Acoustic localisation for real-life applications of wireless sensor networks

The work described in this thesis is concerned with self-localisation (automated estimation of sensor locations) and source-localisation (location of a target) using Wireless Sensor Networks (WSNs). The motivation for the research in this thesis is the on-line localisation of marmots from their alarm calls. The application requires accurate 3D self-localisation (within a small percentage of sensor spacing) as well as timely operation. Further challenges are added by the high data-rate involved: sensor nodes acquire data at a rate that is greater than the available network bandwidth. This data cannot be streamed over a multi-hop network, implying a need for data reduction through in-network event detection and local data compression or filtering techniques. The research approach adopted in this thesis combined simulation, emulation and real-life experimentation. Real-life deployment and experimentation highlighted problems that could not be predicted in controlled experiments or simulation. Emulation used data gathered from controlled, real-life experimentation to simulate proposed system refinements; this was sufficient to provide a proof-of-concept validation for some of the concepts developed. Simulation allowed the understanding of underlying theoretical behaviour without involving the complex environmental effects caused by real-life experimentation. This thesis details contributions in two distinct aspects of localisation: acoustic ranging and end-toend deployable acoustic source localisation systems. With regard to acoustic ranging and 3D localisation, two WSN platforms were evaluated: one commercially available, but heavily constrained (Mica2) and one custom-built for accurate localisation (Embedded Networked Sensing Box (ENSBox)). A new proof of concept platform for acoustic sensing (based on the Gumstix single-board computer) was developed by the author (including the implementation of a ranging mechanism), based on experiences with the platforms above. Furthermore, the literature was found to lack a specific procedure for evaluation and comparison of self-localisation algorithms from theoretical conception to real-life testing. Therefore, an evaluation cycle for self-localisation algorithms that encompassed simulation, emulation and real-life deployment was developed. With respect to source localisation, a hardware and software platform named VoxNet was designed and implemented. This was the result of a collaboration between the author and researchers at UCLA and MIT, as part of the first iteration of the National Science Foundation-funded VoxNet project. The author contributed to the software development and in-situ deployment of VoxNet, as well as leading the experimentation and data analysis resulting from the first deployment. Following in-situ deployment experiences, two strategies to improve end-to-end system timeliness were developed by the author: The first, Lazy Grouping, was a centralised algorithm that performed on-line grouping of event data and facilitated its collection from the network. The second strategy led to the development of Adaptation policies, allowing nodes in the network to evaluate whether to process data locally based on previous data transfers. Through simulation based on realistic data traces, Lazy Grouping was observed to reduce end-to-end system latency significantly by reducing the amount of data that nodes send (whilst maintaining all other functional characteristics). The Adaptation policies