud_Computing_Security_Issues_of_Sensitive_Data_An_Analysis

Malka N. Halgamuge

Numerous organizations are using aspects of the cloud to store data, but as sensitive data is placed on the cloud, privacy and security become difficult to maintain. When users upload data to the cloud, they may become increasingly vulnerable to account hijacking, unauthorized access, and the data may become unavailable because of various technical reasons. Questions remain about the security of sensitive data in the cloud, and in this chapter, the authors perform an analysis of 36 peer reviewed publications describing 30 observations of cloud computing technology (2010-2017). In the articles, applications of cloud computing include, for instance, business (26%) and the internet of things (IoT; 2%), and the result suggests that some issues are unique to a particular domain (such as business, education, health) and some issues cross all domains. The results suggest that data integrity issues have the highest number of solutions whereas data breaches have the lowest number of solutions.

Computational Intelligence and Neuroscience

junaid hassan

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly,...

Gaurav Tyagi

Chief E D I T O R IJRISAT

Cloud computing is Internet-based computing, whereby shared resources, software, and information are provided to computers and other devices on demand, as with the electricity grid. It is a completely internet dependent technology where client data is stored and maintain in the data center of a cloud provider. In this paper, we aim to pinpoint the challenges and issues of cloud computing. Regardless of its advantages, the transition to this computing paradigm raises security concerns, which are the subject of several studies. With the introduction of numerous cloud based services and geographically dispersed cloud service providers, sensitive information of different entities is normally stored in remote servers and locations with the possibilities of being exposed to unwanted parties in situations where the cloud servers storing that information are compromised. including authentication, encryption and decryption and compression. In this paper, the authors discuss security issues, privacy and control issues, accessibility issues, confidentiality, integrity of data and many more for cloud computing.

Kelvinking Olupitan Omoregie

IJAR Indexing

Security is one of the biggest obstacles that prevent the adoption of cloud computing [1]. Businesses and research are reluctant in shifting the control of digital assets to the third?party service providers [2].Organizations does not enjoy administrative control of cloud services and infrastructure [3]. The security measures taken by the cloud service providers (CSP) are transparent to the organization [4].The presence of large number of users from different organizations aggravates the situation further [2]; the users might be trusted by the CSP but may not trust each other [4]. The above reasons increase the customers? uncertainty about their digital assets on the cloud resulting in reluctance to adopt cloud computing [2].This paper exploits certain information security risks namely data, user identity and access control and contractual and legal issues. Moreover, the manuscript presents a comprehensive solution in literature to cater for all security risks. A critical evaluation of the solution by comparing it with other solutions that exist in literature is provided. The analysis proves the thoroughness and outperformance of the comprehensive solution compared to the other solutions that exist in literature.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Abrar Asghar

Innovative Research Publications

International Journal of Engineering & Technology

Jahangir Jabbar

SN Computer Science

Garima Verma

International Journal of Advances in Computer Science and Technology

WARSE The World Academy of Research in Science and Engineering

Maham Iftikhar

PRIYANKA GUPTA

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

susmita mishra

International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT

maher muzamal

Computers & Electrical Engineering

Nalini Subramanian

The Surge in Cloud Computing use and its Data Privacy and Security Measures

Oluwafemi Adedeji , Olamide Alashi , Kido Ukwamefune

Journal of Telecommunication, Electronic and Computer Engineering

mohammed ali

Ijca Proceedings on International Conference on Recent Trends in Information Technology and Computer Science 2012

PRASHANT REWAGAD

Ismath Basha

nupur chugh

rashmi priya

Maria A. Omer

… (TSSA), 2012 7th …

Gurudatt Kulkarni

Rahul Neware

Diksha Jain

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

Lecture Notes in Electrical Engineering

Haider Abbas

Vahid Akbari

Mohanaad Shakir , Maytham Hammood

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Help | Advanced Search

Computer Science > Cryptography and Security

Title: data security and privacy in cloud computing: concepts and emerging trends.

Abstract: Millions of users across the world leverages data processing and sharing benefits from cloud environment. Data security and privacy are inevitable requirement of cloud environment. Massive usage and sharing of data among users opens door to security loopholes. This paper envisages a discussion of cloud environment, its utilities, challenges, and emerging research trends confined to secure processing and sharing of data.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

DBLP - CS Bibliography

Bibtex formatted citation.

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

A review on cloud security issues and solutions

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options.

• Joraviya N Gohil B Rao U (2024) DL-HIDS: deep learning-based host intrusion detection system using system calls-to-image for containerized cloud environment The Journal of Supercomputing 10.1007/s11227-024-05895-3 80 :9 (12218-12246) Online publication date: 1-Jun-2024 https://dl.acm.org/doi/10.1007/s11227-024-05895-3

Recommendations

Cloud security issues and challenges.

The cloud computing provides on demand services over the Internet with the help of a large amount of virtual storage. The main features of cloud computing is that the user does not have any setup of expensive computing infrastructure and the cost of its ...

State-of-the-art cloud computing security taxonomies: a classification of security challenges in the present cloud computing environment

Cloud computing has taken center stage in the present business scenario due to its pay-as-you-use nature, where users need not bother about buying resources like hardware, software, infrastructure, etc. permanently. As much as the technological benefits,...

Cloud Computing Security: Opportunities and Pitfalls

The evolution of modern computing systems has lead to the emergence of Cloud computing. Cloud computing facilitates on-demand establishment of dynamic, large scale, flexible, and highly scalable computing infrastructures. However, as with any other ...

Information

Published in.

Netherlands

Publication History

Author tags.

• Cloud computing
• Cloud security
• virtualization
• security issues
• security solutions
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 1 Total Citations View Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.14(4); Jul-Aug 2021

Security challenges and solutions using healthcare cloud computing

Mohammad mehrtak.

1. School of Medicine and Allied Medical Sciences, Ardabil University of Medical Sciences, Ardabil, Iran

SeyedAhmad SeyedAlinaghi

2. Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran

Mehrzad MohsseniPour

Tayebeh noori.

3. Department of Health Information Technology, Zabol University of Medical Sciences, Zabol, Iran

Amirali Karimi

4. School of medicine, Tehran University of Medical Sciences, Tehran, Iran

Ahmadreza Shamsabadi

5. Department of Health Information Technology, Esfarayen Faculty of Medical Sciences, Esfarayen, Iran

Mohammad Heydari

6. Department of Health Information Technology, Khalkhal University of Medical Sciences, Khalkhal, Iran

Alireza Barzegary

7. School of medicine, Islamic Azad University, Tehran, Iran

Pegah Mirzapour

Mahdi soleymanzadeh.

8. Farabi Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Farzin Vahedi

Esmaeil mehraeen, omid dadras.

