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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.more » « lessFree, publicly-accessible full text available April 9, 2025
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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.more » « lessFree, publicly-accessible full text available March 21, 2025
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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.more » « less
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Electronic Health Records (EHRs) have become increasingly popular in recent years, providing a convenient way to store, manage and share relevant information among healthcare providers. However, as EHRs contain sensitive personal information, ensuring their security and privacy is most important. This paper reviews the key aspects of EHR security and privacy, including authentication, access control, data encryption, auditing, and risk management. Additionally, the paper dis- cusses the legal and ethical issues surrounding EHRs, such as patient consent, data ownership, and breaches of confidentiality. Effective implementation of security and privacy measures in EHR systems requires a multi-disciplinary approach involving healthcare providers, IT specialists, and regulatory bodies. Ultimately, the goal is to come upon a balance between protecting patient privacy and ensuring timely access to critical medical information for feature healthcare delivery.more » « less
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5G and beyond communication networks require satisfying very low latency standards, high reliability, high- speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecommunication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software- Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability.more » « less
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The Internet of Medical Things (IoMT) is a network of interconnected medical devices, wearables, and sensors integrated into healthcare systems. It enables real-time data collection and transmission using smart medical devices with trackers and sensors. IoMT offers various benefits to healthcare, including remote patient monitoring, improved precision, and personalized medicine, enhanced healthcare efficiency, cost savings, and advancements in telemedicine. However, with the increasing adoption of IoMT, securing sensitive medical data becomes crucial due to potential risks such as data privacy breaches, compromised health information integrity, and cybersecurity threats to patient information. It is necessary to consider existing security mechanisms and protocols and identify vulnerabilities. The main objectives of this paper aim to identify specific threats, analyze the effectiveness of security measures, and provide a solution to protect sensitive medical data. In this paper, we propose an innovative approach to enhance security management for sensitive medical data using blockchain technology and smart contracts within the IoMT ecosystem. The proposed system aims to provide a decentralized and tamper-resistant plat- form that ensures data integrity, confidentiality, and controlled access. By integrating blockchain into the IoMT infrastructure, healthcare organizations can significantly enhance the security and privacy of sensitive medical data.more » « less