IoT SENSOR DATA RETRIEVAL AND ANALYSIS THROUGH CLOUD ENVIRONMENT FOR EFFECTIVE POWER MANAGEMENT

Abstract

Due to the massive growth in numbers of Internet of Things (IoT) gadgets, there is an unprecedented rise in sensorgenerated data, which presents new challenges in the effective data retrieval, processing, and use in energyconscious applications. In smart environments like smart grids, smart buildings and industrial automation, where constant monitoring and smart decision-making are required, effective power management has become a critical need. Conventional power management systems tend to be poor in delays in processing data, scalability, and the efficient use of sensor data, which leads to wastage of energy and poor functionality of such systems. This paper introduces a model of retrieving and analyzing data through an IoT sensor using a cloud environment as an effective power management model. The system suggests distributed IoT sensors used to constantly track electrical parameters, including voltage, current, power consumption, and environmental conditions. The sensor data is sent safely into a cloud-based platform where it is stored, accessed and the data processed in real time. Heavy data analytics are used to derive meaningful insights, identify abnormal power consumption patterns, and can be used to support energy optimization plans. The cloud architecture allows scalable storage and high-performance computation, which allows the system to effectively process large quantities of heterogeneous sensor data. The visualization tools and real-time dashboards give users and administrators an actionable insight into the trend in energy consumption and system behavior. As well, the framework promotes automated control systems which regulate the power consumption according to the demand, load conditions and pre-established policies. Through experimental assessment, the proposed solution is proven to be efficient in data retrieval, decreasing the response time, and increasing the overall energy usage compared to the standard power management systems. The findings outline the efficiency of IoT sensing combined with cloud-based analytics in implementing intelligent, large-scale, and scalable power management solutions