Coordinated static and mobile sensing for environmental monitoring

Authors:
Richard Pon;Maxim A. Batalin;Victor Chen;Aman Kansal;Duo Liu;Mohammad Rahimi;Lisa Shirachi;Arun Somasundra;Yan Yu;Mark Hansen;William J. Kaiser;Mani Srivastava;Gaurav Sukhatme;Deborah Estrin
Affiliations:
Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California, Los Angeles, CA
Venue:
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Year:
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. While substantial progress in sensor network performance has appeared, new challenges have also emerged as these systems have been deployed in the natural environment. First, in order to achieve minimum sensing fidelity performance, the rapid spatiotemporal variation of environmental phenomena requires impractical deployment densities. The presence of obstacles in the environment introduces sensing uncertainty and degrades the performance of sensor fusion systems in particular for the many new applications of image sensing. The physical obstacles encountered by sensing may be circumvented by a new mobile sensing method or Networked Infomechanical Systems (NIMS). NIMS integrates distributed, embedded sensing and computing systems with infrastructure-supported mobility. NIMS now includes coordinated mobility methods that exploits adaptive articulation of sensor perspective and location as well as management of sensor population to provide the greatest certainty in sensor fusion results. The architecture, applications, and implementation of NIMS will be discussed here. In addition, results of environmentally-adaptive sampling, and direct measurement of sensing uncertainty will be described.