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AIPI: Network Status Identification on Multi-Protocol Wireless Sensor Networks
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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.
Title: AIPI: Network Status Identification on Multi-Protocol Wireless Sensor Networks
Description:
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.
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