Extracting Intelligence from Large-Scale IoT Data: A Real-Time Analytics Architecture

Abstract

The proliferation of Internet of Things (IoT) technology and the exponential growth of big data necessitate the implementation of intelligent analytical systems within ubiquitous computing environments. Big Data encompasses the vast quantities of information produced by numerous interconnected sensors operating within communication networks. These networked systems demand efficient oversight and coordination across their communication infrastructure. This creates significant analytical challenges regarding the efficient collection, analysis, and supervision of massive datasets from intelligent IoT sensors while maintaining minimal energy consumption. Although smart cities represent a broader IoT ecosystem, this research concentrates on smart office infrastructure to examine sustainable and efficient sensor data management. Our research objective is to demonstrate the capabilities and potential of advanced big data analytics within intelligent office environments to improve operational efficiency. We integrate four compelling technologies—sensors, cloud computing, big data, and IoT—to establish beneficial synergies for enhanced functionality and implementation. Our proposed architecture for sensor service management provides real-time estimates of energy consumption for each node within an intelligent communication network. We evaluate our real-time solution against conventional systems using relevant throughput metrics and energy utilization parameters. The results indicate that our real-time intelligent office solutions can guide the development of efficient smart workplace environments.