Low-Latency Data Aggregation Scheduling for Cognitive Radio Networks With Non-Predetermined Structure

作     者：Chen, Quan Cai, Zhipeng Cheng, Lianglun Gao, Hong 

作者机构：Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON MOBILE COMPUTING》 (IEEE移动计算汇刊)

年 卷 期：2021年第20卷第7期

页      面：2412-2426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] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61802071, 61632010, 61502110, U1801263, U1701262] Guangdong Provincial Key Laboratory of Cyber-Physical System [2016B030301008] Projects of Science and Technology Plan of Guangdong province [2017A010101017, 2018A030310541, 2020A151501107] National Science Foundation (NSF) [CNS-1252292, 1829674, 1741277, 1704287] 

主　　题：Data aggregation Cognitive radio Interference Schedules Processor scheduling Scheduling Distributed algorithms Data aggregation low latency conflict-free scheduling cognitive radio networks (CRNs) 

摘      要：Data aggregation is a fundamental yet popular operation in wireless networks where the sink needs to obtain the combined information of the whole network. However, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in cognitive radio networks. Few studies have addressed this issue and most previous aggregation methods all assume that a fixed-structure based aggregation tree is constructed in advance, which may result in the selection of a node with limited spectrum opportunities as the parent by many nodes and by extension results in a large latency. Thus, the MLAS problem in cognitive radio networks (MLAS-CR) without the above limitation is investigated in this paper. First, the MLAS-CR problem with primary social behaviors where the activity of primary users can be predicted is studied. To make full use of the limited spectrum opportunities, we integrate the construction of the aggregation tree, and the computation of a conflict-free schedule simultaneously, without any predetermined structures. Second, the MLAS-CR problem without the above assumption is also investigated. To reduce the latency, a two-way aggregation scheduling method is proposed to adaptively choose the parent with only current channel information. To further reduce the latency, we also introduce a new data aggregation mode for CRN, i.e., Data Aggregation Scheduling in The Dark, to utilize the spectrum opportunities of scheduled nodes. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.