Scalable Architectures for Real-Time Data Processing in IoT-Enabled Wireless Sensor Networks

Abstract

The combination of Internet of Things (IoT) technologies with wireless sensor networks (WSNs) has spurred the development of scalable architectures designed for processing real-time data. This article explores existing strategies and technologies aimed at addressing the challenges of real-time data processing in IoT-enabled WSNs. Key focuses include optimizing data flow, reducing delay, and ensuring scalability to manage the growing data volumes generated by IoT devices. Various architectural models such as edge computing, fog computing, and cloud-based solutions are analyzed for their ability to efficiently distribute computational tasks across different levels of the network. Performance metrics, including energy efficiency and reliability, are assessed to gauge how well these architectures perform in practical applications. The article concludes with suggestions for future research directions aimed at improving the scalability, efficiency, and flexibility of real-time data processing architectures in IoT-enabled WSNs, supporting diverse applications across various industries.