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Call and response: experiments in sampling the environment

Published: 03 November 2004 Publication History

Abstract

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with 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/m2 over transects exceeding 1000 m2. Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling densityo ver large transects to meet a general set of environmental mapping demands.
To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variabilityin environmental phenomena discovered bythe mobile sensors and to discrete events discovered byst atic sensors. We begin byde scribing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

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cover image ACM Conferences
SenSys '04: Proceedings of the 2nd international conference on Embedded networked sensor systems
November 2004
338 pages
ISBN:1581138792
DOI:10.1145/1031495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 03 November 2004

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Author Tags

  1. adaptive sampling
  2. distributed
  3. mobile robotics
  4. sensor network
  5. task allocation

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  • (2019)Unattended Sensors in Marine EnvironmentsOceanography and Coastal Informatics10.4018/978-1-5225-7308-1.ch018(396-419)Online publication date: 2019
  • (2019)Routing Algorithm for Reducing Packet Loss in Mobile WSN2019 International Conference on Computer Network, Electronic and Automation (ICCNEA)10.1109/ICCNEA.2019.00057(258-263)Online publication date: Sep-2019
  • (2017)Path efficient level set estimation for mobile sensorsProceedings of the Symposium on Applied Computing10.1145/3019612.3019707(262-267)Online publication date: 3-Apr-2017
  • (2017)Data-driven selective sampling for marine vehicles using multi-scale paths2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2017.8206511(6111-6117)Online publication date: Sep-2017
  • (2017)Mobile Sensor Networks and RoboticsInternet of Things and Big Data Analytics Toward Next-Generation Intelligence10.1007/978-3-319-60435-0_2(21-46)Online publication date: 15-Aug-2017
  • (2016)Unattended Sensors in Marine EnvironmentsCritical Socio-Technical Issues Surrounding Mobile Computing10.4018/978-1-4666-9438-5.ch014(285-308)Online publication date: 2016
  • (2016)Skeleton-based orienteering for level set estimationProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1256(1256-1264)Online publication date: 29-Aug-2016
  • (2016)Multiple Mobile Robot SystemsSpringer Handbook of Robotics10.1007/978-3-319-32552-1_53(1335-1384)Online publication date: 27-Jul-2016
  • (2015)Relative continuous-time SLAMInternational Journal of Robotics Research10.1177/027836491558964234:12(1453-1479)Online publication date: 1-Oct-2015
  • (2015)Data-driven robotic sampling for marine ecosystem monitoringInternational Journal of Robotics Research10.1177/027836491558772334:12(1435-1452)Online publication date: 1-Oct-2015
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