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.

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.

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.

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.

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 crane's life on a daily basis.

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.