Master of Science / Department of Computing and Information Sciences / Venkatesh P. Ranganath / As the Internet of Things (IoT) continues to grow, the need for services that span multiple application domains will continue to increase to realise the numerous possibilities enabled by IoT. Today, however, heterogeneity among devices leads to interoperability issues while building a system of systems and often give rise to closed ecosystems. The issues with interoperability are driven by the inability of devices and apps from different vendors to communicate with each other. The interoperability problem forces the users to stick to one particular vendor, leading to vendor lock-in. To achieve interoperability, the users have to do the heavy lifting (at times impossible) of connecting heterogeneous devices. As we slowly move towards system-of-systems and IoT, there is a real need to support heterogeneity and interoperability. A recent effort in Santos Lab developed Medical Device Coordination Framework (MDCF), which was a step to address these issues in the space of human medical systems. Subsequently, we have been wondering if a similar solution can be employed in the area of animal science. In this effort, by borrowing observations from MDCF and knowledge from on-field experience, we have created a demonstration showcasing how a combination of precise component descriptions (via DSL) and communication patterns can be used in software development and deployment to overcome barriers due to heterogeneity, interoperability and to enable an open ecosystem of apps and devices in the space of animal telemetry.

Our data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the sharks' behavior, and behavioral interaction between them. Our method uses particle filters, a computational Bayesian technique designed to sequentially model a dynamic system. We discuss how resampling is used to approximate arbitrary densities, and illustrate its use in a simple example of a particle filter implementation of a state-space model. We then introduce a particular model formulation that uses conditioning to introduce unobserved parameters for the shark's behaviors. We show how the irregularly-observed shark locations can be modeled by interpolation as a set of movements at constant-length time intervals. We use a spline method for generating approximations of the ground truth at these intervals for comparison with our model. Finally, we demonstrate our model's estimates of the sharks' behavioral and ecological parameters of interest on a subset of the observed data. / Statistics