PhD Degree Awarded to Researcher Ali Al-Awami in Information Technology

Researcher Ali Ahmed Mohammed Al-Awami was awarded a PhD degree in Information Technology for his dissertation titled: A Blockchain-Based Model for Enhancing Verification and Data Transmission Rate in Real-Time Internet of Things Applications., which was submitted to the Department of Information Technology, Faculty of Computer and Information Technology–Sana’a University. The dissertation defense was held on Monday, July 20, 2026.

The PhD Viva-Voce Committee, which was formed based on a resolution issued by the Graduate Studies and Scientific Research Council, consisted of the following:

The dissertation aimed to develop and evaluate adaptive hybrid blockchain consensus mechanisms to improve verification efficiency and transaction throughput in real-time Internet of Things (IoT) applications while optimizing latency, scalability, energy efficiency, validator trust evolution, transaction prioritization, and adaptability to dynamic workloads and evolving security threats.

The research adopted a progressive methodological framework that began with the development of a foundational architecture integrating Proof of Verification (PoV) and Proof of Reputation (PoR) mechanisms. It subsequently introduced a more advanced framework incorporating sharding, M/M/c queueing models, Markov chain–based trust modeling, and adaptive validator activation.

The dissertation produced several significant findings. It demonstrated that integrating reputation-based verification with Markov chain trust modeling enhances validator selection while reducing the participation of untrusted nodes. The use of priority queueing mechanisms effectively protected time-critical transactions, particularly under high workloads. The proposed PoV–DRoP framework, which combines sharding with M/M/c queueing, achieved stable simulated performance at a benchmark throughput of approximately 80,000 transactions per second. The study further identified a configuration of 1,000 nodes as an optimal operating point at this workload, balancing high throughput, low latency, zero transaction expiration, and moderate resource utilization. Dynamic validator activation enabled the system to adapt efficiently to queue congestion, workload fluctuations, and cyberattacks. Security evaluations under multi-vector attack scenarios yielded a high detection performance, achieving an F1 score of approximately 0.9753 in the simulation environment. Moreover, the smart city case study demonstrated the framework’s ability to scale to highly dynamic workloads, including a simulated scenario involving 15 million IoT devices, within the assumptions of the simulation model. The study also concluded that blockchain performance should be evaluated using a comprehensive set of metrics, including latency, queue performance, energy consumption, trust, security, and post-attack recovery, rather than throughput alone.

The dissertation recommended implementing the proposed framework on real blockchain platforms or hardware-assisted testbeds, utilizing real-world IoT traffic datasets, developing more sophisticated security and trust models through machine learning techniques, dynamically optimizing validator selection weights, strengthening coordination and interoperability among blockchain shards, investigating privacy, governance, and practical deployment issues, expanding energy-consumption evaluations using real device and network measurements, and examining the effects of mobility, intermittent connectivity, geographical network latency, and burst traffic under real operating conditions.

The dissertation defense was attended by a number of academics, postgraduate students, researchers, as well as the researcher’s colleagues and family members.