Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

Sensors

Zhangbing Zhou⁴  Riliang Xing²  Yucong Duan³  Yueqin Zhu¹  Jianming Xiang²  Yunchuan Sun⁵  Antonio Jara⁵ 

[1] Development Research Center of China Geological Survey, and Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China;School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China;;College of Information Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Computer & Communication Engineering, University of Science & Technology Beijing, Beijing 100083, China;;School of Computer & Communication Engineering, University of Science & Technology Beijing, Beijing 100083, China

关键词: event coverage detection;    event sources determination;    routing tree;    weighted graph;    underwater wireless sensor networks;   

DOI  :  10.3390/s151229875

来源: mdpi

【 摘 要 】

With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.