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. Poster abstract: network bootstrapping and leader election utilizing the capture effect in low-power wireless networks
 
Options

Poster abstract: network bootstrapping and leader election utilizing the capture effect in low-power wireless networks

Publikationstyp
Conference Paper
Date Issued
2017-11
Sprache
English
Author(s)
Al Nahas, Beshr  
Duquennoy, Simon  
Landsiedel, Olaf  
TORE-URI
https://hdl.handle.net/11420/53900
Volume
2017
Citation
SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
Contribution to Conference
15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017  
Publisher DOI
10.1145/3131672.3137002
Scopus ID
2-s2.0-85052018322
Publisher
ACM
ISBN
978-1-4503-5459-2
Many protocols in low-power wireless networks require a leader to bootstrap and maintain their operation. For example, Chaos and Glossy networks need an initiator to synchronize and initiate the communication rounds. Commonly, these protocols use a fixed, compile-time defined node as the leader. In this work, we tackle the challenge of dynamically bootstrapping the network and electing a leader in low-power wireless scenarios.
Subjects
Capture effect | IoT | Synchronous transmissions | WSN
DDC Class
600: Technology
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