Performance Optimization of WSNs using External Information

TL;DR

This paper presents a self-management system for Wireless Sensor Networks that uses external data to optimize the number of active nodes, balancing measurement accuracy and power consumption.

Contribution

It introduces an architecture that correlates external data with local sensor data to dynamically adjust active nodes in WSNs.

Findings

Improved energy efficiency in WSNs through external data integration

Enhanced measurement relevance by considering spatial and data-type relations

Adaptive node management reduces power use while maintaining accuracy

Abstract

The goal of this work is to describe a self-management system that correlates data sensed by different Wireless Sensor Networks (WSNs) and adjusts the number of active nodes in each network to provide an appropriate amount of measurements. The architecture considers the factors that make the external data relevant to the local network, such as the distance between covered areas, the relation between the types of sensed data and the reliability of the measurements. As a result, the operation of each network will be tuned to trade-off the accuracy of the measurements and the power consumption.