Adaptive sink mobility management scheme for wireless sensor networks

Kwang I. Hwang, Doo-Seop Eom

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In wireless sensor networks, it is important to efficiently disseminate information from each source to a sink node. In particular, in mobile sink applications, due to the sink mobility, a stationary dissemination path may no longer be effective. The path will have to be continuously reconfigured according to the current location of the sink. In this paper, an Adaptive Reversal Tree (ART) protocol, based on the Adaptive Reversal algorithm and dynamic Root change mechanism, is proposed. Data dissemination from each source to a mobile sink can be easily achieved along the ART without additional control overhead, because the ART proactively performs adaptive sink mobility management. In addition, the ART can maintain a robust tree structure by quickly recovering the partitioned tree with minimum packet transmission. Finally, the simulation results demonstrate that the ART is a considerably energy-efficient and robust protocol.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages478-487
Number of pages10
Volume4159 LNCS
Publication statusPublished - 2006 Oct 23
Event3rd International Conference on Ubiquitous Intelligence and Computing, UIC 2006 - Wuhan, China
Duration: 2006 Sep 32006 Sep 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4159 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Conference on Ubiquitous Intelligence and Computing, UIC 2006
CountryChina
CityWuhan
Period06/9/306/9/6

Fingerprint

Mobile Applications
Mobility Management
Wireless Sensor Networks
Reversal
Wireless sensor networks
Network protocols
Data Dissemination
Path
Tree Structure
Energy Efficient
Roots
Vertex of a graph

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Hwang, K. I., & Eom, D-S. (2006). Adaptive sink mobility management scheme for wireless sensor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4159 LNCS, pp. 478-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4159 LNCS).

Adaptive sink mobility management scheme for wireless sensor networks. / Hwang, Kwang I.; Eom, Doo-Seop.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4159 LNCS 2006. p. 478-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4159 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hwang, KI & Eom, D-S 2006, Adaptive sink mobility management scheme for wireless sensor networks. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4159 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4159 LNCS, pp. 478-487, 3rd International Conference on Ubiquitous Intelligence and Computing, UIC 2006, Wuhan, China, 06/9/3.
Hwang KI, Eom D-S. Adaptive sink mobility management scheme for wireless sensor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4159 LNCS. 2006. p. 478-487. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Hwang, Kwang I. ; Eom, Doo-Seop. / Adaptive sink mobility management scheme for wireless sensor networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4159 LNCS 2006. pp. 478-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{08586beb92a94cf5885657a673c63bf3,
title = "Adaptive sink mobility management scheme for wireless sensor networks",
abstract = "In wireless sensor networks, it is important to efficiently disseminate information from each source to a sink node. In particular, in mobile sink applications, due to the sink mobility, a stationary dissemination path may no longer be effective. The path will have to be continuously reconfigured according to the current location of the sink. In this paper, an Adaptive Reversal Tree (ART) protocol, based on the Adaptive Reversal algorithm and dynamic Root change mechanism, is proposed. Data dissemination from each source to a mobile sink can be easily achieved along the ART without additional control overhead, because the ART proactively performs adaptive sink mobility management. In addition, the ART can maintain a robust tree structure by quickly recovering the partitioned tree with minimum packet transmission. Finally, the simulation results demonstrate that the ART is a considerably energy-efficient and robust protocol.",
author = "Hwang, {Kwang I.} and Doo-Seop Eom",
year = "2006",
month = "10",
day = "23",
language = "English",
isbn = "3540380914",
volume = "4159 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "478--487",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Adaptive sink mobility management scheme for wireless sensor networks

AU - Hwang, Kwang I.

AU - Eom, Doo-Seop

PY - 2006/10/23

Y1 - 2006/10/23

N2 - In wireless sensor networks, it is important to efficiently disseminate information from each source to a sink node. In particular, in mobile sink applications, due to the sink mobility, a stationary dissemination path may no longer be effective. The path will have to be continuously reconfigured according to the current location of the sink. In this paper, an Adaptive Reversal Tree (ART) protocol, based on the Adaptive Reversal algorithm and dynamic Root change mechanism, is proposed. Data dissemination from each source to a mobile sink can be easily achieved along the ART without additional control overhead, because the ART proactively performs adaptive sink mobility management. In addition, the ART can maintain a robust tree structure by quickly recovering the partitioned tree with minimum packet transmission. Finally, the simulation results demonstrate that the ART is a considerably energy-efficient and robust protocol.

AB - In wireless sensor networks, it is important to efficiently disseminate information from each source to a sink node. In particular, in mobile sink applications, due to the sink mobility, a stationary dissemination path may no longer be effective. The path will have to be continuously reconfigured according to the current location of the sink. In this paper, an Adaptive Reversal Tree (ART) protocol, based on the Adaptive Reversal algorithm and dynamic Root change mechanism, is proposed. Data dissemination from each source to a mobile sink can be easily achieved along the ART without additional control overhead, because the ART proactively performs adaptive sink mobility management. In addition, the ART can maintain a robust tree structure by quickly recovering the partitioned tree with minimum packet transmission. Finally, the simulation results demonstrate that the ART is a considerably energy-efficient and robust protocol.

UR - http://www.scopus.com/inward/record.url?scp=33750069420&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33750069420&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540380914

SN - 9783540380917

VL - 4159 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 478

EP - 487

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -