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ABSTRACT: 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</sup> over transects exceeding 1000 m<sup>2</sup>. 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.
Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on; 09/2005
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ABSTRACT: Embedded networked sensing systems have been successfully applied to environmental monitoring in a wide range of applications. These first results have demonstrated a potential for advancing fundamental environmental science methods and environmental management capability as well as for providing future methods for safeguarding public health. While substantial progress in sensor network performance has appeared, new challenges have also emerged. Specifically, the inevitable and unpredictable time evolution of environmental phenomena introduces sensing uncertainty and degrades the performance of event detection, environment characterization, and sensor fusion. Many of the physical obstacles encountered by static sensors may be circumvented by a new method, networked infomechanical systems (NIMS). NIMS integrates distributed, embedded sensing and computing systems with infrastructure-supported mobility to enable direct uncertainty characterization, autonomous adjustment of spatiotemporal sampling rate, and active sensor fusion. NIMS actuation is also being applied to advancing sensor network performance through methods based on control of distributed, directional antenna systems. In addition to advances in fundamental research objectives, this presentation will describe the architecture, implementation, and application of NIMS now deployed and continuously operating in the field.
Microwave Symposium Digest, 2005 IEEE MTT-S International; 07/2005
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R. Pon,
M.A. Batalin,
J. Gordon,
A. Kansal,
D. Liu,
M. Rahimi,
L. Shirachi,
Y. Yu,
M. Hansen,
W.J. Kaiser,
M. Srivastava,
G. Sukhatme,
D. Estrin
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ABSTRACT: 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.
Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Symposium on; 05/2005
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ABSTRACT: Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. Distributed architectures have been developed for cooperative detection, scalable data transport, and other capabilities and services. However, the complexity of environmental phenomena has introduced a new set of challenges related to sensing uncertainty associated with the unpredictable presence of obstacles to sensing that appear in the environment. These obstacles may dramatically reduce the effectiveness of distributed monitoring. Thus, a new distributed, embedded, computing attribute, self-awareness, must be developed and provided to distributed sensor systems. Self-awareness must provide the ability for a deployed system to autonomously detect and reduce its own sensing uncertainty. The physical constraints encountered by sensing require physical reconfiguration for detection and reduction of sensing uncertainty. Networked Infomechanical Systems (NIMS) consisting of distributed, embedded computing systems provide autonomous physical configuration through controlled mobility. The requirements that lead to NIMS, the implementation of NIMS technology, and its first applications are discussed here.
Distributed Computing Systems, 2004. FTDCS 2004. Proceedings. 10th IEEE International Workshop on Future Trends of; 06/2004
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ABSTRACT: 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 evaluat-
e 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.
Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on;
