TY - JOUR
AU1 - Lin, Shan
AU2 - Miao, Fei
AU3 - Zhang, Jingbin
AU4 - Zhou, Gang
AU5 - Gu, Lin
AU6 - He, Tian
AU7 - Stankovic, John A.
AU8 - Son, Sang
AU9 - Pappas, George J.
AB -  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks SHAN LIN, Stony Brook University FEI MIAO, University of Pennsylvania JINGBIN ZHANG, University of Virginia GANG ZHOU, College of William and Mary LIN GU, NingBo ShuFang Information Tecknology Co., Ltd. TIAN HE, University of Minnesota JOHN A. STANKOVIC and SANG SON, University of Virginia GEORGE J. PAPPAS, University of Pennsylvania 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 
TI - ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks
JF - ACM Transactions on Sensor Networks (TOSN)
DO - 10.1145/2746342
DA - 2016-03-21
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/atpc-adaptive-transmission-power-control-for-wireless-sensor-networks-S5SOGEMIIp
SP - 1
VL - 12
IS - 1
DP - DeepDyve
ER -