9. Department of Global Health and Socioepidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

Cloud computing is among the most beneficial solutions to digital problems. Security is one of the focal issues in cloud computing technology, and this study aims at investigating security issues of cloud computing and their probable solutions. A systematic review was performed using Scopus, Pubmed, Science Direct, and Web of Science databases. Once the title and abstract were evaluated, the quality of studies was assessed in order to choose the most relevant according to exclusion and inclusion criteria. Then, the full texts of studies selected were read thoroughly to extract the necessary results. According to the review, data security, availability, and integrity, as well as information confidentiality and network security, were the major challenges in cloud security. Further, data encryption, authentication, and classification, besides application programming interfaces (API), were security solutions to cloud infrastructure. Data encryption could be applied to store and retrieve data from the cloud in order to provide secure communication. Besides, several central challenges, which make the cloud security engineering process problematic, have been considered in this study.

Introduction

Recently, clinical service demand on technology has been increased; cloud computing solutions, telemedicine, artificial intelligence, and electronic health can frequently provide better services [ 1 ]. Cloud computing is the delivery of different services through the Internet. These resources include tools and applications such as data storage, servers, databases, networking, and software [ 2 ]. Rather than owning their computing infrastructure or data centers, companies and organizations can lease access to whatever consists of storage or processing by the cloud service providers [ 3 ]. Shared resources, including servers, networks, storage tools, and application software, use cloud computing significantly [ 4 , 5 ]. Also, cloud computing users can access their programs and information using the Internet as a conduit. The adoption of cloud technology has been increased in all industries, including healthcare [ 6 , 7 ].

Healthcare organizations generate a wide range of data and information. Big data in the field of health need infrastructure for better storage and management. Patient data availability is one of the most vital needs in the health and medical industry [ 8 ]. Also, health researchers need easy access to extensive data for scientific analysis. Cloud technologies are applied in healthcare fields, such as mobile apps, patient portals, electronic medical records, devices with the Internet of Things (IoT), and big data analytics [9–11)] As per the service demands, healthcare providers need considerably to scale the data storage and network requirements.

Furthermore, using the cloud in electronic health records enables patients to easily and widely access their health information. Cloud computing changes how nurses, doctors, hospitals, and clinics deliver quality and profitable services to the patients. The challenges in the healthcare field include operational and infrastructure costs, security concerns to real-time information sharing, and robust backup.

Cloud computing has several advantages, including easy and convenient collaboration between users, reduced costs, increased speed, scalability, and flexibility. The data sharing process is more facilitated by cloud computing. Further, it has the potential to significantly decrease in-house infrastructural and operational costs in healthcare organizations [ 15 ]. By changing traditional data storage and handling procedures, cloud technology can speed up access to information and overcome the barriers that the industry stakeholders and patients encounter. Despite the numerous benefits of cloud computing, there are some drawbacks and challenges. Healthcare organizations are hesitant to adopt cloud computing due to security concerns, including patient information confidentiality, privacy, and service costs [ 16 , 17 ]. Although massive data generated in healthcare organizations should be available to physicians and researchers, confidentiality concerns must be considered [18–20]. New challenges in cloud computing technology emerge in tandem with the growth, epiphany, and use of cloud technology in healthcare organizations; thus, identifying healthcare challenges and security issues appears essential. Accordingly, there are new challenges or security issues concerning offered solutions to cloud computing in healthcare organizations that should be examined and reviewed. Alongside identifying security challenges in cloud technology, reviewing present security solutions and providing new ones are also important objectives of this study. The present study aims at identifying barriers, challenges, security issues, and solutions in implementing cloud computing in the healthcare industry.

Material and Methods

As this systematic review aimed at updating the results of earlier studies [ 21 ] concerning the topic, the eligible articles from the beginning of 2015 to November 2020 were retrieved. A comprehensive search of relevant literature was conducted utilizing the online databases of Scopus, Pubmed, Science Direct, and Web of Science. Two researchers were involved in the online search and identification of the relevant articles. In the first step, based on the inclusion criteria, the relevant studies were included using relevant keywords in the title or abstract. The literature not relevant to the present research and those with no original data were excluded. The second step involved a scrutinized full-text screening of the related article to choose the most eligible.

Research question

This study aims to address the following issues of the modern healthcare systems:

• • What are the main challenges threatening the security of cloud computing?
• • What are the solutions to overcome these potential difficulties?

Inclusion/Exclusion criteria

We included the English studies serving our study's purpose from the beginning of 2015 to November 2020.

The exclusion criteria were as follows:

• • Preliminary data from incompleted projects;
• • Abstracts, conference abstracts, and any other incomplete projects without full-text manuscripts;
• • Review articles, letters to the editors, or any types of articles lacking original data.
• • Articles lacking available free full-text.

A total of 930 full texts of related studies were identified using the selected search strategy. After reviewing them, 360 duplicates were identified and removed; then, two independent investigators screened the title and abstract of the rest (570 resources). The full text of the extracted articles was reviewed, and the most relevant (245 resources) were selected based on the eligibility criteria. According to the selection criteria, 197 articles were excluded, all of which were found to be reviews (n=36), opinion articles (n=28), or not involving cloud security (n=133). Finally, 48 studies met inclusion criteria and were included in the final review ( Figure 1 ).

Flow diagram for the selection process of identified articles.

We identified common security challenges and potential solutions for cloud technology. The reviewed studies and cloud computing security challenges and solutions are presented in Table 1 . According to the review of the studies, the most frequent cloud security challenges were information confidentiality (n=19), data security (n=14), data availability (n=14), data integrity (n=13), and network security (n=12); frequency data are shown in Figure 2 . Furthermore, data encryption (n=17), authentication (n=10), application programming interfaces (API)(n=7), and data classification (n=6) were the most common solutions for the security challenges in cloud infrastructure ( Figure 3 ).

Frequency of the cloud computing security challenges.

Most common identified solutions for security challenges in cloud computing.

Identified security challenges and potential solutions in healthcare cloud computing.

Although cloud computing, as a novel technology, provides patient data availability all across, it encounters critical challenges in meeting one of the health industry's most significant demands. In cloud computing, providing security systems is necessary due to its inherent features, such as remote data storage, lack of network environment, proliferation, and massive infrastructure sharing [ 69 ]. Therefore, accurate identification of security challenges and their appropriate solutions is essential for both cloud computing providers and organizations using this technology [ 62 ].

Recently, artificial intelligence (AI) has shown a promising bright future in medical issues, especially when combined with cloud computing. Ahmed Sedik et al. have used AI deep learning to create a tool for quick screening of COVID-19 patients from their chest X-rays. This modality can be performed through a cloud-based system anywhere radiography equipment is found [ 70 ].

Identification systems also can use cloud computing. Alsmirat et al. have shown that digital cameras can act as a fingerprint identification system with an image compression rate of 30–40%, widely available on smartphones. Data security is a significant challenge there as well [ 71 ].

The present study indicates that the most vital challenge in cloud computing technology is maintaining data security. Malicious or negligent individuals may threaten data security. Several solutions provide data security, the most important of which is data encryption [ 20 , 25 , 29 ]. Data encryption is an essential line of protection in cybersecurity architecture. Encryption makes interrupted data use as difficult as possible [ 27 , 28 , 32 ]. Furthermore, data encryption is used to develop an encryption scheme that hypothetically can merely be broken with large amounts of computing potency [ 41 , 42 , 44 , 49 ]. Kaur et al. [ 49 ] and Dorairaj et al. [ 57 ] have stated data encryption as a strategy to protect data against security threats. The results of the current research further show data encryption as the best solution to provide data security.

Many methods have been proposed for data encryption. A four-image encryption scheme has been proposed by Yu et al. based on the computer-generated hologram, quaternion fresnel transforms (QFST), and two-dimensional (2D) logistic-adjusted-sine map (LASM). This innovative technology considerably decreases the key data sent to the receiver for decryption, making it more promising to be stored and transmitted [ 72 ]. In order to secure cloud data storage and its delivery to authorized users, a hierarchal identity-based cryptography method has been proposed by Kaushik et al. to assure that a malicious attacker or CSP does not change for its benefit [ 73 ].

Another research has proposed a method to avoid always using the upstream communication channel from the clients to the cloud server via an optimistic concurrency control protocol, which reduces communication delay for IoT users. Only update transactions are sent to the cloud using this method, and they are only partially validated at the fog node [ 74 ].

According to the present research results, confidentiality is the second most important challenge in cloud technology. It refers to the protection of data from being obtained by unauthorized individuals; in other words, sensitive information is only accessible by authorized persons [ 75 ]. Cloud data control can result in an increased risk of data compromise. To ensure that the patient-doctor relationship runs smoothly, patients must have faith in the healthcare system to keep their data private [ 17 ]. Studies have shown that confidentiality may be achieved by access control [ 52 , 62 ] and authentication [ 45 , 76 ]. A Mutual Authentication and Secret Key (MASK) establishment protocol has been presented by Masud et al. in the field of the Internet of Medical Things (IoMT) in COVID-19 patients. The proposed protocol uses Physical Unclonable Functions (PUF) to enable the network devices to validate the doctor legitimacy (user) and sensor node before establishing a session key. Therefore, it addresses the confidentiality, authentication, and integrity problems and secures the sensitive health information of the patients [ 77 ].

This research shows that integrity, availability, and network security are important issues in the cloud computing infrastructure. A developmental study has mentioned integrity and availability as challenging problems in implementing cloud-based services, especially when losing or leaking information could result in major legal- or business-related damage [ 34 ]. Confidentiality, Integrity, and Availability (CIA) have been reported as the main three factors in cloud system security, which are considered here for the evaluation [ 33 ].

The number of network security challenges has rapidly increased with the advent of wireless sensor networks [ 22 , 24 ]. Therefore, network security in cloud infrastructure has become a challenge for organizations [ 41 , 43 ]. The common network attacks have happened at the network layer, including IP spoofing, port scanning, man-in-middle attack, address resolution protocol (ARP) spoofing, routing information protocol (RIP) attack, denial of service (DoS), and distributed denial of service (DDoS) [ 58 ]. The attackers, for instance, can send a considerable number of requests in order to access virtual machines in cloud computing to restrict their availability to valid users; this is termed the DoS attack. The availability of cloud resources is targeted by this attack [ 63 ]. The related studies have shown that no specific security standard exists for security controls in wireless networks [ 24 , 51 , 63 ]. However, in order to keep security in cloud computing networks, potential solutions, including Application Programming Interfaces (API), data classification, and security management protocol, could be applied [ 60 , 64 , 78 , 79 ].

Limitations

Due to the nature of the solution protocols, we could not explain their details. We aimed to clarify the present challenges and possible solutions to help others address and work on the issues, thus skipping some details and protocols presented for solutions. We only reviewed the English studies, thus possibly missing some reports.

Cloud computing offers various benefits in data access and storage, particularly to healthcare organizations and relevant studies. Although the cloud computing environment is considered as a potential Internet-based computing platform, the security concerns encountered are notable. Security concerns may occur as a result of the cloud computing paradigm's shared, virtualized, and public nature. Overcoming these challenges by developing novel solutions is the only option for cloud computing adoption. All users, individuals or organizations, should be well informed of the security risks in the cloud.

In this study, an overview of cloud computing is presented; also, its security challenges and solutions surfaced within the past five years are reviewed. In order to offer safe data access, data encryption can be utilized to store and retrieve data from the cloud. We have also gone through some of the major challenges that make cloud security engineering tough. Identifying these challenges is the first step to tackle them, and future studies need to provide more feasible solutions to fix such bugs.

Acknowledgments

The present study was extracted from the research project with the IR.KHALUMS.REC.1400.001 code entitled "Investigating the necessary infrastructure for implementing cloud computing technology in Khalkhal University of Medical Sciences" conducted at the Khalkhal University of Medical Sciences in 2021.

Conflict of interest

The authors declare that there is no conflict of interest.

We apologize for the inconvenience...

To ensure we keep this website safe, please can you confirm you are a human by ticking the box below.

If you are unable to complete the above request please contact us using the below link, providing a screenshot of your experience.

https://ioppublishing.org/contacts/

HE-AO: An Optimization-Based Encryption Approach for Data Delivery Model in A Multi-Tenant Environment

• Published: 17 September 2024

Cite this article

• Pawan Kumar 1 &
• Ashutosh Kumar Bhatt 2  

Recently, cloud computing has become a growing technology in the information technology industry because of its several smooth delivery services. In cloud computing, multi-tenancy is one of the primary features that affords economic and scalability significance to the service providers and end-users by distributing a similar cloud platform. Due to the increasing demand for cloud computing, cloud usage has increased, so various vulnerabilities and threats have also been enhanced. Hence, data security and privacy are considered the major issues of multi-tenant environments in the cloud. Several existing studies have developed different mechanisms to solve security issues in multi-tenant cloud environments. However, they faced various problems while improving security, and this led to a lack of confidentiality, authenticity, and data integrity. Thus, this research paper intends to propose an efficient encryption approach for securing data delivery in the cloud with reduced time. For secure data delivery, homomorphic encryption is utilized to encode the cloud server’s data. In homomorphic encryption, four stages are available for data delivery: key generation, encryption, decryption, and evaluation. The main problems in this homomorphic encryption mechanism are key sharing and key management. Due to these problems, the performance of homomorphic encryption is diminished. Thus, the proposed work introduces an Aquila optimizer for the key generation process. In this, optimal keys are selected, and it provides improved data security and privacy for cloud users. Finally, the selected keys are generated for the encryption and decryption process. The efficiency of the proposed approach is proved by comparing the performance in terms of encryption time, decryption time and throughput over the existing schemes like Rivest, Shamir and Adleman, ElGamal, Algebra Homomorphic Encryption scheme based on ElGamal (AHEE) and modified AHEE. The experimental results reveal that the proposed model achieves reduced encryption and decryption time of 972 ms and 4261 ms for the data size ranges from 5 to 25 mb.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Artificial Intelligence

Data Availability

No data Availability.

