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Abid, R., Iwendi, C., Javed, A. R., Rizwan, M., Jalil, Z., Anajemba, J. H. & Biamba, C. (2023). An optimised homomorphic CRT-RSA algorithm for secure and efficient communication. Personal and Ubiquitous Computing, 27, 1405-1418
Open this publication in new window or tab >>An optimised homomorphic CRT-RSA algorithm for secure and efficient communication
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2023 (English)In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 27, p. 1405-1418Article in journal (Refereed) Published
Abstract [en]

Secure and reliable exchange of information between devices is crucial for any network in the current digital world. This information is maintained on storage devices, routing devices, and communication over the cloud. Cryptographic techniques are used to ensure the secure transmission of data, ensuring the user’s privacy by storing and transmitting data in a particular format. Using encryption, only the intended user possessing the key can access the information. During data or essential transmission, the channel should be secured by using robust encryption techniques. Homomorphic Encryption (HE) techniques have been used in the past for this purpose. However, one of the flaws of the conventional HE is seen either in its slow transmission or fast key decryption. Thus, this paper proposes an optimized Homomorphic Encryption Chinese Remainder Theorem with a Rivest-Shamir-Adleman (HE-CRT-RSA) algorithm to overcome this challenge. The proposed Technique, HE-CRT-RSA, utilizes multiple keys for efficient communication and security. In addition, the performance of the HE-CRT-RSA algorithm was evaluated in comparison with the classical RSA algorithm. The result of the proposed algorithm shows performance improvement with reduced decryption time. It is observed that the proposed HE-CRT-RSA is 3–4% faster than the classical Rivest-Shamir-Adleman (RSA). The result also suggests that HE-CRT-RSA effectively enhances security issues of the cloud and helps to decrease the involvement of intruders or any third party during communication or inside the data/server centers.

Place, publisher, year, edition, pages
Springer, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-37019 (URN)10.1007/s00779-021-01607-3 (DOI)2-s2.0-85114039999 (Scopus ID)
Available from: 2021-09-13 Created: 2021-09-13 Last updated: 2023-09-04Bibliographically approved
Zhou, J., Lilhore, U. K., M, P., Hai, T., Simaiya, S., Jawawi, D. N., . . . Hamdi, M. (2023). Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing. Journal of Cloud Computing: Advances, Systems and Applications, 12(1), Article ID 85.
Open this publication in new window or tab >>Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing
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2023 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 12, no 1, article id 85Article in journal (Refereed) Published
Abstract [en]

Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Cloud computing; Load balancing; Load balancing metrics; Metaheuristic algorithms; Resource management
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-42457 (URN)10.1186/s13677-023-00453-3 (DOI)001006120400002 ()2-s2.0-85161816636 (Scopus ID)
Available from: 2023-06-26 Created: 2023-06-26 Last updated: 2023-11-15Bibliographically approved
Hai, T., Zhou, J., Lu, Y., Jawawi, D., Wang, D., Onyema, E. M. & Biamba, C. (2023). Enhanced security using multiple paths routine scheme in cloud-MANETs. Journal of Cloud Computing: Advances, Systems and Applications, 12(1), Article ID 68.
Open this publication in new window or tab >>Enhanced security using multiple paths routine scheme in cloud-MANETs
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2023 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 12, no 1, article id 68Article in journal (Refereed) Published
Abstract [en]

Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

Place, publisher, year, edition, pages
Springer, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-41765 (URN)10.1186/s13677-023-00443-5 (DOI)000978588600001 ()2-s2.0-85156195052 (Scopus ID)
Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-11-23Bibliographically approved
Hai, T., Zhou, J., Jawawi, D., Wang, D., Oduah, U., Biamba, C. & Jain, S. K. (2023). Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes. Journal of Cloud Computing: Advances, Systems and Applications, 12, Article ID 15.
Open this publication in new window or tab >>Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes
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2023 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 12, article id 15Article in journal (Refereed) Published
Abstract [en]

Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Cloud Computing; HEFT Algorithm; NP-complete; Task Scheduling
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-41025 (URN)10.1186/s13677-022-00374-7 (DOI)000921008500001 ()2-s2.0-85146924908 (Scopus ID)
Available from: 2023-02-06 Created: 2023-02-06 Last updated: 2023-11-15Bibliographically approved
Yin, F., Jiao, X., Zhou, J., Yin, X., Ibeke, E., Iwendi, M. G. & Biamba, C. (2022). Fintech application on banking stability using Big Data of an emerging economy. Journal of Cloud Computing: Advances, Systems and Applications, 11(1), Article ID 43.
Open this publication in new window or tab >>Fintech application on banking stability using Big Data of an emerging economy
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2022 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 11, no 1, article id 43Article in journal (Refereed) Published
Abstract [en]

