Adaptive sensor selection algorithms for wireless sensor networks
Silvia Santini · Repository for Publications and Research Data (ETH Zurich) · 2009
A wireless sensor network is a collection of tiny, autonomously powered devices commonly called sensor nodes endowed with sensing, communication, and processing capabilities.Once deployed over a region of interest, sensor nodes can collect ne-grained measurements of physical variables, like the temperature of a glacier or the concentration of a pollutant.To report their readings to one or more data sinks, sensor nodes communicate using their integrated radio-transceivers and build ad-hoc possibly multi-hop relay networks.Thanks to the potentially large number of nodes they are composed of and their ability to operate unattended for long periods of time, wireless sensor networks allow monitoring the environment at an unprecedented spatial and temporal scale.However, enabling a wireless sensor network to reliably report large quantities of data over long periods of time is still a challenging goal.In particular, since the operation of the radio is known to be the major factor of energy consumption on sensor nodes, limiting communication is crucial for increasing the lifetime of the network.On the other hand, meeting the requirements of wireless sensor network applications may require sensor nodes to collect and report large amounts of sensor readings.The ecient operation of a wireless sensor network thus requires careful scheduling of node participation in sensing and communication.Beyond the role that medium access control and routing protocols may play in this context, so-called sensor selection algorithms can provide for signicant communication savings by identifying subsets of the deployed nodes that are sucient to comply with the application requirements.This thesis argues for endowing sensor selection algorithms with the ability to dynamically adapt to the observed data and to the local topology of the network.The presented work oers novel sensor selection strategies that can continuously tune their parameters in a distributed fashion, thereby relying on no or only little a priori knowledge about the phenomena of interest.In particular, the thesis rst addresses the