Fuzzy Validation and Fusion for Wireless Sensor Networks

Abstract

Miniaturized, distributed, networked sensors — called motes — promise to be smaller, less expensive and more versatile than other sensing alternatives. While these motes may have less individual reliability, high accuracy for the overall system is still desirable. Sensor validation and fusion algorithms provide a mechanism to extract pertinent information from massively sensed data and identify incipient sensor failures. Fuzzy approaches have proven to be effective and robust in challenging sensor validation and fusion applications. The algorithm developed in this paper — called mote-FVF (fuzzy validation and fusion) — uses a fuzzy approach to define the correlation among sensor readings, assign a confidence value to each of them, and perform a fused weighted average. A sensor network implementing mote-FVF for monitoring the illuminance in a dimmable fluorescent lighting environment empirically demonstrates the timely response of the algorithm to sudden changes in normal operating conditions while correctly isolating faulty sensor readings.

Control of wireless-networked lighting in open-plan offices

“…Leveraging their miniature size, multiple sensors can be embedded into the workstations and integrated into sensor networks where sensor validation and fusion techniques can be used to extract pertinent sensor information for control decisions. 22,23 Occupancy sensing, which was not explicitly addressed in this research, is also essential for the system to effectively optimise the lighting condition based on the presence of occupants. As the immediate next step of this research, occupancy sensing will be implemented either bundled with the workstationbased photosensors or as stand-alone sensors leveraging the same WSN technology.…”

RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning

“…However, as the ground truth of the measured signals is generally unknown, sensor data cleaning methods have to be applied beforehand. Among the common approaches for sensor data cleaning are state estimation methods [2,6,7,25], parameter estimation methods such as Bayesian estimation algorithms [32,33] and truth estimation algorithms [26], and multi-sensor fusion techniques [5,29]. To formulate the whole workflow, the authors of [27] proposed a sensor accuracy estimation framework which consists of four layers: pre-processing, state estimation, accuracy estimation and accuracy indexing.…”

FTDA: outlier detection‐based fault‐tolerant data aggregation for wireless sensor networks

“…A declarative data cleaning mechanism over sensor node data streams is introduced in . A fuzzy logic‐based approach is proposed in to infer the correlation among measurements from different sensors. The proposed technique assigns a confidence value to each measurement and then performs an aggregated weighted average scheme.…”