Programmable Software-Defined Testbed for Visible Light UAV Networks: Architecture Design and Implementation

As of Today, There Has Been Increasing Research on Designing Optimization Algorithms and Intelligent Network Control Methods for Visible Light Unmanned Aerial Vehicles (UAV) Networks to Provide Pervasive and Broadband Connections. for Those Theoretical Analysis based Algorithms, there is an Urgent Need to Have a Visible Light UAV Network Platform that Can Help Evaluate the Proposed Algorithms in Real-World Scenarios. However, to the Best of Our Knowledge, there is Currently No Dedicated High Data Rate and Flexible Visible Light UAV Networking Prototype. to Bridge This Gap, in This Paper, We First Design a Novel Programmable Software-Defined Architecture for Visible Light UAV Networking, Including Control Plane, Network Plane, Signal Processing Chain and Front-Ends Plane, and Ground Facility Plane. We Then Implement a Prototype and Conduct Numerous Experiments to Validate the Feasibility of Visible-Light UAV Networks and Further Evaluate the System Performance Pertaining to Achievable Data Rate and Transmission Distance. the Real-Time Video Streaming Experimental Results Show that Up to 550 Kbps Data Rate and a Maximum Distance of 7 Meters Can Be Achieved

Concatenation of Pre-Trained Convolutional Neural Networks for an Enhanced Corona Virus Screening Using Transfer Learning Technique

Coronavirus is the most prevalent coronavirus infection with respiratory symptoms such as fever; cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, Coronavirus has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of Coronavirus is extremely important for the medical community to limit its spread. For large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish Coronavirus precisely in Chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., Coronavirus case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to con-firm the reliability of the proposed method for identifying the patients with Coronavirus disease from X-ray images. The proposed system was trained on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and Coronavirus cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%

A Blockchain Framework for Patient-Centered Health Records and Exchange (HealthChain): Evaluation and Proof-of-Concept Study

Background: Blockchain has the potential to disrupt the current modes of patient data access, accumulation, contribution, exchange, and control. Using interoperability standards, smart contracts, and cryptographic identities, patients can securely exchange data with providers and regulate access. The resulting comprehensive, longitudinal medical records can significantly improve the cost and quality of patient care for individuals and populations alike. Objective: This work presents HealthChain, a novel patient-centered blockchain framework. The intent is to bolster patient engagement, data curation, and regulated dissemination of accumulated information in a secure, interoperable environment. A mixed-block blockchain is proposed to support immutable logging and redactable patient blocks. Patient data are generated and exchanged through Health Level-7 Fast Healthcare Interoperability Resources, allowing seamless transfer with compliant systems. In addition, patients receive cryptographic identities in the form of public and private key pairs. Public keys are stored in the blockchain and are suitable for securing and verifying transactions. Furthermore, the envisaged system uses proxy re-encryption (PRE) to share information through revocable, smart contracts, ensuring the preservation of privacy and confidentiality. Finally, several PRE improvements are offered to enhance performance and security. Methods: The framework was formulated to address key barriers to blockchain adoption in health care, namely, information security, interoperability, data integrity, identity validation, and scalability. It supports 16 configurations through the manipulation of 4 modes. An open-source, proof-of-concept tool was developed to evaluate the performance of the novel patient block components and system configurations. To demonstrate the utility of the proposed framework and evaluate resource consumption, extensive testing was performed on each of the 16 configurations over a variety of scenarios involving a variable number of existing and imported records. Results: The results indicate several clear high-performing, low-bandwidth configurations, although they are not the strongest cryptographically. Of the strongest models, one’s anticipated cumulative record size is shown to influence the selection. Although the most efficient algorithm is ultimately user specific, Advanced Encryption Standard–encrypted data with static keys, incremental server storage, and no additional server-side encryption are the fastest and least bandwidth intensive, whereas proxy re-encrypted data with dynamic keys, incremental server storage, and additional server-side encryption are the best performing of the strongest configurations. Conclusions: Blockchain is a potent and viable technology for patient-centered access to and exchange of health information. By integrating a structured, interoperable design with patient-accumulated and generated data shared through smart contracts into a universally accessible blockchain, HealthChain presents patients and providers with access to consistent and comprehensive medical records. Challenges addressed include data security, interoperability, block storage, and patient-administered data access, with several configurations emerging for further consideration regarding speed and security

A New Edge Computing Architecture for IoT and Multimedia Data Management

The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT)