The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important...The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.展开更多
In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid for...In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.展开更多
文摘The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.
文摘In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.
