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Abstract Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

In the arena of renewable energy integration for electric vehicle (EV) infrastructure, it is a problem to efficiently use solar power with EV charging, considering grid constraints and user preferences. The current study proposes a new smart charging algorithm that utilizes IoT data for the dynamic optimization of EV charging patterns. A novel aspect of the study was a deep learning model, "EVGridNet", that reliably predicts solar energy output and grid prices. EVGridNet uses deep learning approaches to process accumulated data via IoT devices, facilitating fine-tuned adjustments to charging patterns through predictive analytics. This algorithm receives actual data on the generation of solar energy, the price of grid electricity, and other characteristics set by the user to optimize the usage of solar energy, limit the usage of grid electricity during the peak hours and meet all the needs of the user. The optimization process of the algorithm strategically manages energy sources, uses battery storage systems to exploit solar power effectively and uses grid electricity during low-cost periods, all within user preference parameters. The proposed system has the potential of reducing grid electric use by up to 25% for EV charging, and increasing the renewable energy electricity in EV charging to 40% as per simulation results. This will enable quick EV infrastructure scalability, low carbon emission, and energy independence. EVGridNet is an outstanding innovation in smart charging technology, which is a cost-effective and scalable solution to the renewable energy sector's primary challenge. One of the key aspects of the optimization process is the control of energy sources where battery storage systems allow for flexibility in using solar energy and grid electricity within certain pre-set thresholds. [ABSTRACT FROM AUTHOR]

The problem of data security in EHR is deeply concerning, as well as the methods used in session, feature, service, rule, and access restriction models. However, they fail to achieve higher security performance, which degrades the trust of data owners. To handle this issue, an efficient Adaptive Feature Centric Polynomial (AFCP) data security model is described here. The proposed method can be adapted to enforce security on any kind of data. The AFCP scheme classifies the features of EHR data under different categories based on their importance in being identified from the data taxonomy. By maintaining different categories of data encryption schemes and keys, the model selects a specific key for a unique feature with the use of the polynomial function. The method is designed to choose a dynamic polynomial function in the form of m(x) n, where the values of m and n are selected in a dynamic way. The method generates a blockchain according to the feature values and adapts the cipher text generated by applying a polynomial function to data encryption. The same has been reversed to produce the original EHR data by reversing the operation. The method enforces the Healthy Trust Access Restriction scheme in restricting malicious access. By adapting the AFCP model, the security performance is improved by up to 98%, and access restriction performance is improved by up to 97%. The proposed method increases the access restriction performance in the ratio of 19%, 16%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods. Similarly, security performance is increased by 17% 13%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods. [ABSTRACT FROM AUTHOR]