Content sharing over smartphone-based delay-tolerant networks

Elmurod Talipov, Yohan Chon, Hojung Cha

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

With the growing number of smartphone users, peer-to-peer ad hoc content sharing is expected to occur more often. Thus, new content sharing mechanisms should be developed as traditional data delivery schemes are not efficient for content sharing due to the sporadic connectivity between smartphones. To accomplish data delivery in such challenging environments, researchers have proposed the use of store-carry-forward protocols, in which a node stores a message and carries it until a forwarding opportunity arises through an encounter with other nodes. Most previous works in this field have focused on the prediction of whether two nodes would encounter each other, without considering the place and time of the encounter. In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the mobility information of individuals. Specifically, our approach employs a mobility learning algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an individual's future mobility information. Evaluation based on real traces indicates that with the proposed approach, 87 percent of contents can be correctly discovered and delivered within 2 hours when the content is available only in 30 percent of nodes in the network. We implement a sample application on commercial smartphones, and we validate its efficiency to analyze the practical feasibility of the content sharing application. Our system approximately results in a 2 percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most.

Original languageEnglish
Article number6133286
Pages (from-to)581-595
Number of pages15
JournalIEEE Transactions on Mobile Computing
Volume12
Issue number3
DOIs
Publication statusPublished - 2013 Jan 29

Fingerprint

Delay tolerant networks
Smartphones
Hidden Markov models
Learning algorithms
Program processors
Network protocols

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Talipov, Elmurod ; Chon, Yohan ; Cha, Hojung. / Content sharing over smartphone-based delay-tolerant networks. In: IEEE Transactions on Mobile Computing. 2013 ; Vol. 12, No. 3. pp. 581-595.
@article{ec77cdd33e964abda54119bdcf5fff94,
title = "Content sharing over smartphone-based delay-tolerant networks",
abstract = "With the growing number of smartphone users, peer-to-peer ad hoc content sharing is expected to occur more often. Thus, new content sharing mechanisms should be developed as traditional data delivery schemes are not efficient for content sharing due to the sporadic connectivity between smartphones. To accomplish data delivery in such challenging environments, researchers have proposed the use of store-carry-forward protocols, in which a node stores a message and carries it until a forwarding opportunity arises through an encounter with other nodes. Most previous works in this field have focused on the prediction of whether two nodes would encounter each other, without considering the place and time of the encounter. In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the mobility information of individuals. Specifically, our approach employs a mobility learning algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an individual's future mobility information. Evaluation based on real traces indicates that with the proposed approach, 87 percent of contents can be correctly discovered and delivered within 2 hours when the content is available only in 30 percent of nodes in the network. We implement a sample application on commercial smartphones, and we validate its efficiency to analyze the practical feasibility of the content sharing application. Our system approximately results in a 2 percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most.",
author = "Elmurod Talipov and Yohan Chon and Hojung Cha",
year = "2013",
month = "1",
day = "29",
doi = "10.1109/TMC.2012.21",
language = "English",
volume = "12",
pages = "581--595",
journal = "IEEE Transactions on Mobile Computing",
issn = "1536-1233",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

Content sharing over smartphone-based delay-tolerant networks. / Talipov, Elmurod; Chon, Yohan; Cha, Hojung.

In: IEEE Transactions on Mobile Computing, Vol. 12, No. 3, 6133286, 29.01.2013, p. 581-595.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Content sharing over smartphone-based delay-tolerant networks

AU - Talipov, Elmurod

AU - Chon, Yohan

AU - Cha, Hojung

PY - 2013/1/29

Y1 - 2013/1/29

N2 - With the growing number of smartphone users, peer-to-peer ad hoc content sharing is expected to occur more often. Thus, new content sharing mechanisms should be developed as traditional data delivery schemes are not efficient for content sharing due to the sporadic connectivity between smartphones. To accomplish data delivery in such challenging environments, researchers have proposed the use of store-carry-forward protocols, in which a node stores a message and carries it until a forwarding opportunity arises through an encounter with other nodes. Most previous works in this field have focused on the prediction of whether two nodes would encounter each other, without considering the place and time of the encounter. In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the mobility information of individuals. Specifically, our approach employs a mobility learning algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an individual's future mobility information. Evaluation based on real traces indicates that with the proposed approach, 87 percent of contents can be correctly discovered and delivered within 2 hours when the content is available only in 30 percent of nodes in the network. We implement a sample application on commercial smartphones, and we validate its efficiency to analyze the practical feasibility of the content sharing application. Our system approximately results in a 2 percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most.

AB - With the growing number of smartphone users, peer-to-peer ad hoc content sharing is expected to occur more often. Thus, new content sharing mechanisms should be developed as traditional data delivery schemes are not efficient for content sharing due to the sporadic connectivity between smartphones. To accomplish data delivery in such challenging environments, researchers have proposed the use of store-carry-forward protocols, in which a node stores a message and carries it until a forwarding opportunity arises through an encounter with other nodes. Most previous works in this field have focused on the prediction of whether two nodes would encounter each other, without considering the place and time of the encounter. In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the mobility information of individuals. Specifically, our approach employs a mobility learning algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an individual's future mobility information. Evaluation based on real traces indicates that with the proposed approach, 87 percent of contents can be correctly discovered and delivered within 2 hours when the content is available only in 30 percent of nodes in the network. We implement a sample application on commercial smartphones, and we validate its efficiency to analyze the practical feasibility of the content sharing application. Our system approximately results in a 2 percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most.

UR - http://www.scopus.com/inward/record.url?scp=84872784006&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872784006&partnerID=8YFLogxK

U2 - 10.1109/TMC.2012.21

DO - 10.1109/TMC.2012.21

M3 - Article

AN - SCOPUS:84872784006

VL - 12

SP - 581

EP - 595

JO - IEEE Transactions on Mobile Computing

JF - IEEE Transactions on Mobile Computing

SN - 1536-1233

IS - 3

M1 - 6133286

ER -