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As landscapes become increasingly fragmented, research into impacts from disturbance and how edges affect vegetation and community structure has become more important. Descriptive studies on how microclimate changes across sharp transition zones have long existed in the literature and recently more attention has been focused on understanding the dynamic patterns of microclimate associated with forest edges. Increasing concern about forest fragmentation has led to new technologies for modeling forest microclimates. However, forest boundaries pose important challenges to not only microclimate modeling but also sampling regimes in order to capture the diurnal and seasonal dynamic aspects of microclimate along forest edges. We measured microclimatic variables across a sharp boundary from a clearing into primary lowland tropical rainforest at La Selva Biological Station in Costa Rica. Dynamic changes in diurnal microclimate were measured along three replicated transects, approximately 30 m in length with data collected every 1 m continuously at 30 min intervals for 24 h with a mobile sensor platform supported by a cable infrastructure. We found that a first-order polynomial fit using piece-wise regression provided the most consistent estimation of the forest edge, relative to the visual edge, although we found no “best” sensing parameter as all measurements varied. Edge location estimates based on daytime net shortwave radiation had less difference from the visual edge than other shortwave measurements, but estimates made throughout the day with downward-facing or net infrared radiation sensors were more consistent and closer to the visual edge than any other measurement. This research contributes to the relatively small number of studies that have directly measured diurnal temporal and spatial patterns of microclimate variation across forest edges and demonstrates the use of a flexible mobile platform that enables repeated, high-resolution measurements of gradients of microclimate.

NIMS3D is a novel rapidly deployable cabled robot capable of accurate positioning and trajectory tracking within its 3-dimensional span. A generic sensor node is maneuvered by controlling the lengths of three support cables. We present initialization and calibration methods, means to determine the effective range of the robot, trajectory tracking algorithms, and experimental results that show 1) wide applicability for various sensor deployments 2) accurate and repeatable positioning, and 3) responsive and stable trajectory tracking.

Increasing demands on water supplies, non-point source pollution, and water quality-based ecological concerns all point to the need for observing stream flow perturbations and pollutant discharges at higher resolution than was practical in the past. This work presents a rapidly deployable Networked Infomechanical System (NIMS RD) technology for observing spatiotemporal hydraulic and chemical properties across stream channels.

This paper reports the first application of actuated sensing systems for high spatiotemporal resolution characterization of the threedimensional environment of river and lake aquatic systems. The development of a new method and its verification in these two application areas is described. Both applications involve dynamic phenomena - one resulting from flow of the water and the other from rapidly evolving biological processes. These applications are typical environmental monitoring problems. They exemplify the key challenge in such problems - characterizing phenomena displaying spatiotemporal heterogeneity. In many such examples, the application requires a diverse array of measurements based on sensors for physical, chemical and biological systems. Together, these requirements pose a significant challenge for conventional sensor network methods. We describe the development and applications of a new general purpose method for actuated sensing - Iterative experiment Design for Environmental Applications (IDEA). IDEA introduces a new in-field adaptation of the sensing systems including static and actuated sensors. IDEA addresses the limitations of previous sampling approaches, for example conventional adaptive sampling, by guiding adaptive sampling with an iteratively developed phenomenon model. This paper presents applications of IDEA to: (1) Three-dimensional characterization of contaminant concentration and flow at the confluence of two major rivers; and (2) Characterization of phytoplankton dynamics in a lake system. These applications provide ideal tests by presenting complex structures associated with each phenomenon and enabling a comprehensive evaluation of the general applicability of IDEA methodology. Improved performance using guided adaptive sampling is demonstrated for two existing methodologies, stratified adaptive sampling and hierarchical non-stationary Gaussian Processes. The IDEA experimental results both validate the general applicability of this method and also have advanced the understanding of interrelated physical, chemical and biological processes in these sampled environments.

Environmental monitoring has proven to be a rich source of biological and ecological data but typically does not conform to traditional monitoring techniques. Events of interest are often dynamic and may not tolerate the introduction of a conventional infrastructure without disrupting the environment being monitored. The new NIMS architectures require minimal infrastructure, are rapidly deployable, and have self anchoring coordinate systems. Simple cableways provide system mobility with minimal occlusion of the monitored transect allowing for accurate positioning and high positional resolution within the space of interest. These new systems have been developed to operate in either a two dimensional plane (NIMS-RD: e.g. monitor river or stream properties) or in a three dimensional volume (NIMS-3D: e.g. monitor incident solar radiation under a forest canopy).

