A New Collaborative Knowledge-Based Approach for Wireless Sensor Networks

Abstract

This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output.

Introducing DOWSN: Distributed optimization in wireless sensor networks

“…sleep duration in a frame) that guarantees the highest energy efficiency and the minimum latency of the whole network. In [24], a fuzzy knowledge-based sensor network is proposed in which each node is able to infer information from its neighbors, thus providing a more accurate and reliable output. An extensive survey of applications of machine learning in WSNs is reported in [25].…”

Knowledge-based wireless sensors using sound pressure level for noise pollution monitoring

“…The proposed system is based on the basic structure of Mandani FRBS, introducing some modifications in the inference engine. In a previous work [14], we proposed the modifications to the structure of Mandani FRBS, and utilized technologies requiring the least computational burden in order to minimize computational cost and battery consumption.…”

Autonomic Context-Aware Wireless Sensor Networks

“…Canada-Bago et al [20] propose a fuzzy rule-based system composed of two fuzzy logic engines to process both local and neighboring data. Each WSN node embeds this system seeking to contextualize the presence of olive tree plagues based on processing temperature and humidity sensor data.…”

“…Both works [13,19] are not concerned about neither the WSN battery usage nor the context provider overload. Moreover, [20,21] perform experiments testing the battery consumption to estimate WSN lifetime with their fuzzy system. The authors claim that their proposals allow for saving energy, as well as sending/processing lesser data than [13]; however they do not confirm these hypotheses in the works.…”