research-article

SensorScope: Application-specific sensor network for environmental monitoring

Authors:

François Ingelrest,

Guillermo Barrenetxea,

Gunnar Schaefer,

Martin Vetterli,

Olivier Couach,

Marc ParlangeAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 6, Issue 2

Article No.: 17, Pages 1 - 32

https://doi.org/10.1145/1689239.1689247

Published: 02 March 2010 Publication History

Abstract

SensorScope is a turnkey solution for environmental monitoring systems, based on a wireless sensor network and resulting from a collaboration between environmental and network researchers. Given the interest in climate change, environmental monitoring is a domain where sensor networks will have great impact by providing high resolution spatio-temporal data for long periods of time. SensorScope is such a system, which has already been successfully deployed multiple times in various environments (e.g., mountainous, urban). Here, we describe the overall hardware and software architectures and especially focus on the sensor network itself. We also describe one of our most prominent deployments, on top of a rock glacier in Switzerland, which resulted in the description of a micro-climate phenomenon leading to cold air release from a rock-covered glacier in a region of high alpine risks. Another focus of this paper is the description of what happened behind the scenes to turn SensorScope from a laboratory experiment into successful outdoor deployments in harsh environments. Illustrated by various examples, we point out many lessons learned while working on the project. We indicate the importance of simple code, well suited to the application, as well as the value of close interaction with end-users in planning and running the network and finally exploiting the data.

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Index Terms

SensorScope: Application-specific sensor network for environmental monitoring
1. Networks
  1. Network architectures

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 6, Issue 2

February 2010

270 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/1689239

Issue’s Table of Contents

Copyright © 2010 ACM.

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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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 02 March 2010

Accepted: 01 August 2009

Revised: 01 May 2009

Received: 01 November 2008

Published in TOSN Volume 6, Issue 2

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Cited By

Becker BOberli CMeuser TSteinmetz R(2024)On Maximizing the Probability of Achieving Deadlines in Communication NetworksJournal of Sensor and Actuator Networks10.3390/jsan1301000913:1(9)Online publication date: 18-Jan-2024
https://doi.org/10.3390/jsan13010009
Karmakar PPradhan SChakraborty S(2024)Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from IndiaACM Journal on Computing and Sustainable Societies10.1145/36856942:3(1-40)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3685694
Wang EZhang MYang BYang YWu J(2024)Large-Scale Spatiotemporal Fracture Data Completion in Sparse CrowdSensingIEEE Transactions on Mobile Computing10.1109/TMC.2023.333908923:7(7585-7601)Online publication date: Jul-2024
https://doi.org/10.1109/TMC.2023.3339089
Ouyang XSrivastava M(2024)LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)10.1109/SenSys-ML62579.2024.00007(9-14)Online publication date: 13-May-2024
https://doi.org/10.1109/SenSys-ML62579.2024.00007
Wang EZhang MYang BXu YSong ZYang Y(2024)Few-Shot Data Completion for New Tasks in Sparse CrowdsensingIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621156(1831-1840)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621156
Kang XJia ZJia JRen J(2024)3DA-NTC: 3D Channel Attention Aided Neural Tensor Completion for Crowdsensing Data Inference2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650963(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650963
Zhang KDai JYu XZhang G(2024)Missing Data Recovery based on Temporal Smoothness and Time-Varying Similarity for Wireless Sensor NetworkInternet of Things10.1016/j.iot.2024.101349(101349)Online publication date: Aug-2024
https://doi.org/10.1016/j.iot.2024.101349
Wang EZhang MLiu WXiong HYang BYang YWu J(2023)Outlier-Concerned Data Completion Exploiting Intra- and Inter-Data Correlations in Sparse CrowdSensingIEEE/ACM Transactions on Networking10.1109/TNET.2022.320154531:2(648-663)Online publication date: Apr-2023
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Eriyadi MSupangkat SHidayat F(2023)Sensing Architecture in Smart City: A Review Paper2023 10th International Conference on ICT for Smart Society (ICISS)10.1109/ICISS59129.2023.10291254(1-7)Online publication date: 6-Sep-2023
https://doi.org/10.1109/ICISS59129.2023.10291254
Saad MAl ARashid AThabet HAl Essa MAlani S(2023)Enhanced Energy Efficiency in Wireless Sensor Networks through Harvesting Technologies2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)10.1109/EICEEAI60672.2023.10590315(1-8)Online publication date: 27-Dec-2023
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