Guo, Y., Ganti, S., & Wu, Y. (2024). Enhancing energy efficiency in telehealth internet of things systems through fog and cloud computing integration: simulation study. JMIR biomedical engineering, 9 (1), e50175.

Google Scholar  

Venkatachalam, N., Shanmugam, L., Parkavi, K., & Satheeswari, D. (2024). IoT Next gen and the world of big data computing. In  Big Data Computing  (pp. 1–16). CRC Press.

Mahapatra, A., Mishra, K., Pradhan, R., & Majhi, S. K. (2024). Next generation task offloading techniques in evolving computing paradigms: Comparative analysis, current challenges, and future research perspectives. Archives of Computational Methods in Engineering, 31 (3), 1405–1474.

Chauhan, M., & Shiaeles, S. (2023). An analysis of cloud security frameworks, problems and proposed solutions. Network, 3 (3), 422–450.

Kim, J., & Kim, Y. (2016). Benefits of cloud computing adoption for smart grid security from security perspective. The Journal of Supercomputing, 72 , 3522–3534.

Battula, M. (2024). A systematic review on a multi-tenant database management system in cloud computing. In  2024 international conference on cognitive robotics and intelligent systems (ICC-ROBINS) , 890–897. IEEE.

Adeniyi, E. A., Ogundokun, R. O., Misra, S., Awotunde, J. B., & Abiodun, K. M. (2022). Enhanced security and privacy issue in multi-tenant environment of green computing using blockchain technology. In S. Misra & A. K. Tyagi (Eds.), Blockchain applications in the smart era (pp. 65–83). Cham: Springer International Publishing.

Kudari, R., Reddy, D. A., & Krishna, G. R. (2022). Collaborative Network Security for Virtual Machine in Cloud Computing for Multi-tenant Data Center. In: Machine Intelligence and Smart Systems: Proceedings of MISS 2021 , 341–349. Singapore: Springer Nature Singapore.

Deochake, S., & Channapattan, V. (2022). Identity and access management framework for multi-tenant resources in hybrid cloud computing. In Proceedings of the 17th International Conference on Availability, Reliability and Security , 1–8.

Bindu Madavi, K. P., Neelaveni, P., Rakesh, P., & Asish, S. (2024). Security and privacy issues in cloud and IoT technology and their countermeasures. In A. Prasad (Ed.), Communication technologies and security challenges in IoT: Present and future (pp. 107–129). Singapore: Springer Nature Singapore.

Ali, T., Al-Khalidi, M., & Al-Zaidi, R. (2024). Information security risk assessment methods in cloud computing: comprehensive review. Journal of Computer Information Systems . https://doi.org/10.1080/08874417.2024.2329985

Polkowski, Z., Nycz-Lukaszewska, M. (2020). Infrastructure as a service: Cloud computing model for micro companies. In Advances in Data Science and Management: Proceedings of ICDSM 2019 pp. 307–320. Springer Singapore, 2020.

Matarneh, A. J., AlTahat, S. S., Ali, O. A., & Jwaifel, I. (2019). The real intellectual impact of cloud accounting in achieving the competitive advantage in the jordanian industrial companies. International Journal of Science and Technology Research, 8 , 1763.

Manthiramoorthy, C., & Khan, K. M. S. (2024). Comparing several encrypted cloud storage platforms. International Journal of Mathematics, Statistics, and Computer Science, 2 , 44–62.

Ogiela, U. (2020). Cognitive cryptography for data security in cloud computing. Concurrency and Computation: Practice and Experience, 32 (18), e5557.

Wang, Z., Wang, N., Su, X., & Ge, S. (2020). An empirical study on business analytics affordances enhancing the management of cloud computing data security. International Journal of Information Management., 50 , 387–394.

Chinnasamy, P., Padmavathi, S., Swathy, R., Rakesh, S. (2021). Efficient Data Security Using Hybrid Cryptography on Cloud Computing. In Inventive Communication and Computational Technologies. Springer, Singapore 537–547.

Akyol, S. (2023). A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization. Journal of Ambient Intelligence and Humanized Computing, 14 (6), 8045–8065.

Nematollahi, M., Ghaffari, A., & Mirzaei, A. (2024). Task offloading in Internet of Things based on the improved multi-objective aquila optimizer. Signal, Image and Video Processing, 18 (1), 545–552.

Kumar, P., & Bhatt, A. K. (2020). Enhancing multi-tenancy security in the cloud computing using hybrid ECC-based data encryption approach. IET Communications., 14 (18), 3212–3222.

Veerabathiran, V. K., Mani, D., Kuppusamy, S., Subramaniam, B., Velayutham, P., Sengan, S., & Krishnamoorthy, S. (2020). Improving secured ID-based authentication for cloud computing through novel hybrid fuzzy-based homomorphic proxy re-encryption. Soft Computing, 24 (24), 18893–18908.

Gupta, L.M., Garg, H., Samad, A. (2021). Secure group data sharing with an efficient key management without re-encryption scheme in cloud computing. In Proceedings of Second international conference on computing, communications, and cyber-security. Springer, Singapore 265–277.

Alabdulatif, A., Khalil, I., & Yi, X. (2020). Towards secure big data analytic for cloud-enabled applications with fully homomorphic encryption. Journal of Parallel and Distributed Computing, 137 , 192–204.

Sammeta, N., & Parthiban, L. (2021). Medical data analytics for secure multi-party-primarily based cloud computing utilizing homomorphic encryption. Journal of Scientific and Industrial Research (JSIR), 80 (08), 692–698.

Zhu, K., Wang, Z., Ding, D., Dong, H., & Xu, C. Z. (2024). Secure state estimation for artificial neural networks with unknown-but-bounded noises: a homomorphic encryption scheme. IEEE Transactions on Neural Networks and Learning Systems . https://doi.org/10.1109/TNNLS.2024.3389873

Vangujar, A. K., Ganesh, B., Umrani, A., & Palmieri, P. (2023). A novel approach to e-voting with group identity based identification and homomorphic encryption.  Cryptology ePrint Archive .

Download references

No funding is provided for the preparation of the manuscript.

Author information

Authors and affiliations.

Uttarakhand Technical University, Dehradun, India

Pawan Kumar

School of Computer Sciences and Information Technology, Uou, Haldwani, India

Ashutosh Kumar Bhatt

You can also search for this author in PubMed   Google Scholar

Contributions

All authors are equal contributions in this work.

Corresponding author

Correspondence to Pawan Kumar .

Ethics declarations

Conflict of interest.

Authors Mr. Pawan Kumar & Dr. Ashutosh Kumar Bhatt declares that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to Participate

All the authors involved have agreed to participate in this submitted article.

Consent to Publish

All the authors involved in this manuscript give full consent for publication of this submitted article.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Kumar, P., Bhatt, A.K. HE-AO: An Optimization-Based Encryption Approach for Data Delivery Model in A Multi-Tenant Environment. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11565-7

Download citation

Accepted : 02 September 2024

Published : 17 September 2024

DOI : https://doi.org/10.1007/s11277-024-11565-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cloud computing
• Key generation
• Homomorphic encryption
• Aquila optimization
• Optimal keys
• Find a journal
• Publish with us
• Track your research

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Journal Proposal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Edge computing in healthcare: innovations, opportunities, and challenges.

