Energy Efficient and Accurate Monitoring of Large-Scale Diffusive Objects in Internet of Things

作     者：Oh, Seungmin Lee, Jeongcheol Park, Soochang 

作者机构：Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Hong Kong Univ Sci & Technol HKUST NIE Social Media Lab Hong Kong Hong Kong Peoples R China 

出 版 物：《IEEE COMMUNICATIONS LETTERS》 (IEEE Commun Lett)

年 卷 期：2017年第21卷第3期

页      面：612-615页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

主　　题：Object detection Internet of Things convex hull algorithm 

摘      要：Based on distributed sensing via energy-constrained sensor devices, the key of large-scale and shape-dynamic object monitoring is how to reduce communication costs, which is the highest related factor to energy consumption. For achieving this goal, we propose a novel detection mechanism based on the convex hull algorithm that has a strong aspect for reducing the number of (geographic) sensing points to be transmitted to base stations for figuring out a large-scale object. As the convex hull only could recognize convex shapes, we develop methods to not only detect shape loss being a convex but recovery original boundary. That is, our mechanism could take both low communication cost and high detection reliability of large-scale objects.