Cloud Empowered Self-Managing WSNs

TL;DR

This paper presents a cloud-integrated, self-managing wireless sensor network system that autonomously optimizes data transmission, significantly reducing energy consumption and enabling sensing as a service in IoT environments.

Contribution

It introduces a scalable architecture combining cloud, SDN, and reinforcement learning for autonomous WSN management in IoT applications.

Findings

Reduced transmissions by nearly 85% without data quality loss

Demonstrated autonomous environmental adaptation of sensor nodes

Proposed sensing as a service business model

Abstract

Wireless Sensor Networks (WSNs) are composed of low powered and resource-constrained wireless sensor nodes that are not capable of performing high-complexity algorithms. Integrating these networks into the Internet of Things (IoT) facilitates their real-time optimization based on remote data visualization and analysis. This work describes the design and implementation of a scalable system architecture that integrates WSNs and cloud services to work autonomously in an IoT environment. The implementation relies on Software Defined Networking features to simplify the WSN management and exploits data analytics tools to execute a reinforcement learning algorithm that takes decisions based on the environment's evolution. It can automatically configure wireless sensor nodes to measure and transmit the temperature only at periods when the environment changes more often. Without any human intervention, the system could reduce nearly 85% the number of transmissions, showing the potential of this mechanism to extend WSNs lifetime without compromising the data quality. Besides attending to similar use cases, such a WSN autonomic management could promote a new business model to offer sensing tasks as a service, which is also introduced in this work.