1. Introduction

• An overview of core concepts related to edge computing, highlighting characteristics, use cases, and challenges.
• The definition of a systematic review methodology based on PRISMA by defining a set of research questions and clear inclusion/exclusion criteria.
• An analysis and classification of the selected articles considering the most important research topics, the used techniques, identified gaps, and future research.
• A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for a better understanding of edge computing in healthcare research.

2. Edge Computing Overview

2.1. main characteristics.

• Proximity to data sources: Edge computing brings computation and data storage close to the location where data are generated, allowing real-time processing [ 2 , 14 , 15 ].
• Reduced latency: One of the most significant advantages of edge computing is its ability to offer low latency by processing data locally rather than sending them to centralized cloud data centers far from the data source. This is critical for delay-sensitive applications [ 3 , 4 , 6 ].
• Bandwidth reduction: Edge computing reduces the amount of data that must travel over the network, by employing data processing locally, thereby saving bandwidth and reducing network congestion [ 2 , 16 ].
• Enhanced security and privacy: Processing data locally on edge devices can enhance data security and privacy, as sensitive information does not need to traverse the internet to reach a centralized cloud server. With proper security protocols, data breaches or leaks can be mitigated [ 10 , 16 ].
• Mobility support: Due to the dynamic nature of edge locations and the devices connected within these networks, edge computing offers robust support for mobility, effectively handling the changing conditions and locations of devices [ 2 , 15 ].
• Location awareness: Edge computing systems are aware of their geographical location, which can be leveraged to deliver localized services such as content delivery, local resource sharing, and regional data processing [ 17 , 18 ].
• Heterogeneity: Edge computing can support a diverse range of devices, applications, and services through specific standards and data models [ 2 , 19 ].

2.2. Use Cases and Application Domains

• allows for real-time decisions and enables for immediate response from professionals.
• reduces the risk of data breaches by processing and storing personal information at the edge.
• bandwidth and cost reduction by transmitting only the mandatory information to centralized entities.
• allows AI model deployment closer to the patient, enables personalization of AI models, and the prediction of health issues based on real-time data from the monitoring devices.

2.3. Challenges

• Identify the main use cases of edge computing in healthcare.
• Examine the development of edge computing solutions in healthcare systems.
• Investigate the technical challenges in the edge computing integration into eHealth systems.
• Determine the future research directions and potential advancements in edge computing for improving healthcare technologies.
• Edge Computing Artificial Intelligence Healthcare
• Edge Computing and Ambient Intelligence
• Edge Computing and Personalized Care
• Edge Computing and Active Assisted Living
• Edge Computing and Ambient Assisted Living
• Edge Computing and Remote Care
• Edge Computing Data Privacy and Security Healthcare

4. Literature Review

4.1. privacy and security, 4.2. ai-based optimization in edge environments, 4.3. edge offloading and computational distribution.

5. Discussion

6. conclusions, author contributions, data availability statement, conflicts of interest.

