Rural Healthcare IoT Architecture Based on Low-Energy LoRa

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.

A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments

Healthcare needs in rural areas differ significantly from those in urban areas. Addressing the healthcare challenges in rural communities is of paramount importance, as these regions often lack access to adequate healthcare facilities. Moreover, technological advancements, particularly in the realm of the Internet of Things (IoT), have brought about significant changes in the healthcare industry. IoT involves connecting real-world objects to digital devices, opening up various possibilities for improving healthcare delivery. One promising application of IoT is its use in monitoring the spread of diseases in remote villages through interconnected sensors and devices. Surprisingly, there has been a noticeable absence of comprehensive research on this topic. Therefore, the primary objective of this study is to conduct a thorough and systematic review of intelligent IoT-based healthcare systems in rural communities and their governance. The analysis covers research papers published until December 2022 to provide valuable insights for future researchers. The selected articles have been categorized into three main groups: monitoring, intelligent services, and body sensor networks. The findings indicate that IoT research has garnered significant attention within the healthcare community. Furthermore, the results illustrate the potential benefits of IoT for governments, especially in rural areas, in improving public health and strengthening economic ties. It is worth noting that establishing a robust security infrastructure is essential for implementing IoT effectively, given its innovative operational principles. In summary, this review enhances scholars' understanding of the current state of IoT research in rural healthcare settings while highlighting areas that warrant further investigation. Additionally, it keeps healthcare professionals informed about the latest advancements and applications of IoT in rural healthcare.

A Communication Framework for Image Transmission through LPWAN Technology

Analyzing the conditions of use and selecting which technology is more efficient to apply is required when transmitting information through wireless networks.The Internet of Things (IoT) has gained traction in industry and academia as a paradigm in which information and communication technologies merge to deliver unique solutions by detecting, actuating, calculating, and sharing massive volumes of data via embedded systems. In this scenario, Low-Power Wide-Area Networks (LPWAN) appear to be an attractive solution for node connectivity. Typical IoT solutions demand flexible restrictions for wireless communication networks in terms of data rates and latency in exchange for having larger communication ranges and low energy consumption. Nonetheless, as the amount of data and data speeds demanded for particular applications increase, such as image transmissions, IoT network connectivity deteriorates. This paper proposes a communication architecture for image transmission across LPWAN networks utilizing LoRa modulation. The framework combines image processing techniques (classification, compressive sensing (CS), and reconstruction) with an investigation of LoRa modulation parameters using a Software-Defined Radio (SDR) environment. The results show that is possible to communicate an image of 128×128 pixels with four packets and one frequency channel in 2.51 s.

International eHealth ecosystems and the quest for the winning value proposition: findings from a survey study

BACKGROUND

eHealth ecosystems are becoming increasingly important for national and international healthcare. In such ecosystems, different actors are connected and work together to create mutual value. However, it is important to be aware of the goals that each actor pursues within the ecosystem.

METHOD

This study describes the outcomes of a workshop (30 participants) and two surveys (completed by 54 and 100 participants), which investigated how different types of industry stakeholders, namely social services, healthcare, technology developers and researchers, rated potential value propositions for an eHealth ecosystem. Both the feasibility and the importance of each proposition was taken into account.

RESULTS

Interoperability between services was highly valued across industry types but there were also vast differences concerning other propositions.

CONCLUSION

Jointly reflecting on the different perceived values of an ehealth ecosystem can help actors working together to form an ecosystem.

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3-10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.