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems.

The resolution at which a sensor network observes the environment is a crucial parameter of performance since it governs the range of applications that are feasible using that network. A higher resolution, in most situations, enables more applications and improves the reliability of existing ones. For science applications for instance, a higher resolution may yield newer insights into the phenomenon than available from lower resolution data. In this poster we discuss a system architecture that uses controlled motion to provide virtual high-resolution in a network of cameras. Several orders of magnitude advantage in resolution is achieved. We discuss several system design choices in the context of our prototype camera network implementation that realizes the proposed architecture. Real world data is collected using our prototype system and used for the evaluation of our proposed methods.

Following in the footsteps of the field Networked Infomechanical Systems (NIMS) currently deployed at the James San Jacinto Mountain Reserve in Idyllwild California, two smaller scale NIMS architectures have emerged. The Laboratory Systems (NIMS-LS) provides an experimental testbed for verification of actuated sensing of environmental phenomena. The NIMS-LS System also includes emulation of environmental phenomena (i.e. light distribution in ecosystems). The Rapidly Deployable (NIMS-RD) platform provides a flexible means of sampling environmental phenomena directly with minimal infrastructure, rapid deployability and minimal impact on the environment. NIMS-RD has been deployed at the James San Jacinto Mountain Reserve, Medea Creek in the Santa Monica Mountains, Merced River in Merced CA and White Mountains Research Station near Bishop CA.

Sensing uncertainty is a key performance metric of interest to any application based on a sensor network. It is thus desirable to sense the phenomenon of interest at the highest possible sensing resolution to achieve the least uncertainty. Given a finite set of sensing resources, it is of interest to determine how to allocate these resources for the best sensing performance. This allocation depends on the distribution of the sensed phenomenon in space and also the characteristics of the medium, such as the presence of obstacles. We propose methods to acquire this environmental information and then move the sensors to improve the sensing performance.

The information acquisition performance of a sensor network is critical to all applications based on it. This performance depends on factors which cannot be completely known at design or deployment time: sensing medium characteristics and the phenomenon distribution. Simplifying assumptions such as the homogeneous nature of sensing media do not hold in most practical scenarios due to the presence of sensing obstacles. Further, the medium and phenomena may change over time. We propose to use controlled mobility to enhance coverage at run time in an autonomous manner. However, extensive robotic capabilities and supporting services such as precise navigation may be infeasible in large scale sensor networks. We present feasible alternatives for physical reconfiguration using low complexity and low energy actuation. The key contribution of the paper is to show that even small degrees of actuation can lead to a significant coverage advantage. We also compare this approach to conventional means for achieving equivalent coverage by increasing node density without actuation. Further, we discuss the relevant trade-offs which affect the use of mobility in terms of the time required for actuation.

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipation sensor devices as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture,

Algal blooms and other negative stream conditions result from dynamic, interrelated factors. Understanding complex biotic and abiotic interactions often require multi-scale, high-resolution measurements. Stream conditions can change rapidly. Conventional low-resolution field sampling may miss important system dynamics. Using the NIMS node technology, we will be able to extract much higher resolution stream data. The nodes will be able to sense and sample a variety of water quality parameters, both abiotic and biotic. The node technology will enable better understanding of the process with which anthropogenic inputs flow through watersheds and change stream ecology. Greater familiarity of these processes will enable better management techniques.

Realistic sensing environments pose a significant challenge to ensuring the quality of sensing due to the unpredictable nature and dynamics of sensing media. This paper presents a practical approach for reducing sensing uncertainty by exploiting mobility while at the same time elimitaing the mobility overheads of complex navigation and energy expense.

This paper introduces NIMS as Networked InfoMechanical Systems and describes new semantic of adaptive sampling for environmental robotics to cope with irregularities of the phenomena.

This Technical Report introduces Networked Infomechanical Systems (NIMS) technology and the general principles of self-aware, physically reconfigurable sensor networks.