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Shan Lin

Researcher at Stony Brook University

Publications -  112
Citations -  3824

Shan Lin is an academic researcher from Stony Brook University. The author has contributed to research in topics: Wireless sensor network & Network packet. The author has an hindex of 26, co-authored 104 publications receiving 3471 citations. Previous affiliations of Shan Lin include University of Virginia & Nanjing University.

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Proceedings ArticleDOI

ATPC: adaptive transmission power control for wireless sensor networks

TL;DR: ATPC is presented, a lightweight algorithm of Adaptive Transmission Power Control for wireless sensor networks that employs a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time and is robust even with environmental changes over time.
Proceedings ArticleDOI

Realistic and Efficient Multi-Channel Communications in Wireless Sensor Networks

TL;DR: A novel tree-based multichannel scheme for data collection applications, which allocates channels to disjoint trees and exploits parallel transmissions among trees and outperforms other schemes in dense networks with a small number of channels is proposed.

ALARM-NET: Wireless Sensor Networks for Assisted-Living and Residential Monitoring

TL;DR: The correctness, robustness, and extensibility of the system architecture is shown through a scenario-based evaluation of the integrated ALARM-NET system, as well as performance data for individual software components.
Journal ArticleDOI

ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks

TL;DR: ATPC is presented, a lightweight algorithm for Adaptive Transmission Power Control in wireless sensor networks that employs a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time and is robust even with environmental changes over time.
Journal ArticleDOI

Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

TL;DR: A receding horizon control (RHC) framework to dispatch taxis is presented, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System location and occupancy information and is compatible with a wide variety of predictive models and optimization problem formulations.