David J. Anthony

Sensing through the continent: towards monitoring migratory birds using cellular sensor networks

This paper presents CraneTracker, a novel sensor platform for monitoring migratory birds. The platform is designed to monitor Whooping Cranes, an endangered species that conducts an annual migration of 4,000 km between southern Texas and north-central Canada. CraneTracker includes a rich set of sensors, a multi-modal radio, and power control circuitry for sustainable, continental-scale information delivery during migration. The need for large-scale connectivity motivates the use of cellular technology in low-cost sensor platforms augmented by a low-power transceiver for ad-hoc connectivity. This platform leads to a new class of cellular sensor networks (CSNs) for time-critical and mobile sensing applications. The CraneTracker is evaluated via field tests on Wild Turkeys, Siberian Cranes, and an on-going alpha deployment with wild Sandhill Cranes. Experimental evaluations demonstrate the potential of energy-harvesting CSNs for wildlife monitoring in large geographical areas, and reveal important insights into the movements and behaviors of migratory animals. In addition to benefiting ecological research, the developed platform is expected to extend the application domain of sensor networks and enable future research applications.

On crop height estimation with UAVs

Remote sensing by Unmanned Aerial Vehicles (UAVs) is changing the way agriculture operates by increasing the spatial-temporal resolution of data collection. Micro-UAVs have the potential to further improve and enrich the data collected by operating close to the crops, enabling the collection of higher spatio-temporal resolution data. In this paper, we present a UAV-mounted measurement system that utilizes a laser scanner to compute crop heights, a critical indicator of crop health. The system filters, transforms, and analyzes the cluttered range data in real-time to determine the distance to the ground and to the top of the crops. We assess the system in an indoor testbed and in a corn field. Our findings indicate that despite the dense canopy and highly variable sensor readings, we can precisely fly over crops and measure its height to within 5cm of measurements gathered using current measurement technology.

Environmental Reviews and Case Studies: Bringing Unmanned Aerial Systems Closer to the Environment

Increasingly, Unmanned Aerial Systems (UASs) are changing the way that scientists and practitioners collect environmental data. Current UASs, however, are largely relegated to collecting data while flying remotely, far away in the air. This article examines two case studies where micro-UASs fly in immediate proximity to the environment, enabling them to collect physical samples and capture sensor data that cannot be obtained at a distance. The first case study presents an aerial water sampler that flies to remote locations and dips a pump into the water to collect samples for lab analysis. The second case study examines a UAS that flies within a meter of crops to accurately measure their height. Each requires different sensors and methods specifically tailored to operating and interacting near the environment. This article evaluates the performance of these systems and also presents preliminary validation that they collect datasets that are compatible with those gathered by existing approaches. Futhermore, it distills some common underlying design and operating principles shared by UASs aimed at working close to the environment. Finally, this article concludes that in spite of numerous pending challenges, UASs that directly interact with the environment will transform the way environmental data is collected.

Surface classification for sensor deployment from UAV landings

Using Unmanned Aerial Vehicles (UAVs) to deploy sensor networks promises an autonomous and useful method of installation in remote or hard to access locations. Some sensors, such as soil moisture sensors, must be physically installed in soft soil, yet UAVs cannot easily determine soil softness with remote sensors. In this paper, we use data from an onboard accelerometer measured during UAV landings to determine the softness of the ground. We collect and analyze over 200 data sets gathered from 8 different materials: foam, carpet, wood, tile, grass, dirt, concrete, and woodchips. Based on this analysis, we examine a number of features from the accelerometer and four classification algorithms: LDA, QDA, SVM, and binary decision trees. The decision tree performs well and is simple to implement onboard the UAV. We implement this in our UAV control system and perform experiments to verify that the UAV can accurately classify the softness of the surface with 90% accuracy. This lays the groundwork for our future work on developing a UAV capable of installing sensors in soft soil.

Simulating and testing mobile wireless sensor networks

Developing applications for wireless sensor networks (WSNs) can provide many challenges. Environmental conditions have a large impact on the behavior of an application, but it may not be feasible to replicate the conditions of the deployment environment while creating the application. Furthermore, long-term deployment of monitoring applications require extensive pre-deployment analysis of such applications since the sensors cannot be accessed after their deployment. Through a combination of simulation and software engineering practices, it is possible to rigorously test and validate the software for WSNs. In this paper, several methods for simulating distributed mobile WSNs and testing the software are provided. These methods are used in the development of a WSN that was deployed to track Whooping Cranes during their year long migration.

Controlled sensor network installation with unmanned aerial vehicles

Robots improve wireless sensor network (WSN) deployments by reducing deployment times, deploying nodes to improve coverage, and ferrying data. Utilizing Unmanned Aerial Vehicles (UAVs) to install sensor networks in environmentally sensitive areas is especially valuable, as the UAVs are able to quickly traverse rough and environmentally sensitive terrain. UAV based deployments are challenging, as the UAVs may need to install nodes in a specific orientation or location type, which is difficult to sense from a UAV. We present our work towards resolving these difficulties by first classifying the surface a UAV has landed on, and then conducting a post-deployment analysis of the installation.

Crane charades: behavior identification via backpack mounted sensor platforms

The Whooping Crane is an endangered species native to North America and there are approximately 575 in existence. There have been recent efforts to provide ecologists with a tool to study the multifaceted behavior of the endangered species. Like many species, cranes display distinctly identifiable movements while being threatened, acting territorial, migrating, or preening. The preliminary experiments described in this poster provide evidence that sensor data presented by a novel sensing platform, the CraneTracker, can be used to identify crane behaviors on-board. With the ability to identify these behaviors, ecologists will have a more granular insight on what occurs during a cranes life on a daily basis.

An Empirical Study of the Code Pitching Mechanism in the .NET Framework.

The .NET Compact Framework is designed to be a high-performance virtual machine for mobile and embedded devices that operate on Windows CE (version 4.1 and later). It achieves fast execution time by compiling methods dynamically instead of using interpretation. Once compiled, these methods are stored in a portion of the heap called the code cache and can be quickly reused to satisfy future method calls. While the code cache provides a high-level of reusability, it can also use a large amount of memory. As a result, the Compact Framework provides a “code pitching” mechanism that can be used to discard the previously compiled methods as needed. In this paper, we study the effect of code pitching on the overall performance and memory utilization of .NET applications. We conduct our experiments using Microsoft’s Shared-Source Common Language Infrastructure (SSCLI). We profile the access behavior of the compiled methods. We also experiment with various code cache configurations to perform pitching. We find that programs can operate efficiently with a small code cache without incurring substantial recompilation and execution overheads.

Poster abstract: Crane charades: Behavior identification via backpack mounted sensor platforms

The Whooping Crane is an endangered species native to North America and there are approximately 575 in existence. There have been recent efforts to provide ecologists with a tool to study the multifaceted behavior of the endangered species. Like many species, cranes display distinctly identifiable movements while being threatened, acting terri-torial, migrating, or preening. The preliminary experiments described in this poster provide evidence that sensor data presented by a novel sensing platform, the CraneTracker, can be used to identify crane behaviors on-board. With the ability to identify these behaviors, ecologists will have a more granular insight on what occurs during a cranes life on a daily basis.