• Wang, X.; Han, Y.; Leung, V.C.M.; Niyato, D.; Yan, X.; Chen, X. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2020 , 22 , 869–904. [ Google Scholar ] [ CrossRef ]
• Khan, W.Z.; Ahmed, E.; Hakak, S.; Yaqoob, I.; Ahmed, A. Edge computing: A survey. Future Gener. Comput. Syst. 2019 , 97 , 219–235. [ Google Scholar ] [ CrossRef ]
• Yu, W.; Liang, F.; He, X.; Hatcher, W.G.; Lu, C.; Lin, J.; Yang, X. A Survey on the Edge Computing for the Internet of Things. IEEE Access 2018 , 6 , 6900–6919. [ Google Scholar ] [ CrossRef ]
• Chen, J.; Ran, X. Deep Learning with Edge Computing: A review. Proc. IEEE 2019 , 107 , 1655–1674. [ Google Scholar ] [ CrossRef ]
• Alahi, M.E.E.; Sukkuea, A.; Tina, F.W.; Nag, A.; Kurdthongmee, W.; Suwannarat, K.; Mukhopadhyay, S.C. Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends. Sensors 2023 , 23 , 5206. [ Google Scholar ] [ CrossRef ]
• Pan, J.; McElhannon, J. Future Edge Cloud and Edge Computing for Internet of Things Applications. IEEE Internet Things J. 2018 , 5 , 439–449. [ Google Scholar ] [ CrossRef ]
• Anghel, I.; Cioara, T.; Moldovan, D.; Antal, M.; Pop, C.D.; Salomie, I.; Pop, C.B.; Chifu, V.R. Smart Environments and Social Robots for Age-Friendly Integrated Care Services. Int. J. Environ. Res. Public Health 2020 , 17 , 3801. [ Google Scholar ] [ CrossRef ]
• Cicirelli, G.; Marani, R.; Petitti, A.; Milella, A.; D’Orazio, T. Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population. Sensors 2021 , 21 , 3549. [ Google Scholar ] [ CrossRef ]
• Deng, S.; Zhao, H.; Fang, W.; Yin, J.; Dustdar, S.; Zomaya, A.Y. Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. IEEE Internet Things J. 2020 , 7 , 7457–7469. [ Google Scholar ] [ CrossRef ]
• Ometov, A.; Molua, O.L.; Komarov, M.; Nurmi, J. A Survey of Security in Cloud, Edge, and Fog Computing. Sensors 2022 , 22 , 927. [ Google Scholar ] [ CrossRef ]
• Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021 , 372 , n71. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Arcas, G.I.; Cioara, T.; Anghel, I.; Lazea, D.; Hangan, A. Edge Offloading in Smart Grid. Smart Cities 2024 , 7 , 680–711. [ Google Scholar ] [ CrossRef ]
• Premsankar, G.; Di Francesco, M.; Taleb, T. Edge Computing for the Internet of Things: A Case Study. IEEE Internet Things J. 2018 , 5 , 1275–1284. [ Google Scholar ] [ CrossRef ]
• Zhou, Z.; Chen, X.; Li, E.; Zeng, L.; Luo, K.; Zhang, J. Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing. Proc. IEEE 2019 , 107 , 1738–1762. [ Google Scholar ] [ CrossRef ]
• Satyanarayanan, M. The Emergence of Edge Computing. Computer 2017 , 50 , 30–39. [ Google Scholar ] [ CrossRef ]
• Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016 , 3 , 637–646. [ Google Scholar ] [ CrossRef ]
• Sun, X.; Ansari, N. EdgeIoT: Mobile Edge Computing for the Internet of Things. IEEE Commun. Mag. 2016 , 54 , 22–29. [ Google Scholar ] [ CrossRef ]
• Shi, W.; Dustdar, S. The Promise of Edge Computing. Computer 2016 , 49 , 78–81. [ Google Scholar ] [ CrossRef ]
• Ai, Y.; Peng, M.; Zhang, K. Edge Computing Technologies for Internet of Things: A Primer. Digit. Commun. Netw. 2018 , 4 , 77–86. [ Google Scholar ] [ CrossRef ]
• Arcas, G.I.; Cioara, T.; Anghel, I. Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services. Biomimetics 2024 , 9 , 302. [ Google Scholar ] [ CrossRef ]
• Dastjerdi, A.V.; Buyya, R. Fog Computing: Helping the Internet of Things Realize Its Potential. Computer 2016 , 49 , 112–116. [ Google Scholar ] [ CrossRef ]
• Li, H.; Ota, K.; Dong, M. Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing. IEEE Netw. 2018 , 32 , 96–101. [ Google Scholar ] [ CrossRef ]
• Qureshi, R.; Irfan, M.; Ali, H.; Khan, A.; Nittala, A.S.; Ali, S.; Shah, A.; Gondal, T.M.; Sadak, F.; Shah, Z.; et al. Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review. IEEE Access 2023 , 11 , 61600–61620. [ Google Scholar ] [ CrossRef ]
• Rauniyar, A.; Hagos, D.H.; Jha, D.; Håkegård, J.E.; Bagci, U.; Rawat, D.B.; Vlassov, V. Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. IEEE Internet Things J. 2024 , 11 , 7374–7398. [ Google Scholar ] [ CrossRef ]
• Ekiz, D.; Can, Y.S.; Dardagan, Y.C.; Ersoy, C. Can a Smartband be Used for Continuous Implicit Authentication in Real Life? IEEE Access 2020 , 8 , 59402–59411. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Wei, X.; Cao, J.; Ning, J.; Ying, Z.; Zheng, D. Blockchain-Enabled Decentralized Attribute-Based Access Control with Policy Hiding for Smart Healthcare. J. King Saud Univ.-Comput. Inf. Sci. 2022 , 34 Pt A , 8350–8361. [ Google Scholar ] [ CrossRef ]
• Mandarino, V.; Pappalardo, G.; Tramontana, E. A Blockchain-Based Electronic Health Record (EHR) System for Edge Computing Enhancing Security and Cost Efficiency. Computers 2024 , 13 , 132. [ Google Scholar ] [ CrossRef ]
• Akkaoui, R.; Hei, X.; Cheng, W. EdgeMediChain: A Hybrid Edge Blockchain-Based Framework for Health Data Exchange. IEEE Access 2020 , 8 , 113467–113486. [ Google Scholar ] [ CrossRef ]
• Rani, S.; Kataria, A.; Kumar, S.; Tiwari, P. Federated Learning for Secure IoMT-Applications in Smart Healthcare Systems: A Comprehensive Review. Knowl.-Based Syst. 2023 , 274 , 110658. [ Google Scholar ] [ CrossRef ]
• Mayer, A.H.; Rodrigues, V.F.; da Costa, C.A.; da Rosa Righi, R.; Roehrs, A.; Antunes, R.S. FogChain: A Fog Computing Architecture Integrating Blockchain and Internet of Things for Personal Health Records. IEEE Access 2021 , 9 , 122723–122737. [ Google Scholar ] [ CrossRef ]
• Ejaz, M.; Kumar, T.; Kovacevic, I.; Ylianttila, M.; Harjula, E. Health-BlockEdge: Blockchain-Edge Framework for Reliable Low-Latency Digital Healthcare Applications. Sensors 2021 , 21 , 2502. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Annane, B.; Alti, A.; Lakehal, A. Blockchain-Based Context-Aware CP-ABE Schema for Internet of Medical Things Security. Array 2022 , 14 , 100150. [ Google Scholar ] [ CrossRef ]
• Dammak, B.; Turki, M.; Cheikhrouhou, S.; Baklouti, M.; Mars, R.; Dhahbi, A. LoRaChainCare: An IoT Architecture Integrating Blockchain and LoRa Network for Personal Health Care Data Monitoring. Sensors 2022 , 22 , 1497. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Papadopoulos, G.T.; Antona, M.; Stephanidis, C. Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning. IEEE Access 2021 , 9 , 73890–73909. [ Google Scholar ] [ CrossRef ]
• Humayun, M.; Alsirhani, A.; Alserhani, F.; Shaheen, M.; Alwakid, G. Transformative synergy: SSEHCET—Bridging mobile edge computing and AI for enhanced eHealth security and efficiency. J. Cloud Comput. 2024 , 13 , 37. [ Google Scholar ] [ CrossRef ]
• Chamikara, M.A.P.; Bertok, P.; Khalil, I.; Liu, D.; Camtepe, S. Privacy preserving distributed machine learning with federated learning. Comput. Commun. 2021 , 171 , 112–125. [ Google Scholar ] [ CrossRef ]
