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The need for secure and efficient communication between connected devices continues to grow in healthcare systems within smart cities. Secure communication of healthcare data in Internet of Things (IoT) systems is critical to ensure patient privacy and data integrity. Problems with healthcare communication, like data breaches, integrity issues, scalability issues, and cyber threats, make it harder for people to trust doctors, cause costs to rise, stop people from using new technology, and put private data at risk. So, this paper presents a blockchain-based hybrid method for sending secure healthcare data that combines IoT systems with blockchain technology and high-tech encryption techniques like elliptic curve cryptography (ECC). The proposed method uses the public key of a smart contract to encrypt private data to protect its privacy. It also uses cryptographic hashing and digital signatures to make sure that the data is correct and real. The framework stores metadata (e.g., hashes and signatures) on-chain, and large data uses off-chain storage like IPFS to reduce costs and improve scalability. It also incorporates a mechanism to authenticate IoT devices and enable secure communication across heterogeneous networks. Moreover, this work bridges gaps in existing solutions by providing an end-to-end secure communication system for healthcare applications. It provides strong data security and efficient storage for a reliable and scalable way to handle healthcare data safely in IoT ecosystems. 

The management of radioactive sources is a criti- cal process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone’s safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain’s decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real- time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials. 

The emerging field of smart healthcare has identified emotion detection as a key component in improving patient care, diagnostics, and therapeutic interventions. This paper introduces an innovative approach to emotion detection within the healthcare domain by integrating a Convolutional Neural Network (CNN) with a Maximum A Posterior (MAP) estimator prepared for Magnitude-Squared Spectrum (MSS) analysis. The effectiveness of CNN’s advanced feature extraction capabilities with the statistical strength of MAP estimation offers a promising avenue for interpreting complex physiological signals. The proposed methodology aims to accurately discern and quantify emotional states, thus contributing to the personalization and effectiveness of healthcare services. To validate the efficacy of this approach, the work conducted extensive experiments on a diverse data set composed of physiological signals, demonstrating that the proposed model outperforms existing limitations in emotion recognition tasks. The integration of MSS into CNN frameworks, added with MAP estimation, provides a significant improvement in the detection and analysis of emotions, resulting in more responsive and intelligent healthcare systems. This proposed paper not only presents a novel methodological contribution, but also demonstrates the groundwork for future research toward the intersection of emotional intelligence and healthcare technology. 

The rapid evolution of Software-Defined Networking (SDN) has transformed network management by decoupling the control and data planes. It provides centralized control, enhanced flexibility, and programmability of network management services. However, this centralized control introduces security vulnerabilities and challenges related to data integrity, unauthorized access, and resource management. In addition, it brings forth significant challenges in secure and scalable data storage and computational resource management. These challenges are further increased by the need for real-time processing and the ever-increasing volume of data. To address these challenges, this paper presents a scalable blockchain-based framework for security and computational resource management in SDN architectures. The proposed framework ensures decentralized and tamper-resistant data handling and utilizes smart contracts for automated resource allocation. Due to the need for advanced security and scalability in SDN networks, this work incorporates sharding to improve parallel processing capabilities. The performance of sharded versus non-sharded blockchain systems under various network conditions is evaluated. Our findings demonstrate that the sharded blockchain model enhances scalability and throughput with robust security and fault tolerance. The framework is also assessed for its performance, scalability, and security to enhance SDN resilience against data breaches, malicious activities, and inefficient resource distribution. 

In the rapidly growing consumer electronics industry, continuous innovation drives increasing demand for smart devices and advanced gadgets. However, this sector faces changing demands and complex supply chains due to the management of rapid technological advancements and consumer expectations. Seamless communication between suppliers and consumers is essential to optimize production processes, minimize waste, and enhance overall customer satisfaction. In response to these demands, this paper presents a solution that combines Digital Twins (DT) and blockchain to improve security and efficiency in metaverse-inspired consumer-oriented supply chains. Herein, DT is used to represent products in virtual spaces and blockchain secures sensitive information using encryption and access controls. Our objective is to create a transparent, secure, and user-friendly system where consumers and suppliers can interact in real-time to verify product details and access important information of featured tasks like warranties and payment settlement. Smart contracts automates these tasks to make processes faster and more reliable. Through experiments, we tested how well the system maintains product integrity, authenticates transactions, and supports consumer-oriented supply chain (CSC) operations. Comparative analysis shows that our approach improves security, performance, and scalability over existing methods. Furthermore, the proposed system not only enhances security, trust, and transparency in CSC but also sets a higher standard for consumer demands and satisfaction. The findings point to the potential solution for future innovations in metaverse-driven CSC management systems. 

The safety and security of educational environments are paramount concerns for communities worldwide. Recent incidents of violence in schools underscore the urgent need for innovative and proactive safety measures that extend beyond traditional reactive approaches. In response to this imperative, we propose an Advanced Federated Learning- Empowered Edge-Cloud Framework for School Safety Prediction and Emergency Alert System, which is a groundbreaking solution designed to address the pressing challenges of ensuring school safety. In a world where educational institutions face escalating threats, this framework leverages the innovative approach of federated learning, enabling real-time threat detection and proactive alert generation while preserving data privacy. Challenges such as delayed response times, false alarms, and limited threat assessment protocols are met head-on through the integration of predictive algorithms, sensors, and edge computing. This transformative system not only revolutionizes security but also prioritizes the psychological well-being of students, staff, and visitors, fostering an environment conducive to learning. Its significance lies in its potential to prevent incidents, minimize harm, and bolster community confidence in school safety measures, ultimately contributing to the well- being and growth of future generations. Through this pioneering work, we aim to redefine school safety paradigms, making educational institutions safer and more secure for all. 

The healthcare industry has experienced a re-markable digital transformation through the adoption of IoT technologies, resulting in a significant increase in the volume and variety of medical data generated. Challenges in processing, analyzing, and sharing healthcare data persist. Traditional cloud computing approaches, while useful for processing healthcare data, have drawbacks, including delays in data transfer, data privacy concerns, and the risk of data unavailability. In this paper, we propose a software-defined 5G and AI-enabled distributed edge-cloud collaboration platform to classify healthcare data at the edge devices, facilitate realtime service delivery, and create AI/ML-based models for identifying patients' potential medical conditions. In our architecture, we have incorporated a federated learning scheme based on homomorphic encryption to provide privacy in data sharing and processing. The proposed framework ensures secure and efficient data communication and processing, ultimately fostering effective collaboration among healthcare institutions. The models will be validated by performing a comparative time analysis, and the interplay between edge and cloud computing will be investigated to support realtime healthcare applications. 

In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption.