Guest Editorial: IoT-based secure health monitoring and tracking through estimated computing

Despite the substantial advancements in health technology, the COVID-19 pandemic has underscored the imperative of enhancing the resilience and efficiency of healthcare systems. Within this context, the Internet of Things (IoT) paradigm emerges as highly pertinent in healthcare services, facilitating enriched doctor-patient interaction while concurrently ameliorating healthcare expenditures. Wearable devices provide patients with personalised access to health-related data, empower physicians with more effective health monitoring capabilities, and enable hospitals to oversee medical equipment, personnel, and infection transmission dynamics. IoT devices, functioning as data aggregators, accumulate extensive datasets, furnishing valuable insights that augment decision-making prowess within healthcare settings. However, the exponential proliferation of IoT devices poses formidable challenges in processing this voluminous and diverse data and extracting actionable insights. Amid the manifold benefits of IoT integration in healthcare services, several hurdles persist, including paramount data security and privacy concerns. Real-time data transmission from IoT devices amplifies these concerns, compounding issues related to data overload and potential inaccuracies. This special issue endeavours to disseminate the latest advancements in IoT within healthcare services. The principal objective is to empower researchers to delve into key concepts conducive to IoT's practical, feasible, and robust integration in healthcare delivery, thereby ensuring expeditious, end-to-end, and dependable service provision to patients.

In this Special Issue, our attention has been directed towards a spectrum of topics of scientific interest, encompassing artificial intelligence and IoT-based healthcare methodologies tailored for pandemic disease management, the synergy between Cloud computing and IoT-based healthcare infrastructures, the intricacies of IoT-based healthcare networks, the application of IoT for personalised health monitoring, the utilisation of IoT for disease diagnosis, and related domains. This special issue aims to showcase the latest research in IoT-based health monitoring systems and estimated computing. The papers presented here will provide valuable insights and contribute to the ongoing efforts to mitigate the impact of pandemics on public health.

The papers selected for this Special Issue collectively demonstrate the progressive advancement of scientific inquiry into solutions for IoT-based Secure Health Monitoring and Tracking through Estimated Computing. The pursuit of synergy among disciplines such as Artificial Intelligence, IoT, and Cloud Computing to develop diagnostic systems for diseases and personalised health monitoring stands poised to emerge as a paramount ambition within the scientific community dedicated to advancing societal well-being and health. Thus, the overall submissions were of high quality, which marks the success of this Special Issue.

The five accepted papers can be clustered into two main categories: healthy and security-oriented. The papers in the first category exhibit novelties and solutions to the problem of the definition of healthy environments. A ‘healthy environment’ encompasses not only healthcare support systems for disease detection in humans but also technological solutions aimed at bridging the productivity-sustainability gap across various sectors, such as agriculture. It refers to an environment where human health and ecological sustainability are prioritised, ensuring the well-being of individuals and the planet. The papers in this category are by Florea et al., Rani et al., and Sarin et al. The second category of papers offers innovative solutions to security and privacy challenges in IoT-based healthcare systems. The papers in this category are from Boi et al. and Baldo et al.

Florea et al. present the design and implementation of a flexible, scalable, and user-friendly IoT system for controlling sprinkler irrigation in an outdoor Thuja conifer nursery. The system uses Mamdani Fuzzy Inference Logic to adjust irrigation based on real-time weather conditions and plant monitoring. It offers manual, automated, and scheduled operation modes and remains robust during power outages or loss of connectivity. The system improves water use efficiency, promotes crop health, and introduces farmers to the benefits of IoT technology.

Rani et al. introduce the SMOTE-RF methodology for Alzheimer's disease prediction. They evaluate well-known machine learning algorithms, including decision tree (DT), extreme gradient boosting (XGB), and random forest (RF). This solution proposes an effective integration of well-known techniques in the machine learning literature, highlighting how the correct and intelligent use of traditional techniques can provide solutions to complex problems without exploiting effective but computationally expensive techniques.

Sarin et al. introduce the SEIR-Driven Semantic Integration Framework (SDSIF) to address the challenges of the COVID-19 pandemic. Leveraging the IoT, SDSIF integrates diverse data sources and features an extensive COVID-19 ontology for enhanced data interoperability and semantic inference. The framework enables real-time data integration, advanced analytics, anomaly detection, and predictive modelling using Recurrent Neural Networks (RNNs). SDSIF demonstrates exceptional performance, highlighting its effectiveness in explaining variations in disease data. Additionally, it achieves high accuracy and precision in predictive modelling, making it a valuable tool for epidemiological surveillance and COVID-19 outbreak management.

Boi et al. discuss the security challenges in transmitting sensitive health data in IoT systems and propose a novel encryption technique within a medical context. It introduces the use of ECG signals as a source of randomness for encryption, leveraging a Physical Unclonable Function (PUF) approach. The proposed model includes pre-processing techniques and a fuzzy extractor to enhance signal stability. Experiments conducted on a 6-month ECG dataset demonstrate promising short-term results and valuable long-term outcomes, paving the way for adaptive PUF techniques in healthcare IoT systems.

Baldo et al. discuss the importance of online presence for the elderly and propose a decentralised architecture to address their engagement needs. It focuses on three main issues: providing a human-readable domain, content moderation, and creating a reward system based on user reputation. Based on Ethereum and Swarm, the proposed architecture utilises smart contracts for automated rule handling and Swarm for distributed storage and web hosting. The result is a fully decentralised, authenticated, and moderated platform where users can share content presentations on the Internet.