Impact of neighborhood discovering and adaptive sampling in wireless sensor networks

Abstract

OF THE THESIS Impact of Neighborhood Discovering and Adaptive Sampling in Wireless Sensor Networks by Eun Kyung Lee Thesis Director: Professor Dario Pompili Wireless Sensor Networks (WSNs) are networks characterized by a dense deployment of sensor nodes. Because of the dense deployment, sensors can make interference when exchanging data messages. Besides these data messages, in location-based routing that uses geographical positions to route messages, there is a Neighborhood Discovery Protocol (NDP). It should periodically broadcast ”Hello” packets to discover neighboring nodes and maintain routing tables updated. This is due to the uncertainty of the wireless environment such as varying radio interference and mobility. Due to the overhead caused by these periodic broadcasts from many nodes in certain radio range, however, NDP may heavily impact on the performance of the routing scheme itself, which in turn could affect end-to-end performance. Although this is an important and challenging problem in WSNs, this impact and the associated tradeoffs have not been fully explored in the literature. Hence, in the first half of this thesis, an analytical and experimental study is conducted to determine how parameters such as power and transmission frequency of neighborhood discovery packets affect the communication process in static and mobile environments.