Group-Based Neighbor Discovery in Low-Duty-Cycle Mobile Sensor Networks

Liangyin Chen, Yuanchao Shu, Yu Gu, Shuo Guo, Tian He, Fan Zhang, Jiming Chen

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Wireless sensor networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pairwise based, in which discovery is passively achieved when two nodes are prescheduled to wake up at the same time. In contrast, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Since proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pairwise one. Our testbed experiments using 40 sensor nodes and extensive simulations confirm the theoretical analysis, showing one order of magnitude reduction in discovery delay compared with legacy pairwise methods in dense, uniformly distributed sensor networks with at most 8.8 percent increase in energy consumption.

Original languageEnglish (US)
Article number7283619
Pages (from-to)1996-2009
Number of pages14
JournalIEEE Transactions on Mobile Computing
Volume15
Issue number8
DOIs
StatePublished - Aug 1 2016

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.

Keywords

  • Wireless sensor networks
  • group-based mechanism
  • low-duty-cycle
  • proactive discovery

Fingerprint

Dive into the research topics of 'Group-Based Neighbor Discovery in Low-Duty-Cycle Mobile Sensor Networks'. Together they form a unique fingerprint.

Cite this