Existing adaptive sampling methods for mobile sensors are poorly suited to scenarios in which the sensor continuously samples as it moves. We discuss why this is the case and suggest some techniques that may help. Next we propose an entirely different approach that uses a colored planar triangulation model and how it can be analysed by using Bayesian inference and MCMC simulation, which we believe better utilizes the path sampling capabilities of some sensors.

Tamarixia radiata, a species specific ectoparasitoid of the Asian citrus psyllid (ACP), Diaphorina citri, was imported from Pakistan and permitted by the PPQ Permitting Unit for field release in Texas. Over 130,000 parasitoids have been released in the Lower Rio Grande Valley with establishment confirmed at 39 locations. Both open and closed releases are conducted and used to assess establishment and efficacy. Closed releases made in fine-mesh sleeve cages indicate parasitism levels at 10.4%.  When compared to the controls, host mortality is reported at 64.9% in cages with parasitoids present versus 4.4% in cages with parasitoids absent. Further investigations into the host mortality of ACP nymphs have been explored by conducting visual observations on the behavior of female parasitoids (n = 30) for one-hour periods in arenas with suitable hosts. Data indicates that females will mount 3.1 + 0.5 nymphs per hour. The parasitoid will either oviposit the nymph on the ventral side (36.5% of the time) or probe the nymph on the dorsal side (63.5% of the time).  After probing, the parasitoid will either walk away (87.9% of the time) or host feed (12.1% of the time).  Host feeding was documented at 0.43 + 0.1 nymphs per hour. All nymphs that were host-fed were found to be eventually dead.  Host mortality (64.9%) and parasitism rates (10.4%) combined can reduce ACP populations by 75.3%. Studies are still ongoing to help reduce both ACP populations and the incidence of citrus greening disease.

This paper introduces the concept of participatory sensing, which tasks everyday mobile devices, such as cellular phones, to form interactive, participatory sensor networks that enable public and professional users to gather, analyze and share local knowledge. An initial architecture to enhance data credibility, quality, privacy and ‘shareability’ in such networks is described, as well as a campaign application model that encompasses participation at personal, social and urban scales. Example applications are outlined in four areas: urban planning, public health, cultural identity and creative expression, and natural resource management.

In this poster, we consider two major themes in the design of sensor networks: data integrity, and sampling strategies. For the data integrity problem, we propose a signature-based fault detection system for identifying both intermittent faults and persistent faults. Data-dependent features using temporal, spatial, and spatio-temporal information that are useful for detecting faults are identified. These features are combined into signatures that characterize each of the different fault types. We also discuss the problem of simultaneous parameter estimation and fault detection. In this case, parameters must be estimated from a distribution that is truncated in various ways as a result of the fault detection algorithm, which can lead to biased estimates. We propose several methods to account for the bias in parameter estimates. For the sampling problem, we describe two on-going projects. The first one deals with situations where sampling as you move (using sampling paths) is more effective than contemplating sampling points. A PAR sensor riding on a NIMS 3D node is one such situation. This configuration is especially well-suited for sampling phenomena that exhibit latent geometric structure, such as light fields in forest understories. We will consider the case where the phenomena can be approximated by a piecewise-constant field and suggest a novel estimation approach when we have sample paths as observations. The second project considers the problem of finding a sampling strategy to optimize the selection of the correct regression model from a set of competing regression models. The solution is driven by minimizing the probability of error in the selection and consists of a sequential algorithm that directs the collection of measurements. We develop an adaptive sampling algorithm to sample the field with a set of static sensors and one mobile sensor. The algorithm aims to jointly minimize the probability of error in the selection and the mobility cost. The algorithm presented provides a significant improvement in the probability of error in the selection of the correct model over the random collection of measurements.