Jiang Peng, Feng Xinglin, Feng Renhai, Cui Junpeng
School of Electrical and Information Engineering, Weijin Road Campus, Tianjin University, Nankai District, Tianjin 300072, China.
Tianjin Motimo Membrane Tech. Co., Ltd., Tianjin 300457, China.
Sensors (Basel). 2025 Feb 22;25(5):1347. doi: 10.3390/s25051347.
Topology control is important for extending networks lifetime and reducing interference. The accuracy of topology identification plays a crucial role in topology control. Traditional passive interception can only identify the connectivity among cooperative sensor networks with known protocol. This paper proposes a novel method called Active Interfere and Passive Interception (AIPI) to identify the topology of non-cooperative sensor networks by using both active and passive interceptions. Active interception uses full duplex sensors to disrupt communication until frequency hopped to acquire distance information, and thus, infer their connectivity and calculate the location after modifying error in a non-cooperative sensor network. Passive interception uses Granger causality to infer the connectivity between two communication nodes after getting the time frame structure in physical layer. Passive interception is applied to conserve power consumption after obtaining physical information via active interception. Simulation results indicate that AIPI can identify the topology of non-cooperative sensor networks with a higher accuracy than traditional method.
拓扑控制对于延长网络寿命和减少干扰至关重要。拓扑识别的准确性在拓扑控制中起着关键作用。传统的被动拦截只能识别具有已知协议的协作传感器网络之间的连通性。本文提出了一种名为主动干扰与被动拦截（AIPI）的新方法，通过主动和被动拦截来识别非协作传感器网络的拓扑结构。主动拦截使用全双工传感器干扰通信，直到跳频以获取距离信息，从而在非协作传感器网络中推断其连通性并在修正误差后计算位置。被动拦截在获取物理层的时间帧结构后，使用格兰杰因果关系来推断两个通信节点之间的连通性。被动拦截在通过主动拦截获得物理信息后用于节省功耗。仿真结果表明，AIPI能够比传统方法更准确地识别非协作传感器网络的拓扑结构。
