Collaborative Data Collection with Opportunistic Network Erasure Coding

作     者：Xu, Mingsen Song, Wen-Zhan Zhao, Yichuan 

作者机构：Georgia State Univ Dept Comp Sci Sensorweb Res Lab Atlanta GA 30303 USA Georgia State Univ Dept Math & Stat Atlanta GA 30303 USA 

出 版 物：《IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS》 (IEEE Trans Parallel Distrib Syst)

年 卷 期：2013年第24卷第10期

页      面：1941-1950页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：NSF-CNS-1066391 NSF-CNS-0914371 NSF-CPS-1135814 NSF-CDI-1125165 

主　　题：Disruptive sensor networks opportunistic network coding erasure codes 

摘      要：Disruptive network communication entails transient network connectivity, asymmetric links, and unstable nodes, which pose severe challenges to data collection in sensor networks. Erasure coding can be applied to mitigate the dependency of feedback in such a disruptive network condition, improving data collection. However, the collaborative data collection through an in-network erasure coding approach has been underexplored. In this paper, we present an Opportunistic Network Erasure Coding protocol (ONEC) to collaboratively collect data in dynamic disruptive networks. ONEC derives the probability distribution of coding degree in each node and enables opportunistic in-network recoding, and guarantees that the recovery of original sensor data can be achieved with high probability upon receiving any sufficient amount of encoded packets. First, it develops a recursive decomposition structure to conduct probability distribution deconvolution, supporting heterogeneous data rates. Second, every node conducts selective in-network recoding of its own sensing data and received packets, including those opportunistic overheard packets. Last, ONEC can efficiently recover raw data from received encoded packets, taking advantages of low decoding complexity of erasure codes. We evaluate and show that our ONEC can achieve efficient data collection in various disruptive network settings. Moreover, ONEC outperforms other epidemic network coding approaches in terms of network goodput, communication cost, and energy consumption.