Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).

The rapid expansion of electric vehicle (EV) infrastructure necessitates advanced solutions for secure and private authentication at EV charging stations. This research introduces a blockchain-based framework enhanced with self-sovereign identity (SSI) features, targeting the improvement of privacy and security in cyber marketplaces for EVs. The inclusion of SSI enables users to maintain full control over their digital identities, a critical advancement for authenti- cation processes at EV charging stations. This system effectively addresses the growing privacy and security challenges within the expanding EV infrastructure. By integrating Zero-knowledge proof with self-sovereign identity, the framework not only ensures robust security but also preserves user privacy by enabling users to prove their identity without exposing sensitive personal information. We propose an efficient and user-friendly solution, showcasing its potential as a pioneering inno- vation in the field of EV charging infrastructure.

In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency.

Service discovery matchmaking plays a vital role in the cyber marketplace for the Internet of Things (IoT), especially in peer-to-peer environments where buyers and sellers dynamically register and match resource profiles online. As the IoT marketplace expands, efficient resource allocation through matchmaking is increasingly important. However, the growing complexity of service discovery, coupled with data security and privacy challenges, complicates the identification of suitable services. To address these issues, this study conducts a comprehensive review of matchmaking algorithms within the IoT marketplace by examining their key attributes, strengths, and limitations as documented in academic literature. This paper categorizes and summarizes state-of-the-art approaches, identifying research gaps and proposing future directions. Our comparative analysis highlights the strengths and weaknesses of current methodologies, advocating for deep learning and context-aware solutions to improve service efficiency. Additionally, blockchain-based approaches are discussed for their potential to improve security, trust, and privacy-preserving transactions. This research lays a critical foundation for the advancement of secure, efficient IoT-enabled marketplaces.