The extensive utilization of mobile consumer electronic technology in several areas highlights the importance of maximizing battery energy efficiency. Users may want to ensure a specific duration of battery life to allow continuous use of their smart mobile devices. This research introduces a novel method to tackle the issues associated with micro power batteries in consumer devices by utilising a sensor-based smart IoT system. The study also examines the use of Split Learning, an innovative algorithmic framework that improves the accuracy and effectiveness of state-of-charge monitoring in micro power batteries. This approach reduces power usage and enhances the precision of estimating battery life. The circuits that monitor battery temperature and current remain in a low-power mode, only activating when the controller needs to make measurements, leading to a substantial reduction in power consumption. Integrating Split Learning algorithms is a major advancement in battery monitoring technology, offering exceptional power efficiency and accuracy for upcoming micro power battery applications in consumer devices. The proposed approach accurately predicts the remaining battery charge under various discharge scenarios. The device also includes an automated learning system using Split Learning to safeguard the battery from excessive current consumption, ensuring the longevity and reliability of these crucial power sources. The results of the current research were 0.9340 for testing data and 0.9376 for training data.

The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively.

Nowadays the usage of Cloud computing services is expanding, because of the high usage of cloud. The Cloud resources face major drawback in energy consumption. The predominant components of energy consumption in cloud resources are by means of server, storage devices, network devices and power required to cool the IT load. The cloud resources contribute high operational energy cost and emit more carbon emission to the environment. Therefore the cloud services providers needs a green cloud environment resolution to decrease the operational energy cost along with environmental impact. The foremost objective of this work is to trim down the energy from utilized and unutilized (idle) cloud resources and save the energy efficiently. To achieve the unceasing green cloud environment, Ant Colony Algorithm is used here to choose the appropriate virtual services so that the power at the server, storage devices, and network resources can be reduced.

Nowadays the Cloud computing services are proliferation. The Cloud computing resources face major pitfall in energy consumes. The prime of energy consumption in cloud computing is by means of client computational devices, server computational devices, network computational devices and power required to cool the IT load. The cloud resources contribute high operational energy cost and emit more carbon emission to the environment. Therefore the cloud services providers need green cloud environment resolution to decrease the operational energy cost along with environmental impact. The major objective of this work is to trim down the energy from utilized and unutilized (idle) cloud resources and save the energy in cloud resources efficiently. To achieve the sustainable green cloud environment from a Hybrid Ant colony Genetic Algorithm used in this paper which chooses the appropriate virtual services so that the power at the client, server and network recourses can be reduced.