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Content Provider | IEEE Xplore Digital Library |
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Author | Wei-Chih Liao Hung-Yun Hsieh |
Copyright Year | 2013 |
Description | Author affiliation: Grad. Inst. of Commun. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Wei-Chih Liao; Hung-Yun Hsieh) |
Abstract | In this paper, we consider the problem of correlated data gathering in M2M (machine-to-machine) wireless networks with a large number of machines. Since machines communicate directly with the aggregator, the limited radio resources at the aggregator become the bottleneck for supporting all machines. Unlike related work that employs distributed source coding for minimizing resource usage, we assume that machines only perform local source coding. However, machines can leverage data overheard from transmissions of other machines for removing redundancy based on “dependent” source coding. To explore the performance tradeoffs of overhearing, we formulate a joint optimization problem involving node selection, resource allocation, and transmission scheduling, and then solve the problem based on the cross entropy method. Evaluation results show that without incurring the complexity of distributed source coding, dependent source coding via overhearing can achieve noticeable performance gain compared to independent source coding - even if the overhearing range and time are limited due to energy consideration. The results thus motivate further investigation for leveraging overhearing opportunities in M2M networks with limited radio resources. |
Sponsorship | IEEE Commun. Soc. |
Starting Page | 2909 |
Ending Page | 2914 |
File Size | 569853 |
Page Count | 6 |
File Format | |
ISBN | 9781479913534 |
DOI | 10.1109/GLOCOM.2013.6831516 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2013-12-09 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Source coding Resource management Correlation Entropy Vectors Joints |
Content Type | Text |
Resource Type | Article |
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