The rapid growth and development of financial technological advancement (Fintech) services and innovations have attracted the attention of scholars who are now on a quest to analyse their impact on the banking sector. This study conducts several kinds of analyses to measure the effect of the fintech era on the stability of the Chinese banking sector. It uses Big Data and performs Pearson correlation and regression analysis on the fintech era’s transition period to measure the impact of several explanatory variables— institutional regulation, government stability, bank credit to deposit ratio, and economic growth— on the outcome variables, which includes Nonperforming loans (NPLs) and its numerical measurement in relation to the mean score of the Big Data (Z-score). This study uses yearly Big Data from 1995–2018 and revealed that compared to the first wave of the fintech era, the second wave helped in the reduction of NPLs and the enhancement of financial stability in China. This study concludes that in the second wave of the fintech era, the explanatory variables mentioned above had a positive impact on NPLs and banking stability. This work helps comprehend fintech development in modern society and the importance of its disruptive forces in developing and developed countries.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Banking stability, Fintech, Innovations, Big data, Nonperforming loans, Corruption, First wave, Interaction analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-39967 (URN)10.1186/s13677-022-00320-7 (DOI)000853856300001 ()2-s2.0-85138019701 (Scopus ID)
Available from: 2022-09-22 Created: 2022-09-22 Last updated: 2023-11-15Bibliographically approved
Ogunji, C. V., Onwe, J. O., Ngwa, E. S., David, E., Olaolu, M. & Biamba, C. (2022). Higher education and the new normal: implications for sustainable post covid-19 era in Nigerian tertiary institutions. Cogent Education, 9(1), Article ID 2125206.
Open this publication in new window or tab >>Higher education and the new normal: implications for sustainable post covid-19 era in Nigerian tertiary institutions
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2022 (English)In: Cogent Education, E-ISSN 2331-186X, Vol. 9, no 1, article id 2125206Article in journal (Refereed) Published
Abstract [en]

This study assessed readiness of Nigerian Tertiary institutions towards adopting e-learning education as a new normal post COVID-19, identified e-learning packages available for use in the institutions before and during the COVID-19 Lockdown using the E-Learning Survey for Academic Staff and Students of Nigerian Tertiary Institutions (ELSASSoNTI). This research adopted an online survey using a quantitative method of data collection. A structured Google Form questionnaire was shared with academic staff and students of public and private tertiary institutions in Nigeria via different online platforms. Population comprised all academic staff and students of South-East Nigerian Tertiary Institutions. A sample size of 615 academic staff and students responded to the instrument. Data were analyzed using descriptive and inferential statistics. Results revealed that: tertiary institutions in Nigeria are to a larger extent not ready for the adoption of e-learning education approaches as teaching-learning alternative during emergencies. Majority of tertiary institutions except private universities did not adopt any e-learning platform for use before and during the COVID-19 lockdown. There is lack of basic resources, logistics, and inadequate capacity for the effective adoption and implementation of e-learning within Nigerian tertiary institutions. The study thus recommends, among other things; provision of facilities needed for smooth transition to the new normal, training programs to improve the confidence of academic staff and students in using e-learning platforms. These would improve their e-learning readiness, overcome the usual disruption of school activities during emergencies and ensure a sustainable post Covid-19 era in the higher education sector.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
Higher education, E-learning readiness, Nigerian tertiary institutions, post COVID-19, New normal
National Category
Educational Sciences
Identifiers
urn:nbn:se:hig:diva-40068 (URN)10.1080/2331186x.2022.2125206 (DOI)000856901900001 ()2-s2.0-85138683267 (Scopus ID)
Available from: 2022-10-06 Created: 2022-10-06 Last updated: 2022-10-10Bibliographically approved
Zhang, R., Zhou, J., Hai, T., Zhang, S., Iwendi, M., Biamba, C. & Anumbe, N. (2022). Quality assurance awareness in higher education in China: big data challenges. Journal of Cloud Computing: Advances, Systems and Applications, 11(1), Article ID 56.
Open this publication in new window or tab >>Quality assurance awareness in higher education in China: big data challenges
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2022 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 11, no 1, article id 56Article in journal (Refereed) Published
Abstract [en]

