Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 85-88). Also available in print.

Collecting data from sensor nodes to designated sinks is a common and challenging task in a wide variety of wireless sensor network (WSN) applications, ranging from animal monitoring to security surveillance. A number of approaches exploiting sink mobility have been proposed in recent years: some are proactive, in that sensor nodes push their read- ings to storage nodes from where they are collected by roaming mobile sinks, whereas others are reactive, in that mobile sinks pull readings from nearby sensor nodes as they traverse the sensor network. In this thesis, we point out that deciding which data collection approach is more energy-efficient depends on application characteristics, includ- ing the mobility patterns of sinks and the desired latency of collected data. We introduce novel adaptive data collection schemes that are able to automatically adjust to changing sink visiting patterns or data requirements, thereby significantly easing the deployment of a WSN. We illustrate cases where combining proactive and reactive modes of data collection is particularly beneficial. This motivates the design of TwinRoute, a novel hybrid algorithm that can flexibly mix the two col- lection modes at appropriate levels depending on the application sce- nario. Our extensive experimental evaluation, which uses synthetic and real-world sink traces, allows us to identify scenario characteristics that suit proactive, reactive or hybrid data collection schemes. It shows that TwinRoute outperforms the pure approaches in most scenarios, achiev- ing desirable tradeoffs between communication cost and timely delivery of sensor data.