Robust Trust Mechanisms for Monitoring Aggregator Nodes in Sensor Networks

Sensor nodes are often deployed in large numbers to monitor extended sensor fields. In such scenarios data aggregation plays a crucial role on summarizing data forwarded to base stations in sensor networks. As sensor networks are commonly deployed in open and unattended areas, they are vulnerable to physical tampering as well as remote attacks. Existing security techniques focus primarily on methods used by an aggregator node to monitor and detect compromised behavior by nodes that report data to it. We propose a novel mechanism, combining techniques from statistics and artificial intelligence, by which nodes reporting to an aggregator node can monitor whether the latter is reporting incorrect aggregated values. In our framework, nodes are arranged in a hierarchy. We develop a reputation management scheme in which each child node keeps a reputation value of its parent. For each data reporting event, a node is privy only to the data value it reported and the aggregated value forwarded by its parent node. Each node then calculates the probability of the parent reporting correctly aggregated values over an epoch of events by adapting a statistical hypothesis testing scheme. This probability is used to incrementally update the trustworthiness of the parent node using a learning scheme. When the trustworthiness of a node falls below a threshold it can no longer be trusted with the aggregation task and should be reallocated or eliminated from the network. We evaluate the robustness of our adaptation of a couple of statistical hypothesis testing schemes and analyze their applicability for different types of malicious behaviors by compromised sensor nodes.