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NETWORKED INFOMECHANICAL SYSTEMS

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NETWORKED INFOMECHANICAL SYSTEMS: A MOBILE EMBEDDED NETWORKED SENSOR PLATFORM Richard Pon1,2, Maxim A. Batalin1,3, Jason Gordon1,2, Aman Kansal1,2, Duo Liu1,2, Mohammad Rahimi1,3, Lisa Shirachi1,2, Yan Yu1,4, Mark Hansen1,5, William J. Kaiser1,2, Mani Srivastava1,2, Gaurav Sukhatme1,3, Deborah Estrin1,4 1Center for Embedded Networked Sensing, University of California, Los Angeles, CA 90095 2Electrical Engineering Department, University of California, Los Angeles, CA 90095 3Computer Science Department, University of Southern California, Los Angeles, CA 4Computer Science Department, University of California, Los Angeles, CA 90095 5Statistics Department, University of California, Los Angeles, CA 90095 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. Keywords- Embedded; Networked; Sensor; Actuation; System; Mobility I. INTRODUCTION Advances in embedded networked sensor systems (ENS) have enabled the first deployments of these devices in many environments [1,2]. Applications for ENS devices now appear in public health, security, and environmental monitoring [3]. However, the first deployment of ENS devices reveal new challenges, to be discussed below, associated with operation of static sensor networks and raise requirements for new capabilities [4]. These include precise sampling of dynamic phenomena, deployment of diverse sensor types in three-dimensional environments, long term and constantly available operation, as well as on-demand high performance computing and communication to remote users. First, the physical configuration including the distribution of sensing elements for a static ENS network is determined at deployment time. While the ENS network may be optimized for sensing fidelity based upon the initial state of the environment, the inevitable and unpredictable time evolution of environmental phenomena may introduce obstacles to sensing, introduce sources of distortion or interference, or cause the spatial distribution of events to depart from the design-time distribution. In particular, the spatiotemporal sampling rate required to reconstruct an environmental model with a required level of fidelity may evolve with time according to rapidly changing of phenomena. Thus, a specified design-time distribution of ENS devices may not provide the required sampling rate. For ENS systems that must detect events, the obstacles present in environments may evolve and obscure events again rendering a design-time solution for deployment to be suboptimal or inapplicable at run-time. Figure 1. NIMS Class I, II, and III architectures are shown in schematic view, deployed in a forest environment for microclimate and water system monitoring. NIMS Class II devices move horizontally and control elevation of attached NIMS Class III nodes. Class I nodes suspended from the infrastructure are shown as well. Many important phenomena require extended periods of observation over multiple seasonal cycles. Not only are conventional battery sources inadequate for long term support of many sensor element types, but also, energy harvesting methods for static nodes may not provide sustainability over the required time frame. Specifically, the energy sources (e.g. solar radiation) available for harvesting may not be spatially distributed according to the ENS node energy consumption requirements. Finally, many important environments for ENS monitoring are characteristically large, three-dimensional spaces where measurements must be performed. This paper describes the Networked Infomechanical Systems (NIMS) ENS platform and its contribution to address the fundamental and important challenges described above [5]. As will be discussed, many deployments in the natural environment of ENS devices now show that a wide range of sensors and now actuator systems are required for characterization of the phenomena important to environmental science. Node and system architecture must then address these requirements. The NIMS architecture described here has been shown to address sensing uncertainty [6], adaptive sampling requirements [7,8], and operation in large three-dimensional environments with energy harvesting capability [5]. A series of NIMS architecture classes have been developed and deployed (as shown in Figures 1 and 2). This material is based upon work supported by the National Science Foundation (NSF) under Grant No. ANI-00331481. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 3760-7803-9201-9/05/$20.00 ©2005 IEEEa) VerticalSensor NodeImageSensorVerticalTransportModuleHorizontalTransportEmbeddedPlatformClass ISensor NodesVerticalSensor NodeImageSensorVerticalTransportModuleHorizontalTransportEmbeddedPlatformClass ISensor Nodes b) Figure 2. a) A NIMS platform designed for characterization of forest environment microclimate. The vertically actuated sensor node and vertically-suspended, static sensor nodes are also shown. All nodes are linked over wireless networks. b) Acquired maps of photosynthetically active radiation (PAR) solar light intensity. These were acquired by actuating the node within the transect plane (coordinates indicated as horizontal and vertical displacements in millimeters) while sampling PAR sensors using the systems to be described below. These maps were acquired at intervals of one hour during normal daylight conditions. These reveal the typical dramatic change in light distribution occurring in the environment due to solar illumination angle change and forest canopy physical structure. NIMS embedded


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