Bandwidth-Optimal Group-Based Malinformation Filtering for Secure Data Aggregation in Wireless Sensor Networks
Keywords:
Wireless Sensor Networks, Malinformation Attacks, Secure Data Aggregation, Group-Based FilteringAbstract
Wireless Sensor Networks (WSNs) are finding more and more application in sensitive areas which include environmental control, healthcare, industrial control, and military surveillance. Their unrestricted wireless broadcast, unsupervised deployment, and extreme resource limitations however render them extremely susceptible to malinformation attacks and especially to the false data that have been propagated by groups of sensor nodes that have been compromised. These attacks do not only distort sensing results, but also consume too much bandwidth and drain energy fast. The article explores the nature and the effects of group-based malinformation attacks on multipath WSN systems, and draws attention to the weaknesses of the conventional node-centric and probabilistic detection strategies. To overcome these issues, a Bandwidth-Optimal Group-Injected Data Filtering (BO-GIDF) architecture is highlighted which combines collaborative filtering with data aggregation and authentication schemes. The proposed strategy can significantly reduce the spread of false data by identifying coordinated malicious behavior in the middle of data transmission and, consequently, saving network resources, and increasing the stability of the entire network. The paper shows that group-aware filtering is a key to scalable, power-efficient, and reliable data aggregation in the current WSNs.
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References
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