Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Engineering

First Advisor

Sajal Das


Wireless sensor networks are being deployed in a wide variety of applications such as environment monitoring, smart buildings, security, machine surveillance system, and so on.The deployment of sensor networks for a specific sensing application enhances the ability to control and examine the physical environments while collecting meaningful information from the monitoring area.In densely deployed networks, the sensor nodes located in an adjacent area detect the targeted phenomena in its sensing range and report the gathered (raw or processed) data to designated sinks via single-hop or multi-hop communication paths.Although the correlation of data from proximity sensors cause overheads in terms of energy consumption for data delivery and processing, yet they improve data accuracy.Therefore, the definition of quality of service (QoS) and the metrics to evaluate the performance of a wireless sensor network are different from traditional networks in that the QoS attributes highly depend on the specific sensing tasks and applications.While energy efficiency is an important consideration for designing algorithms and protocols for wireless sensor networks, other QoS parameters such as the coverage rate, the end-to-end delay, fairness, throughput, and error rates for delivery or sensing may be equally important depending on the application objectives.Thus, an important issue in a sensor network is to design task-specific QoS-aware data reporting algorithms and protocols that optimize resource consumption and extend the network lifetime.In this dissertation, we propose an integrated framework for QoS-aware data reporting in wireless sensor networks.More specifically, the proposed framework is designed for single-hop cluster-based wireless sensor networks and includes two strategies: an intra-cluster data reporting control strategy (IntraDRC) and an inter-cluster data reporting control strategy (InterDRC).The IntraDRC strategy is based on the selection of data reporting nodes that applies the block design concept from combinatorial theory and a novel two-phase node scheduling (TNS) scheme that defines class-based data reporting rounds and node assignment for each time slot.The objective of IntraDRC is to provide optimized data reporting control in a distributed manner.In this strategy, a certain number of data reporting nodes are selected in each cluster in order to satisfy the throughput fidelity specified by the applications while reducing redundant data reporting by selecting a subset of cluster members.This intra-cluster reporting control eventually helps control the overall amount of traffic in the network.The TNS scheme schedules data reporting while considering the priority of data, yet guaranteeing that sensor nodes compete with each other in the same class only.The InterDRC strategy, on the other hand, is based on QoS-aware data reporting tree management scheme that balances the trade-off between the end-to-end delay and energy efficiency.The idea of this strategy is to manage variants of the data reporting tree based on two information, such as the hop counts to a data sink and the traffic amount generated from local area.For this purpose, each cluster head analyzes the traffic scenario of its cluster for load balancing and congestion control, thus improving the overall network performance.In InterDRC, the proposed spanning tree construction algorithm first builds the fewest hop-based reporting tree, used for delay constrained data delivery.This tree is updated with traffic load information in order to construct a traffic-adaptive reporting tree, used for energy efficient data delivery.By separating the controls of data reporting within a cluster and that from one cluster to another, the proposed integrated framework can define different levels of various QoS parameters in each intra-cluster data reporting as well as inter-cluster reporting.To the best of our knowledge, we are the first to propose node arrangement using block designs in order to design task-specific data report scheduling in wireless sensor networks.This node arrangement strategy facilities an efficient local data collection in a cluster.Simulation results demonstrate that the proposed framework results in a significant conservation of energy by reducing the competition between data reporting nodes and establishing traffic-adaptive data reporting paths.The results also show that the throughput performance of our integrated framework is especially good due to stable data reporting independent of the network density.


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington