A data-quality driven framework for data dissemination in wireless sensor networks

Distinct from wireless ad hoc networks, wireless sensor networks are data-centric, application-oriented, collaborative, and resource-constrained in nature. Especially, the limited resources on energy, bandwidth and computation capability radically change the considerations of system design.; In this thesis we propose a comprehensive data-centric information processing and dissemination framework for sensor networks. We consider two main research issues in this thesis: (T1) light-weight coordination mechanisms among sensors to collect valuable information from the environment and ( T2) efficient dissemination methods to deliver the information of the best quality from sensors to subscribers. For the first issue, we propose a self-organized, dynamic clustering approach for target tracking. Coordination between sensors is triggered by the events of interests and a cluster consisting of a leader and several sensors is formed dynamically. With the use of a probabilistic leader volunteering procedure and a sensor replying method based on the quality of data, the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations. For the second issue, we consider the problems of routing and data transport and formulate as a utility-based optimization problem, with the objective of maximizing the amount of information (utility) collected at sinks (subscribers), subject to flow conservation, channel bandwidth, and energy constraints. Both the centralized and distributed approaches are devised to solve the optimization problem. To validate the design and to empirically study the performance of the proposed works, we implement a subset of acoustic tracking and utility-based data transport components on the Motes testbed, and demonstrate the value of incorporating information quality in data transport.