Wireless Sensor Networks (WSNs) have provided a new model for building intelligent applications with better computer-human/environment interaction. WSNs are built using massive tiny and resources-constrained sensor nodes. With the limited power supply for the sensor nodes, energy consumption is one of the major concerns of the usefulness of the application. It is widely accepted that sleeping is the most effective way to reduce the energy consumption and thus prolong the network lifetime. Many sleeping algorithms have been proposed which aim to identify the redundant sensor nodes and turn them into the sleeping mode....[ Read more ]

Wireless Sensor Networks (WSNs) have provided a new model for building intelligent applications with better computer-human/environment interaction. WSNs are built using massive tiny and resources-constrained sensor nodes. With the limited power supply for the sensor nodes, energy consumption is one of the major concerns of the usefulness of the application. It is widely accepted that sleeping is the most effective way to reduce the energy consumption and thus prolong the network lifetime. Many sleeping algorithms have been proposed which aim to identify the redundant sensor nodes and turn them into the sleeping mode.

In diffusion stimulus (DS) monitoring, the application will result in degraded performance when traditional sleeping algorithms are in use. It is due to the fact that detection delay is as important as energy consumption.

We propose our Stimulus-based Adaptive Sleeping (SAS) which incorporate these application-specific requirements into the sleeping algorithm. This decentralized design provides a balanced performance on both the energy consumption and detection delay with the understanding and utilization of the DS behaviors.