Article

Demo: Urgent Task Assignment for Mutual Help in Mobile Social Networks

Authors:

Changyou LiuAuthors Info & Claims

EWSN '19: Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks

Pages 276 - 278

Published: 15 March 2019 Publication History

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Abstract

Although the quality of task completion is related to the level of participants, the cost of cooperation among participants is more important for a collaborative task, especially in an urgent situation. In this demo, we study the urgent task assignment problem for mobile social networks. Based on Formal Context theory, we propose a clique-based team formation scheme with minimum communication cost for urgent tasks. Simulation results verify that the proposed scheme can provide good performance in terms of success ratio, cardinality of chosen teams, running time and coverage.

References

[1]

P. Z. Hao F, Min G. K -clique community detection in social networks based on formal concept analysis. IEEE Systems Journal, PP(99):1–10, 2015.

Google Scholar

[2]

M. Kargar and A. An. Discovering top-k teams of experts with/without a leader in social networks. In Proceedings of the 20th ACM international conference on Information and knowledge management, pages 985–994. ACM, 2011.

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Google Scholar

[3]

T. Lappas, K. Liu, and E. Terzi. Finding a team of experts in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 467–476. ACM, 2009.

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Information & Contributors

Information

Published In

EWSN '19: Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks

February 2019

436 pages

ISBN:9780994988638

General Chairs:
Yunhao Liu
Tsinghua University
,
Guoliang Xing
The Chinese University of Hong Kong
,
Program Chairs:
Yuan He
Tsinghua University
,
Gian Pietro Picco
University of Trento

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Junction Publishing

United States

Publication History

Published: 15 March 2019

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Article

Acceptance Rates

Overall Acceptance Rate 81 of 195 submissions, 42%

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