A Quantum-Optimized Decentralized Ledger for Real-Time TVET Skill Assessment via Wearable IoT Sensors: A Framework for the Kenyan Technical and Vocational Education and Training Sector

PDF (694KB), PP.94-104

Views: 0 Downloads: 0

Author(s)

David Shiala Ongoma

1. Department of ICT, Media, and Engineering, Zetech University, Nairobi, Kenya

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2026.05.07

Received: 7 Aug. 2026 / Revised: 30 Aug. 2026 / Accepted: 23 Sep. 2026 / Published: 8 Oct. 2026

Index Terms

Decentralized ledger technology, quantum-inspired optimization, wearable Internet of Things sensors, technical and vocational education and training, competency-based assessment, Kenya, consensus algorithms

Abstract

There exists an ongoing problem among technical and vocational education and training institutions in Kenya regarding validation of demonstrated practical competencies, since the verification method depends on periodic observation which is paper-based and hence prone to subjectivity and potential corruption. In this article, we propose a quantum-optimized decentralized ledger system in which trainees wear wearable Internet of Things sensors and a consensus algorithm referred to as Quantum-Optimized Decentralized Proof-of-Skill is used to record and validate skill demonstration. The sensors detect motion, physiological, and environmental signals that are converted into skill proficiency ratings using an edge-based feature-processing pipeline; validator nodes are then chosen by solving a quadratic unconstrained binary optimization problem with a quantum-inspired algorithm. Assessment records are written to a permissioned ledger accessible only to training institutions, national qualification authorities, and prospective employers. According to a discrete-event simulation of the proposed consensus algorithm relative to proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, the proposed algorithm has a reduced mean confirmation time of 148 ms and an increased sustained throughput of 386 tx/s under the modeled conditions of the Kenyan network environment, alongside skill-scoring consensus (F1 overall = 0.88) with the assessor ground truth obtained through simulations based on the accuracy of the wearable-assessments in published reports.

Cite This Paper

David Shiala Ongoma, "A Quantum-Optimized Decentralized Ledger for Real-Time TVET Skill Assessment via Wearable IoT Sensors: A Framework for the Kenyan Technical and Vocational Education and Training Sector", International Journal of Education and Management Engineering (IJEME), Vol.16, No.5, pp. 94-104, 2026. DOI:10.5815/ijeme.2026.05.07

