In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

In view of the immense use of LiNbO₃ (LN) in the Photonic industry, a need arises to develop a theory to study the variation of Refractive Indices near the surface of LN device. The “Local” fields as experienced by ions on or near the surface of a dielectric medium ought to be different from those in the interior, thus one expects a Refractive Index variation near the surface of the material. In the studies involving thin films and optical waveguides, this variation could possibly alter the propagation characteristics. The variation of ordinary and extraordinary refractive indices (n_o, n_e) from the surface to the interior of depth 69.315A° near the surface for X-cut and Z-cut LN waveguides are evaluated using theoretical Point Dipole Approximation (PDA) method. For an X-cut waveguide, n_o is found to decrease from 2.9696 on the surface to 2.2864 in the interior whereas, n_e is found to increase from 1.4849 to 2.2024. The birefringence (dn=n_e–n_o) is negative and observed to be increase from -1.4847 to -0.0840. For a Z-cut waveguide, n_o is found to increase from 1.8046 to 2.2864 whereas n_e is found to decrease from 2.7005 to 2.2024. Here the birefringence is positive, decreasing from 0.8958 to zero to a depth of 60.5A° and further decreasing to -0.0840 to a depth of 69.315A°. It is observed that the variation of refractive indices n_o , n_e and dn variation with depth near the surface for both the cases are quite opposite in nature. This is primarily due to the dipole orientation and their relative distributions in the lattice being different for the two directions of the electric vectors of light as is evident from the values.

Introduction: Intelligent, networked nodes make up the Internet of Things (IoT), which is a dynamic, worldwide infrastructure. Through the autonomous interaction of linked smart objects, IoT provides users with seamless service delivery. The LoRaWAN protocol was developed to provide very low-power signal demodulation, facilitating long-distance, low-data-rate communication among the devices of the Low-power Wide Area Network (LPWAN). Objectives: This work focuses on enhancing transmission efficiency in the LoRaWAN-IoT network by dynamically regulating traffic under different conditions while reducing power consumption. Methods: In this paper, we propose a Traffic Aware Data Scheduling Policy (TADSP) for Optimizing Transmission in LoRaWAN-IoT network. Based on various traffic types, traffic priorities are assigned. The gateway nodes apply a data scheduling policy that determines when a certain packet should be forwarded to the network server or discarded. Based on this policy, the gateway node schedules the received data from the end devices and performs time slot scheduling. Results:The proposed methodology is implemented in LoRaWAN simulator. The simulation results show that the proposed TADSP reduces the energy consumption and packet drop rate and increases the uplink delivery rate. Conclusions: The proposed policy provides an optimum transmission strategy for LoRaWAN-IoT networks.