The quality assurance of higher education in China is an issue of vital international interest. To improve the international reputation of the nation’s universities, steps must be taken to ensure a sustained focus on the quality assurance within its ranks. This paper is primarily focused on the quality assurance models operational in Chinese universities, the Big data challenges and the legal framework backing them. The paper also discusses the implementation of the models, the extent to which they meet international standards, and how they adhere to prevailing laws. The degree of success in attaining and maintaining quality and evaluation of quality improvement opportunities are also discussed. Some of the solutions recommended in this study are the participation of more teachers and students in quality management, more emphasis of Higher Education Institution (HEI) quality assurance on self-regulation and a learning-oriented approach and conducting sessions to collect anonymous feedback from students to reward staff with best practices. Some of the Quality Assurance practices/models adopted in Chinese Universities are the Ministry of Education (MOE) reviews; the Academic Degree Committee oversight; Higher Education Evaluation Center (HEEC) overview, University self-evaluation according to HEEC Indicators, and the Webometric Ranking Model.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Analytics; Big data; HEEC; Higher education; Quality assurance; Quality management
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:hig:diva-40269 (URN)10.1186/s13677-022-00321-6 (DOI)000861900500004 ()2-s2.0-85139219268 (Scopus ID)
Available from: 2022-10-17 Created: 2022-10-17 Last updated: 2023-11-15Bibliographically approved
Ramasamy, L. K., Khan, F., Shah, M., Prasad, B. V., Iwendi, C. & Biamba, C. (2022). Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring. Sensors, 22(3), Article ID 1076.
Open this publication in new window or tab >>Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 3, article id 1076Article in journal (Refereed) Published
Abstract [en]

The functionality of the Internet is continually changing from the Internet of Computers (IoC) to the “Internet of Things (IoT)”. Most connected systems, called Cyber-Physical Systems (CPS), are formed from the integration of numerous features such as humans and the physical environment, smart objects, and embedded devices and infrastructure. There are a few critical problems, such as security risks and ethical issues that could affect the IoT and CPS. When every piece of data and device is connected and obtainable on the network, hackers can obtain it and utilise it for different scams. In medical healthcare IoT-CPS, everyday medical and physical data of a patient may be gathered through wearable sensors. This paper proposes an AI-enabled IoT-CPS which doctors can utilise to discover diseases in patients based on AI. AI was created to find a few disorders such as Diabetes, Heart disease and Gait disturbances. Each disease has various symptoms among patients or elderly. Dataset is retrieved from the Kaggle repository to execute AI-enabled IoT-CPS technology. For the classification, AI-enabled IoT-CPS Algorithm is used to discover diseases. The experimental results demonstrate that compared with existing algorithms, the proposed AI-enabled IoT-CPS algorithm detects patient diseases and fall events in elderly more efficiently in terms of Accuracy, Precision, Recall and F-measure. 

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
Artificial intelligence; Classification; Cyber-Physical System; Internet of Computers; Internet of Things; Patients
National Category
Computer and Information Sciences
Research subject
Health-Promoting Work, Digital shapeshifting
Identifiers
urn:nbn:se:hig:diva-37882 (URN)10.3390/s22031076 (DOI)000755601400001 ()35161820 (PubMedID)2-s2.0-85123614825 (Scopus ID)
Available from: 2022-02-07 Created: 2022-02-07 Last updated: 2023-09-15Bibliographically approved
Krishnan, R., Nair, S., Saamuel, B. S., Justin, S., Iwendi, C., Biamba, C. & Ibeke, E. (2022). Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model. Sustainability, 14(6), Article ID 3378.
Open this publication in new window or tab >>Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model
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2022 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 14, no 6, article id 3378Article in journal (Refereed) Published
Abstract [en]

In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
learning management system; learning analytics; artificial intelligence; data visualization techniques; LA plug-ins; teaching; learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-38373 (URN)10.3390/su14063378 (DOI)000774445600001 ()2-s2.0-85126972466 (Scopus ID)
Available from: 2022-04-05 Created: 2022-04-05 Last updated: 2022-04-19Bibliographically approved
Zhou, J., Hai, T., Jawawi, D. N. A., Wang, D., Ibeke, E. & Biamba, C. (2022). Voice spoofing countermeasure for voice replay attacks using deep learning. Journal of Cloud Computing: Advances, Systems and Applications, 11(1), Article ID 51.
Open this publication in new window or tab >>Voice spoofing countermeasure for voice replay attacks using deep learning
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2022 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 11, no 1, article id 51Article in journal (Refereed) Published
Abstract [en]

In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Automatic Speaker Verification (ASV), spoofing, voice biometrics, deep learning, neural network, machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-40069 (URN)10.1186/s13677-022-00306-5 (DOI)000857838600002 ()2-s2.0-85138702714 (Scopus ID)
Available from: 2022-10-06 Created: 2022-10-06 Last updated: 2023-11-15Bibliographically approved
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