A Centralized Mechanism to Make Predictions Based on Data From Multiple WSNs

TL;DR

This paper proposes a centralized mechanism that leverages data exchange between multiple Wireless Sensor Networks (WSNs) to improve prediction accuracy and optimize energy consumption and measurement quality, demonstrated through a humidity-temperature use-case.

Contribution

It introduces a novel approach for inter-WSN data utilization to enhance prediction and workload adaptation, addressing a gap in multi-WSN collaboration.

Findings

The approach effectively predicts environmental parameters using external WSN data.

Simulation results show improved energy efficiency and measurement quality trade-offs.

The method demonstrates feasibility for cross-domain WSN data integration.

Abstract

In this work, we present a method that exploits a scenario with inter-Wireless Sensor Networks (WSNs) information exchange by making predictions and adapting the workload of a WSN according to their outcomes. We show the feasibility of an approach that intelligently utilizes information produced by other WSNs that may or not belong to the same administrative domain. To illustrate how the predictions using data from external WSNs can be utilized, a specific use-case is considered, where the operation of a WSN measuring relative humidity is optimized using the data obtained from a WSN measuring temperature. Based on a dedicated performance score, the simulation results show that this new approach can find the optimal operating point associated to the trade-off between energy consumption and quality of measurements. Moreover, we outline the additional challenges that need to be overcome, and draw conclusions to guide the future work in this field.