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

Abstract

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.