An Investigation on Handling IoT Big Data with RBSEE Architecture

Abstract

Internet of Things (IoT) devices have resulted in an unprecedented surge in data generation, presenting both opportunities and challenges in data management and analytics. This paper investigates the feasibility and effectiveness of utilizing the RBSEE (Resource-Based Storage, Edge Computing, and Elasticity) architecture for handling IoT Big Data. Firstly, the paper elucidates the fundamental concepts of IoT and Big Data, highlighting the unique characteristics and challenges associated with managing large volumes of heterogeneous data streams generated by IoT devices. Subsequently, it introduces the RBSEE architecture, which integrates resource-based storage, edge computing, and elasticity to address the scalability, latency, and resource constraints inherent in IoT environments. The architecture's ability to distribute data storage and processing tasks across edge devices and cloud infrastructure while dynamically adapting to fluctuating workloads makes it particularly suitable for handling IoT Big Data. In any typical IoT ecosystem, real-time and instantaneous handling of request and dataset is pivotal and a fundamental property. This aims to propose energy efficient distributed system architecture for persuasive, privacy-preserved and real time handling of data generated from IoT devices.