The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems.

Wireless sensor networks are expanding across nearly all aspects of social and economic activity. Their deployment now spans almost all available environments, including, in some cases, off-planet applications. Applications may range from near-ground, underground, undersea, indoor–outdoor, and on- and in-body, to name a few deployment environments. Each channel model exhibits its own physical representation and statistical distributions. In this work, we categorize and re-evaluate the various propagation models published in the open literature while, at the same time, we contribute critical updates needed due to the rapid expansion of the wireless sensor network discipline. Our work includes various results, based on simulation and/or analytical formulas, that advocate for and evaluate our classification and findings. Unlike prior reviews that treat environments in isolation, this article introduces a unified analytical and simulation framework for a cross-environment comparison, with updated parameterizations aligned with modern WSN deployments. The innovation lies in the consistent cross-environment evaluation of propagation mechanisms, updated parameterizations aligned with modern WSN deployments, and the identification of feasibility and trade-off boundaries across heterogeneous media.

Digital telecommunications have become the backbone of modern healthcare, transforming how patients and professionals interact, share information, and deliver treatment. The integration of telecommunications with medicine, biomedical engineering and health services has enabled rapid growth in telemedicine, remote patient monitoring, wearable biomedical devices, and data-driven clinical decision-making. Emerging technologies such as artificial intelligence, big data analytics, virtual and augmented reality and robotic tele-surgery are further expanding the scope of digital health. This review provides a comprehensive overview of the role of telecommunications in medicine and biomedical engineering. We classify key applications, highlight enabling technologies and critically examine the challenges regarding interoperability, data security, latency, and cost. Finally, we discuss future directions, including 5G/6G networks, edge computing, and privacy-preserving medical AI, emphasizing the need for reliable and equitable access to telecommunications-enabled healthcare worldwide.