• Attaullah, H.; Anjum, A.; Kanwal, T.; Malik, S.U.R.; Asheralieva, A.; Malik, H.; Zoha, A.; Arshad, K.; Imran, M.A. F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. Sensors 2021 , 21 , 4933. [ Google Scholar ] [ CrossRef ]
• Rachakonda, L.; Bapatla, A.K.; Mohanty, S.P.; Kougianos, E. SaYoPillow: Blockchain-Integrated Privacy-Assured IoMT Framework for Stress Management Considering Sleeping Habits. IEEE Trans. Consum. Electron. 2021 , 67 , 20–29. [ Google Scholar ] [ CrossRef ]
• Sánchez-Gallegos, D.D.; Galaviz-Mosqueda, G.; González, J.L.; Villarreal, S.; Perez-Ramos, A.-E.; Carrizales-Espinoza, D.; Carretero, J. On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition. IEEE Access 2020 , 8 , 120255–120281. [ Google Scholar ] [ CrossRef ]
• Loghin, D.; Ta, Q.-T.; Wang, W.; Xiao, X.; Yang, Y.; Zhang, M.; Zhang, Z.; Cai, S.; Chen, G.; Dinh, A.; et al. The Disruptions of 5G on Data-Driven Technologies and Applications. IEEE Trans. Knowl. Data Eng. 2020 , 32 , 1179–1198. [ Google Scholar ] [ CrossRef ]
• Rathore, S.; Park, J.H.; Chang, H. Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT. IEEE Access 2021 , 9 , 90075–90083. [ Google Scholar ] [ CrossRef ]
• Akter, M.; Moustafa, N.; Turnbull, B. SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems. Cogn. Comput. 2024 , 16 , 2626–2641. [ Google Scholar ] [ CrossRef ]
• Saheed, Y.K.; Arowolo, M.O. Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms. IEEE Access 2021 , 9 , 161546–161554. [ Google Scholar ] [ CrossRef ]
• Ferrag, M.A.; Friha, O.; Maglaras, L.; Janicke, H.; Shu, L. Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis. IEEE Access 2021 , 9 , 138509–138542. [ Google Scholar ] [ CrossRef ]
• Raza, A.; Tran, K.P.; Koehl, L.; Li, S. AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description. Eng. Appl. Artif. Intell. 2023 , 121 , 106051. [ Google Scholar ] [ CrossRef ]
• Abdullah, S.; Arshad, J.; Khan, M.M.; Alazab, M.; Salah, K. PRISED tangle: A privacy-aware framework for smart healthcare data sharing using IOTA tangle. Complex. Intell. Syst. 2023 , 9 , 3023–3041. [ Google Scholar ] [ CrossRef ]
• Zubair, M.; Ghubaish, A.; Unal, D.; Al-Ali, A.; Reimann, T.; Alinier, G.; Hammoudeh, M.; Qadir, J. Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System. Sensors 2022 , 22 , 8280. [ Google Scholar ] [ CrossRef ]
• Rehman, A.; Saba, T.; Haseeb, K.; Alam, T.; Lloret, J. Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services. Sustainability 2022 , 14 , 12185. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Ouda, A.; Abu-Rukba, R. Authentication and Key Agreement Protocol in Hybrid Edge–Fog–Cloud Computing Enhanced by 5G Networks. Future Internet 2024 , 16 , 209. [ Google Scholar ] [ CrossRef ]
• Hernandez-Jaimes, M.L.; Martinez-Cruz, A.; Ramírez-Gutiérrez, K.A.; Feregrino-Uribe, C. Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud–Fog–Edge architectures. Internet Things 2023 , 23 , 100887. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Zhang, F.; Huang, X.; Liu, X. Leakage-Resilient Authenticated Key Exchange for Edge Artificial Intelligence. IEEE Trans. Dependable Secur. Comput. 2021 , 18 , 2835–2847. [ Google Scholar ] [ CrossRef ]
• Ullah, I.; Khan, M.A.; Alkhalifah, A.; Nordin, R.; Alsharif, M.H.; Alghtani, A.H.; Aly, A.A. A Multi-Message Multi-Receiver Signcryption Scheme with Edge Computing for Secure and Reliable Wireless Internet of Medical Things Communications. Sustainability 2021 , 13 , 13184. [ Google Scholar ] [ CrossRef ]
• Zhang, L.; Wang, X.; Wang, J.; Pung, R.; Wang, H.; Lam, K.-Y. An Efficient FHE-Enabled Secure Cloud–Edge Computing Architecture for IoMT Data Protection with its Application to Pandemic Modeling. IEEE Internet Things J. 2024 , 11 , 15272–15284. [ Google Scholar ] [ CrossRef ]
• Scrugli, M.A.; Loi, D.; Raffo, L.; Meloni, P. An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things. IEEE Access 2022 , 10 , 1688–1705. [ Google Scholar ] [ CrossRef ]
• Djelouat, H.; Al Disi, M.; Boukhenoufa, I.; Amira, A.; Bensaali, F.; Kotronis, C.; Politi, E.; Nikolaidou, M.; Dimitrakopoulos, G. Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device. Microprocess. Microsyst. 2020 , 72 , 102839. [ Google Scholar ] [ CrossRef ]
• Irshad, R.R.; Hussain, S.; Sohail, S.S.; Zamani, A.S.; Madsen, D.Ø.; Alattab, A.A.; Ahmed, A.A.A.; Norain, K.A.A.; Alsaiari, O.A.S. A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer. Sensors 2023 , 23 , 2932. [ Google Scholar ] [ CrossRef ]
• Sakib, S.; Fouda, M.M.; Al-Mahdawi, M.; Mohsen, A.; Oogane, M.; Ando, Y.; Fadlullah, Z.M. Deep Learning Models for Magnetic Cardiography Edge Sensors Implementing Noise Processing and Diagnostics. IEEE Access 2022 , 10 , 2656–2668. [ Google Scholar ] [ CrossRef ]
• Velichko, A.; Huyut, M.T.; Belyaev, M.; Izotov, Y.; Korzun, D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. Sensors 2022 , 22 , 7886. [ Google Scholar ] [ CrossRef ]
• Hemalatha, M. A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection. Expert Syst. Appl. 2022 , 210 , 118227. [ Google Scholar ] [ CrossRef ]
• Antal, C.; Cioara, T.; Antal, M.; Anghel, I. Blockchain Platform For COVID-19 Vaccine Supply Management. IEEE Open J. Comput. Soc. 2021 , 2 , 164–178. [ Google Scholar ] [ CrossRef ]
• Badawy, M.; Balaha, H.M.; Maklad, A.S.; Almars, A.M.; Elhosseini, M.A. Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs. Biomimetics 2023 , 8 , 499. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wan, S.; Gu, Z.; Ni, Q. Cognitive computing and wireless communications on the edge for healthcare service robots. Comput. Commun. 2020 , 149 , 99–106. [ Google Scholar ] [ CrossRef ]
• Kim, J.; Kang, H.; Yang, J.; Jung, H.; Lee, S.; Lee, J. Multitask Deep Learning for Human Activity, Speed, and Body Weight Estimation Using Commercial Smart Insoles. IEEE Internet Things J. 2023 , 10 , 16121–16133. [ Google Scholar ] [ CrossRef ]
• Alekseeva, D.; Ometov, A.; Arponen, O.; Lohan, E.S. The future of computing paradigms for medical and emergency applications. Comput. Sci. Rev. 2022 , 45 , 100494. [ Google Scholar ] [ CrossRef ]
• Wang, W.-H.; Hsu, W.-S. Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments. Sensors 2023 , 23 , 5913. [ Google Scholar ] [ CrossRef ]
• Elbagoury, B.M.; Vladareanu, L.; Vlădăreanu, V.; Salem, A.B.; Travediu, A.-M.; Roushdy, M.I. A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform. Sensors 2023 , 23 , 3500. [ Google Scholar ] [ CrossRef ]
• Paramasivam, A.; Shahila, D.F.D.; Jenath, M.; Sivakumaran, T.S.; Sankaran, S.; Reddy Pittu, P.S.K.; Vijayalakshmi, S. Development of artificial intelligence edge computing based wearable device for fall detection and prevention of elderly people. Heliyon 2024 , 10 , e28688. [ Google Scholar ]
