Blockchain-based data storage methods offer strong data integrity, decentralized security, and transparent access control but also face scalability challenges, high computational costs, and complex data management. This study provides a comprehensive review of on-chain, off-chain, and hybrid storage architectures, analyzing their security vulnerabilities, performance trade-offs, and industry-specific applications. On-chain data storage ensures immutability, data integrity, and high security by storing data directly on the blockchain; however, it is associated with high transaction costs and scalability limitations. In contrast, off-chain solutions reduce costs and enhance performance by storing data outside the blockchain but introduce risks related to data integrity and access security in decentralized environments. Hybrid approaches aim to balance security, cost, and scalability by integrating the strengths of both on-chain and off-chain solutions. This study examines the fundamental components of blockchain-based data storage systems, their sector-specific applications, and the technical challenges they present. Additionally, it explores the trade-offs between security, cost, and decentralization, offering insights into blockchain storage optimization strategies. As a result, this study evaluates the optimization of security protocols, the efficiency of hybrid systems, and the sustainability of distributed storage solutions, contributing to future research in this field.

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security.

The goal of this project is to develop a Webcam-based system for monitoring the activities of automobile drivers. As in any system deployed for monitoring driver activities, the primary goal is to distinguish between safe and unsafe driving actions. There is no fixed list of actions that qualifies the unsafe driving behaviors. In general, an activity or an action that reduces a driver's alertness of their surroundings should be classified as unsafe driving behavior. Some examples of unsafe driving behavior include fatigue, talking on a cellular telephone, eating, and adjusting the controls of the dashboard stereo while driving. In this study, we also investigated the relationship between 2D and 3D face and pose recognition.

In the telesurgery operations, images should be high resolution and processed in real time. It seems difficult to accomplish this process in real time using only one computer. In our study, we propose to get 3D scene images inside and outside of surgery environment in real time and transfer them into different places. As known, 3D reconstruction needs intensive calculations. Nowadays, using distributed parallel systems are getting increase. Therefore, we investigate the performance rates using a group of inexpensive inert computers which work as a distributed parallel system supposed to be established in a medical environment.

Artificial intelligence is increasingly deployed where direct observation, reliable ground truth, and repeatable experimentation are structurally unavailable, and pipelines resting on an externally verifiable reference fail by design. This work introduces the Living Data Genome, an architecture treating data as lifecycle-managed evidence. Internal representations remain local; external artifacts cross an externalization boundary only when an admissibility gate and an immune screening condition jointly hold, giving accountability without raw-data exposure. Evidence is anchored by four causal genes and evolves through genesis, mutation, and repair. After release, apoptosis withdraws institutional validity by certified revocation, an append-only lineage remains auditable, and regeneration re-issues a corrected artifact without erasure. A protocol reliability score is defined without labels, combining triadic alignment, transition stability, admissibility survival, and governance coherence under a weakest-link rule; it measures whether evidence passed through the declared lifecycle as required, not whether the underlying claim is factually correct. In synthetic scenarios, under the declared protocol and measured governance coherence, it reached 0.789 on post-incident red-light violation reconstruction and 0.785 on a healthcare vignette, which was a second parameterization of the same generator rather than an independent domain; each of these values was the mean over fifty generator draws. A supervised baseline predicting repair-stage admissibility from the genesis state alone reached a balanced accuracy of 0.595 and 0.553 in the two configurations; hence, the correction is not predetermined. The score moved under the four failure modes it targeted and remained within a declared bound under three it did not. Certified revocation and regeneration recover most of the reliability lost to silent discard.