Environment reconstruction through sampling is a difficult task and usually requires a large amount of resources. In this paper, a sampling technique is presented that approaches exhaustive sampling performance with only sparse samples. The goal is achieved by combining information from sensors of different types and resolutions. Image processing techniques are employed to extract global information. This information is passed on to the local sensors to optimize the number and locations of low-level sampling points. The sampled values are then applied back to the image to reconstruct the whole field. The technique is tested in the lab setup and shown to achieve a better result than traditional sampling methods.

Networked infomechanical systems (NIMS) introduces a new actuation capability for embedded networked sensing. By exploiting a constrained actuation method based on rapidly deployable infrastructure, NIMS suspends a network of wireless mobile and fixed sensor nodes in three-dimensional space. This permits run-time adaptation with variable sensing location, perspective, and even sensor type. Discoveries in NIMS environmental investigations have raised requirements for 1) new embedded platforms integrating many diverse sensors with actuators, and 2) advances for in-network sensor data processing. This is addressed with a new and generally applicable processor-preprocessor architecture described in this paper. Also this paper describes the successful integration of R, a powerful statistical computing environment, into the embedded NIMS node platform.

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena coupled with the demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m/sup 2/ over transects exceeding 1000 m/sup 2/. Clearly, adequate sampling coverage of such transect requires an impractically large number of sensing nodes. A new approach, networked infomechanical system (NIMS), has been introduced to combine autonomous-articulated and static sensor nodes enabling sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands. This paper describes our work on the critical parts of NIMS, the task allocation module. We present our methodologies and the two basic greedy task allocation policies - based on time of the task arrival (time policy) and distance from the robot to the task (distance policy). We present results from NIMS deployed in a forest reserve and from a lab testbed. The results show that both policies are adequate for the task of spatiotemporal sampling, but also complement each other. Finally, we suggest the future direction of research that would both help us better quantify the performance of our system and create more complex policies.

The capabilities and distributed nature of networked sensors are uniquely suited to the characterization of distributed phenomena in the natural environment. However, environmental characterization by fixed distributed sensors encounters challenges in complex environments. In this paper we describe Networked Infomechanical Systems (NIMS), a new distributed, robotic sensor methodology developed for applications including characterization of environmental structure and phenomena. NIMS exploits deployed infrastructure that provides the benefits of precise motion, aerial suspension, and low energy sustainable operations in complex environments. NIMS nodes may explore a three-dimensional environment and enable the deployment of sensor nodes at diverse locations and viewing perspectives. NIMS characterization of phenomena in a three dimensional space must now consider the selection of sensor sampling points in both time and space. Thus, we introduce a new approach of mobile node adaptive sampling with the objective of minimizing error between the actual and reconstructed spatiotemporal behavior of environmental variables while minimizing required motion. In this approach, the NIMS node first explores as an agent, gathering a statistical description of phenomena using a nested stratified random sampling approach. By iteratively increasing sampling resolution, guided adaptively by the measurement results themselves, this NIMS sampling enables reconstruction of phenomena with a systematic method for balancing accuracy with sampling resource cost in time and motion. This adaptive sampling method is described analytically and also tested with simulated environmental data. Experimental evaluations of adaptive sampling algorithms have also been completed. Specifically, NIMS experimental systems have been developed for monitoring of spatiotemporal variation of atmospheric climate phenomena. A NIMS system has been deployed at a field biology station to map phenomena in a 50m width and 50m span transect in a forest environment. In addition, deployments have occurred in testbed environments allowing additional detailed characterization of sampling algorithms. Environmental variable mapping of temperature, humidity, and solar illumination have been acquired and used to evaluate the adaptive sampling methods reported here. These new methods have been shown to provide a significant advance for efficient mapping of spatially distributed phenomena by NIMS environmental robotics.