A Survey about Prediction-Based Data Reduction in Wireless Sensor Networks

TL;DR

This survey reviews prediction-based data reduction techniques in Wireless Sensor Networks, highlighting their importance for energy efficiency and spectrum management amid growing IoT demands.

Contribution

It provides a systematic procedure for selecting prediction schemes tailored to WSN constraints and data characteristics, advancing the understanding of their application.

Findings

Categorization of existing prediction-based data reduction mechanisms

A systematic procedure for selecting suitable prediction schemes

Discussion of future challenges and open research directions

Abstract

One of the main characteristics of Wireless Sensor Networks (WSNs) is the constrained energy resources of their wireless sensor nodes. Although this issue has been addressed in several works and got a lot of attention within the years, the most recent advances pointed out that the energy harvesting and wireless charging techniques may offer means to overcome such a limitation. Consequently, an issue that had been put in second place, now emerges: the low availability of spectrum resources. Because of it, the incorporation of the WSNs into the Internet of Things and the exponential growth of the latter may be hindered if no control over the data generation is taken. Alternatively, part of the sensed data can be predicted without triggering transmissions and congesting the wireless medium. In this work, we analyze and categorize existing prediction-based data reduction mechanisms that have been designed for WSNs. Our main contribution is a systematic procedure for selecting a scheme to make predictions in WSNs, based on WSNs' constraints, characteristics of prediction methods and monitored data. Finally, we conclude the paper with a discussion about future challenges and open research directions in the use of prediction methods to support the WSNs' growth.