• Monti, L.; Tse, R.; Tang, S.-K.; Mirri, S.; Delnevo, G.; Maniezzo, V.; Salomoni, P. Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context. Sensors 2022 , 22 , 3692. [ Google Scholar ] [ CrossRef ]
• Wilhelm, S.; Kasbauer, J. Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach. Sensors 2021 , 21 , 8036. [ Google Scholar ] [ CrossRef ]
• Janbi, N.; Mehmood, R.; Katib, I.; Albeshri, A.; Corchado, J.M.; Yigitcanlar, T. Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge. Sensors 2022 , 22 , 1854. [ Google Scholar ] [ CrossRef ]
• Chen, M.; Zhou, P.; Wu, D.; Hu, L.; Hassan, M.M.; Alamri, A. AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework. Inf. Fusion 2020 , 54 , 1–9. [ Google Scholar ] [ CrossRef ]
• Jain, V.; Gupta, G.; Gupta, M.; Sharma, D.K.; Ghosh, U. Ambient intelligence-based multimodal human action recognition for autonomous systems. ISA Trans. 2023 , 132 , 94–108. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Arikumar, K.S.; Prathiba, S.B.; Alazab, M.; Gadekallu, T.R.; Pandya, S.; Khan, J.M.; Moorthy, R.S. FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems. Sensors 2022 , 22 , 1377. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kumar, A.; Sharma, K.; Sharma, A. Genetically optimized Fuzzy C-means data clustering of IoMT-based biomarkers for fast affective state recognition in intelligent edge analytics. Appl. Soft Comput. 2021 , 109 , 107525. [ Google Scholar ] [ CrossRef ]
• Sodhro, A.H.; Zahid, N. AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications. Sensors 2021 , 21 , 8039. [ Google Scholar ] [ CrossRef ]
• Lakhan, A.; Sodhro, A.H.; Majumdar, A.; Khuwuthyakorn, P.; Thinnukool, O. A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks. Sensors 2022 , 22 , 2379. [ Google Scholar ] [ CrossRef ]
• Kumar, A.; Krishnamurthi, R.; Nayyar, A.; Sharma, K.; Grover, V.; Hossain, E. A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes. IEEE Access 2020 , 8 , 118433–118471. [ Google Scholar ] [ CrossRef ]
• Maksymyuk, T.; Gazda, J.; Bugár, G.; Gazda, V.; Liyanage, M.; Dohler, M. Blockchain-Empowered Service Management for the Decentralized Metaverse of Things. IEEE Access 2022 , 10 , 99025–99037. [ Google Scholar ] [ CrossRef ]
• Lakhan, A.; Mohammed, M.A.; Abdulkareem, K.H.; Jaber, M.M.; Nedoma, J.; Martinek, R.; Zmij, P. Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks. Sensors 2022 , 22 , 5937. [ Google Scholar ] [ CrossRef ]
• Bojović, P.D.; Malbašić, T.; Vujošević, D.; Martić, G.; Bojović, Ž. Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programmability. Sensors 2022 , 22 , 2849. [ Google Scholar ] [ CrossRef ]
• Kim, J.A.; Park, D.G.; Jeong, J. Design and performance evaluation of cost-effective function-distributed mobility management scheme for software-defined smart factory networking. J. Ambient. Intell. Humaniz. Comput. 2020 , 11 , 2291–2307. [ Google Scholar ] [ CrossRef ]
• Pandya, S.; Srivastava, G.; Jhaveri, R.; Babu, M.R.; Bhattacharya, S.; Maddikunta, P.K.R.; Mastorakis, S.; Piran, M.J.; Gadekallu, T.R. Federated learning for smart cities: A comprehensive survey. Sustain. Energy Technol. Assess. 2023 , 55 , 102987. [ Google Scholar ] [ CrossRef ]
• Talaat, F.M. Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system. Multimed. Tools Appl. 2022 , 81 , 8235–8258. [ Google Scholar ] [ CrossRef ]
• Shumba, A.-T.; Montanaro, T.; Sergi, I.; Fachechi, L.; De Vittorio, M.; Patrono, L. Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications. Sensors 2022 , 22 , 7675. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nasser, N.; Fadlullah, Z.M.; Fouda, M.M.; Ali, A.; Imran, M. A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept. Comput. Netw. 2022 , 205 , 108672. [ Google Scholar ] [ CrossRef ]
• Lin, B.; Yu, T.; Peng, C.; Lin, C.; Hsu, H.; Lee, I.; Zhang, Z. Fall Detection System with Artificial Intelligence-Based Edge Computing. IEEE Access 2022 , 10 , 4328–4339. [ Google Scholar ] [ CrossRef ]
• Velichko, A. A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare. Sensors 2021 , 21 , 6209. [ Google Scholar ] [ CrossRef ]
• Shynu, P.G.; Menon, V.G.; Kumar, R.L.; Kadry, S.; Nam, Y. Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing. IEEE Access 2021 , 9 , 45706–45720. [ Google Scholar ] [ CrossRef ]
• Mutlag, A.A.; Ghani, M.K.A.; Mohammed, M.A.; Lakhan, A.; Mohd, O.; Abdulkareem, K.H.; Garcia-Zapirain, B. Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring. Sensors 2021 , 21 , 6923. [ Google Scholar ] [ CrossRef ]
• Gómez-Valiente, P.; Benedí, J.P.; Lillo-Castellano, J.M.; Marina-Breysse, M. Smart-IoT Business Process Management: A Case Study on Remote Digital Early Cardiac Arrhythmia Detection and Diagnosis. IEEE Internet Things J. 2023 , 10 , 16744–16757. [ Google Scholar ] [ CrossRef ]
• Hassan, S.R.; Ahmad, I.; Ahmad, S.; Alfaify, A.; Shafiq, M. Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture. Sensors 2020 , 20 , 6574. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wen, F.; He, T.; Liu, H.; Chen, H.-Y.; Zhang, T.; Lee, C. Advances in Chemical Sensing Technology for Enabling the Next-Generation Self-Sustainable Integrated Wearable System in the IoT Era. Nano Energy 2020 , 78 , 105155. [ Google Scholar ] [ CrossRef ]
• Syed Sabir Mohamed, S.; Gopi, R.; Thiruppathy Kesavan, V.; Kaliyaperumal, K. Adaptive heuristic edge assisted fog computing design for healthcare data optimization. J. Cloud Comput. 2024 , 13 , 127. [ Google Scholar ]
• Liu, Q.; Tian, Z.; Wang, N.; Lin, Y. DRL-based dependent task offloading with delay-energy tradeoff in medical image edge computing. Complex. Intell. Syst. 2024 , 10 , 3283–3304. [ Google Scholar ] [ CrossRef ]
• Kumar, R.H.; Rajaram, B. Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System. IEEE Access 2024 , 12 , 45456–45474. [ Google Scholar ] [ CrossRef ]
• Sachin, D.N.; Annappa, B.; Hegde, S.; Abhijit, C.S.; Ambesange, S. FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments. IEEE Access 2024 , 12 , 15867–15883. [ Google Scholar ]

Click here to enlarge figure

Share and Cite

Rancea, A.; Anghel, I.; Cioara, T. Edge Computing in Healthcare: Innovations, Opportunities, and Challenges. Future Internet 2024 , 16 , 329. https://doi.org/10.3390/fi16090329

Rancea A, Anghel I, Cioara T. Edge Computing in Healthcare: Innovations, Opportunities, and Challenges. Future Internet . 2024; 16(9):329. https://doi.org/10.3390/fi16090329

Rancea, Alexandru, Ionut Anghel, and Tudor Cioara. 2024. "Edge Computing in Healthcare: Innovations, Opportunities, and Challenges" Future Internet 16, no. 9: 329. https://doi.org/10.3390/fi16090329

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals