TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. IdealVolting: reliable undervolting on Wireless Sensor Nodes
 
Options

IdealVolting: reliable undervolting on Wireless Sensor Nodes

Publikationstyp
Journal Article
Date Issued
2016-04-15
Sprache
English
Author(s)
Kulau, Ulf  
Büsching, Felix  
Wolf, Lars  
TORE-URI
http://hdl.handle.net/11420/10893
Journal
ACM transactions on sensor networks  
Volume
12
Issue
2
Start Page
1
End Page
38
Article Number
11
Citation
ACM Transactions on Sensor Networks 12 (2): 11 1-38 (2016-04)
Publisher DOI
10.1145/2885500
Scopus ID
2-s2.0-84966521942
Publisher
ACM
The energy consumption of Wireless Sensor Networks (WSNs) correlates with the voltage level at which the nodes are powered-a lowered voltage leads to a prolonged lifetime of nodes and networks. Operating nodes at voltage levels below the recommendation-so-called undervolting-saves energy but is accompanied by an increased risk of failures. In this article, we show that a WSN can still work reliably, even if the voltage recommendations are violated. We show that there is a correlation between temperature and error-proneness at the same voltage level and that ideal voltage levels depend on environmental conditions. Especially in outdoor deployments, temperatures fluctuate often and heavily. Additionally, we show that these ideal voltage levels are different for each individual node. To deal with these individual variations and varying temperatures, we present a supervised learning strategy that is able to keep the nodes in uncritical states even if environmental conditions are constantly and heavily changing while saving as much energy as possible by constantly adapting the voltage level of these nodes to an individually ideal level. All measurements were performed on sensor node prototypes that are also presented in this article. In detailed evaluations, it is shown (i) that a single node will never run in the same unsafe state twice, (ii) that only five known bias points are needed to predict nearly all temperature dependencies for an individual node, and, thus, (iii) that a system of undervolted nodes can be as reliable as a conventionally powered network while prolonging the lifetime by more than 40%.
Subjects
Energy efficiency
Robustness
Supervised learning
Undervolting
Voltage scaling
Wireless sensor networks
DDC Class
004: Informatik
TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback