期刊
IEEE TRANSACTIONS ON COMMUNICATIONS
卷 69, 期 2, 页码 1109-1120出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2020.3036058
关键词
Topology; Network topology; Sensors; Wireless networks; Wireless sensor networks; Radio frequency; Blind source separation; Topology inference; blind source separation; independent component analysis; link detection; Granger causality; transfer entropy; neural network
资金
- MIUR
- CoACh project - POR FESR 2014-2020 program
This study proposes a framework for discovering the topology of wireless networks using RF sensors, with blind methods to sense unknown network features. By utilizing blind source separation and measurement association to handle mixed over-the-air signals, the framework achieves high link detection probability and moderate false alarm rate through causal inference methods for detecting data flows among nodes.
This work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据