A Self-Managed Architecture for Sensor Networks Based on Real Time Data Analysis

TL;DR

This paper introduces a self-managed wireless sensor network platform that analyzes real-time data to optimize network operation, reducing energy consumption and wireless medium usage, and integrating with the Internet of Things.

Contribution

The paper presents a novel self-managed architecture that uses real-time data analysis to autonomously adjust sensor network operations, enhancing efficiency and IoT integration.

Findings

Optimized WSN operation at runtime based on data analysis

Reduced energy consumption and wireless medium occupancy

Facilitated integration of WSNs into IoT environments

Abstract

Wireless sensor networks (WSNs) have been adopted as merely data producers for years. However, the data collected by WSNs can also be used to manage their operation and avoid unnecessary measurements that do not provide any new knowledge about the environment. The benefits are twofold because wireless sensor nodes may save their limited energy resources and also reduce the wireless medium occupancy. We present a self-managed platform that collects and stores data from sensor nodes, analyzes its contents and uses the built knowledge to adjust the operation of the entire network. The system architecture facilitates the incorporation of traditional WSNs into the Internet of Things by abstracting the lower communication layers and allowing decisions based on the data relevance. Finally, we demonstrate the platform optimizing a WSN's operation at runtime, based on different real-time data analysis.