SenseChain: Blockchain based Reputation System for Distributed Spectrum Enforcement

Abstract

Distributed enforcement of spectrum policies require fusion of sensing results from a set of spatially scattered sensors to detect anomalous behavior with the highest possible accuracy. Central to this problem is the lack of trust or reputation of the participating sensors, which often leads to incorrect and biased inferences. In SenseChain, we leverage the distributed consensus mechanism employed in Blockchain networks to capture the reputation of the sensors, leading to a highly reliable and accurate enforcement system. Specifically, we define and analyze a detection mechanism to identify falsifying sensors using a distributed anomaly detection system and use the Blockchain to record the individual’s behavior. The reputation is then based on the combination of the difficulty level of the consensus method and the degree of falsehood in the reported sensor values. We evaluate SenseChain using an integrated Blockchain and anomaly detection simulator to show that DLTs can be used to track reputation of distributed sensors for distributed enforcement of spectrum policies.