Bio-Inspired Distributed Sensing Using a Self-Organizing Sensor Network

Abstract EN

Nature offers several examples of self-organizing systems that automatically adjust to changing conditions without adversely affecting the system goals.

We propose a self-organizing sensor network that is inspired from real-life systems for sampling a region in an energy-efficient manner.

Mobile nodes in our network execute certain rules by processing local information.

These rules enable the nodes to divide the sampling task in a manner such that the nodes self-organize themselves to reduce the total power consumed and improve the accuracy with which the phenomena are sampled.

The digital hormone-based model that encapsulates these rules, provides a theoretical framework for examining this class of systems.

This model has been simulated and implemented on cricket motes.

Our results indicate that the model is more effective than a conventional model with a fixed rate sampling.