# PhD Dissertation Defense Presentation

**Tuesday, April 19th, 2016 at 8:00 AM in Rice 242**

## Zhiheng Xie

**Advisor:** John Stankovic

**Committee Members:** John Lach (Committee Chair), Gabriel Robins, Kamin Whitehouse and Alfred Weaver.

**Title: Collaborative Localization Using Wireless Sensor Networks**

**ABSTRACT**:Wireless sensor networks (WSN) are now widely used in many applications. Knowing each sensor node’s position is always a critical issue for these applications.

This dissertation proposes a unified range-based localization mathematical model, which can be applied to a large scale static WSN, to a large scale WSN with dynamic topology changes, or to a mobile WSN. When designing such a model, the following purposes are addressed: 1) unified representation of various measurement types; 2) suitable for both mobile and static networks; 3) quantitative representation for uncertainty; 4) both centralized and decentralized architecture support; 5) efficiency and scalability.

The dissertation first proposes the incremental node-voltage analysis (INOVA) localization algorithm, which is used to localize a stationary WSN with dynamic topology changes in a centralized way. INOVA analogizes a WSN to a generalized electrical network, and borrows the node-voltage analysis from the electrical engineering field to reduce the computational complexity by 70 times (comparing with the optimization technique based solution–Best Linear Unbiased Estimator). By using the same idea, an overlapping subgraph estimator of covariance (OSE-COV) algorithm is proposed. Together with the original overlapping subgraph estimator (OSE) algorithm, it is able to estimate both the positions and the covariance matrices of sensor nodes in a decentralized way. In order to tailor for mobile node localization, the elastic decentralized collaborative localization (EDCL) algorithm is proposed. Different from the above two algorithms, which are theoretically optimal and asymptotically optimal respectively, EDCL is a non-optimal solution. By controlling the number of the historical measurements allowed to be stored in memory, EDCL is able to make the trade-off between the approximation degree and the resource consumption.

For system part, a hybrid pedestrian collaborative localization system is built to demonstrate the effectiveness and the efficiency of the collaborative algorithms. The system is fully decentralized self-contained, which consists of three modules, a RSS to distance estimator, a foot-mounted inertial navigation module, and the EDCL filter. The experimental results show that the localization accuracy of EDCL is close to the optimal solution–INOVA, which reduces the error by as much as 49.44% over the inertial-only solution, and the resource consumption is low (with maximum memory usage 760 bytes and average communication cost 33 bytes per message).