This research presents a modern framework that combine quantum computing and blockchain technologies to transform city management in provision for the 6G era. Quantum computing is recognized for its extraordinary processing power, while blockchain is known for its security, privacy, and transparency. these modern technologies can leverage from the 6G technologies capabilities like high speed, low latency, high bandwidth with giant connectivity. The idea of the study comes due to the few comprehensive standards and frameworks that facilitate the effective employment of these advanced technologies in the context of 6G. The proposed Quantum-Blockchain-6G (QBG) Framework main objective to leverage these emerging technologies with 6G capabilities to bridge the gap by providing the proper guidelines and framework to manage city management infrastructure with the help of efficient delivery of the required services. we used mixed method as interviews with expert, systematic literature review and case studies for this study to explore the challenges for integration of blockchain and quantum computing in 6g technology context and developed strategic framework for these challenges and evaluate its impact in the management activities in city management. This research will highlight critical challenges, relate to the quantum computing such as the complexity and cost and blockchain scalability. Furthermore, the study will investigate the integration the threads of advanced cryptographic measurement to mitigate quantum threats. This framework is precisely designed to empower cities management by utilizing the power of these emerging technologies that use 6G to meet the future connectivity demands in city management to make it smarter, faster, secure and trustworthy for the society.

Over recent years, the Internet of Things (IoT) has been widely adopted in various domains, including modern manufacturing. However, IoT security remains a critical challenge due to resource constraints and deployment in unsecured environments. This paper introduces a lightweight IoT authentication protocol leveraging physical unclonable functions (PUFs) tailored for smart manufacturing. The protocol employs lightweight cryptographic techniques and a PUF-based challenge–response mechanism to ensure key security properties such as confidentiality, integrity, anonymity, and perfect forward secrecy. Through a combination of formal analysis, the proposed protocol demonstrates resilience against prevalent attacks while significantly reducing computational and communication overheads. Comparative performance analysis highlights its efficiency and suitability for resource-constrained IoT environments.

Mobile app development has been considered as one of the fastest growing segments of the software industry both worldwide and in Saudi Arabia. Due to their pervasiveness, mobile applications call for consideration of complex and rapidly changing requirements given the diversity of their environments. Therefore, agile is considered the most suitable methodology for developing mobile apps. However, little research has investigated agile adoption in mobile app development in the real context. Therefore, the purpose of this PhD is to investigate the factors that have a significant impact on agile adoption in mobile app development by small and medium-size software organisations in Saudi Arabia. The expected key contribution of this research will be a deep insight into agile adoption in mobile app development, and the design and development of tools and techniques that may support agile adoption within Saudi context.

Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment.

Software Development (Dev) and Information Technology operations (Ops) in Saudi Arabia rely on collaborative microservices and organization readiness toward application (app) design and deployment. These factors are coupled with agile process development and efficiency improvements with digital support from the IT industry. Considering these requirements, the need for reducing adversarial factors is mandatory for which the agile and adaptable framework is proposed. This framework relies on process flow and service demands to place the applications intermediate to the users/ clients and industries. Through this framework, the actual process demand, process design, and development environment are selected and defined as tuneable metrics. These tuneable metrics are defined to target process completion and adaptability from demand and enterprise perspectives turning out to be efficient. Besides, a validating factor for the above is analyzed using a lateral explainable AI model. This model persuades independent design, deployment, and adaptability validation to meet the DevOps design purpose. For the modified module type, this framework turned out to be 12.73% adaptable with 12.07% less wait interval to achieve 12.59% high process completion.