Reference

[1]W. K. Kogo, M. Kapkiai, and S. K. Chumba, “Effectiveness of staff capacity building on integration of CBET approach in TVET institutions in the North Rift region, Kenya,” Africa Journal of Technical and Vocational Education and Training, vol. 7, no. 1, pp. 29–42, 2022, doi: 10.69641/afritvet.2022.71137.
[2]A. Tekle, S. Areaya, and G. Habtamu, “Stakeholders’ perceptions of occupational competency assessment and certification systems in Ethiopia’s TVET programs,” Higher Education, Skills and Work-Based Learning, vol. 15, no. 2, pp. 274–289, 2025. doi:10.1108/HESWBL-02-2024-0030
[3]S. Timotheou et al., “Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review,” Education and Information Technologies, vol. 28, no. 6, pp. 6695–6726, 2023. doi:10.1007/s10639-022-11431-8
[4]M. A. Santos Rego, D. Sáez-Gambín, J. L. González-Geraldo, and D. García-Romero, “Transversal competences and employability of university students: Converging towards service-learning,” Education Sciences, vol. 12, no. 4, art. 265, 2022, doi: 10.3390/educsci12040265.
[5]H. Alsobhi et al., “Blockchain-based micro-credentialing system in higher education institutions: Systematic literature review,” Knowledge-Based Systems, vol. 265, art. 110238, 2023. doi:10.1016/j.knosys.2022.110238
[6]G. Bjelobaba et al., “Blockchain technologies and digitalization in function of student work evaluation,” Sustainability, vol. 14, no. 9, art. 5333, 2022. doi:10.3390/su14095333
[7]J. Passos et al., “Wearables and Internet of Things (IoT) technologies for fitness assessment: A systematic review,” Sensors, vol. 21, no. 16, art. 5418, 2021. doi:10.3390/s21165418
[8]S. C. Mukhopadhyay, N. K. Suryadevara, and A. Nag, “Wearable sensors and systems in the IoT,” Sensors, vol. 21, no. 23, art. 7880, 2021. doi:10.3390/s21237880
[9]H. S. Saad, J. F. W. Zaki, and M. M. Abdelsalam, “Employing of machine learning and wearable devices in healthcare system: Tasks and challenges,” Neural Computing and Applications, vol. 36, no. 29, pp. 17829–17849, 2024. doi:10.1007/s00521-024-10197-z
[10]A. Nawaz et al., “Edge computing to secure IoT data ownership and trade with the Ethereum blockchain,” Sensors, vol. 20, no. 14, art. 3965, 2020. doi:10.3390/s20143965
[11]R. Bose et al., “Enhancing cloud-based healthcare security with Quantum-Secure HealthChain: A quantum computing and blockchain integrated framework,” Health Science Reports, vol. 9, no. 5, art. e72367, 2026, doi: 10.1002/hsr2.72367.
[12]M. Wazid, A. K. Das, and Y. Park, “Generic quantum blockchain-envisioned security framework for IoT environment: Architecture, security benefits and future research,” IEEE Open Journal of the Computer Society, vol. 5, pp. 248–267, 2024, doi: 10.1109/OJCS.2024.3397307.
[13]R. Bala and R. Manoharan, “Trusted consensus protocol for blockchain networks based on fuzzy inference system,” The Journal of Supercomputing, vol. 78, pp. 16951–16974, 2022. doi:10.1007/s11227-022-04510-7
[14]E. Filatovas et al., “A MCDM-based framework for blockchain consensus protocol selection,” Expert Systems with Applications, vol. 204, art. 117609, 2022. doi: 10.1016/j.eswa.2022.117609
[15]K. Blekos et al., “A review on quantum approximate optimization algorithm and its variants,” Physics Reports, vol. 1068, pp. 1–66, 2024. doi: 10.1016/j.physrep.2024.03.002
[16]T. M. Fernandez-Carames and P. Fraga-Lamas, “Towards post-quantum blockchain: A review on blockchain cryptography resistant to quantum computing attacks,” IEEE Access, vol. 8, pp. 21091–21116, 2020. doi:10.1109/ACCESS.2020.2968985
[17]N. O. Kiktenko et al., “Quantum-secured blockchain,” Quantum Science and Technology, vol. 3, no. 3, art. 035004, 2018. doi:10.1088/2058-9565/aabc6b
[18]M. Elkhodr, “An AI-driven framework for integrated security and privacy in Internet of Things using quantum-resistant blockchain,” Future Internet, vol. 17, no. 6, art. 246, 2025. doi:10.3390/fi17060246
[19]F. Glover, G. Kochenberger, and Y. Du, “Quantum bridge analytics I: A tutorial on formulating and using QUBO models,” 4OR, vol. 17, no. 4, pp. 335–371, 2019. doi:10.1007/s10288-019-00424-y
[20]N. Tashatov et al., “Integrating multi-criteria decision making and reinforcement learning for consensus protocol selection,” Bulletin of Electrical Engineering and Informatics, vol. 14, no. 4, pp. 2613–2624, 2025. doi:10.11591/eei.v14i4.9552
[21]H. Baniata, A. Anaqreh, and A. Kertesz, “Distributed scalability tuning for evolutionary sharding optimization with random-equivalent security in permissionless blockchain,” Internet of Things, vol. 24, art. 100955, 2023. doi: 10.1016/j.iot.2023.100955
[22]M. Cerezo et al., “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, 2021. doi:10.1038/s42254-021-00348-9