ATPC: Adaptive transmission power control for wireless sensor networks

Shan Lin, Fei Miao, Jingbin Zhang, Gang Zhou, Lin Gu, Tian He, John A. Stankovic, Sang Son, George J. Pappas

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

Abstract

Extensive empirical studies presented in this article confirm that the quality of radio communication between low-power sensor devices varies significantly with time and environment. This phenomenon indicates that the previous topology control solutions, which use static transmission power, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This article presents ATPC, a lightweight algorithm for Adaptive Transmission Power Control in wireless sensor networks. In ATPC, each node builds a model for each of its neighbors, describing the correlation between transmission power and link quality. With this model, we employ a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. The intellectual contribution of this work lies in a novel pairwise transmission power control, which is significantly different from existing node-level or network-level power control methods. Also different from most existing simulation work, the ATPC design is guided by extensive field experiments of link quality dynamics at various locations over a long period of time. The results from the real-world experiments demonstrate that (1) with pairwise adjustment, ATPC achieves more energy savings with a finer tuning capability, and (2) with online control, ATPC is robust even with environmental changes over time.

Original languageEnglish (US)
Article number6
JournalACM Transactions on Sensor Networks
Volume12
Issue number1
DOIs
StatePublished - Mar 2016

Bibliographical note

Funding Information:
This work is supported by the National Science Foundation, under NSF grants CNS-1239108, CNS-1218718, CNS-0931239, IIS-1231680, and CNS-1253506 (CAREER). We would like to thank Professor Gang Tao, Professor Lionel M. Ni, and anonymous reviewers for their insightful comments.

Publisher Copyright:
© 2016 ACM.

Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

Keywords

  • Adaptive control
  • Feedback
  • Link quality
  • Sensor network
  • Transmission power control

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