Sensor Network Provenance Compression Using Dynamic Bayesian Networks

Abstract

Provenance records the history of data acquisition and transmission. In wireless sensor networks (WSNs), provenance is critical for many different purposes, including assessing the trustworthiness of data acquired and forwarded by sensors, supporting situation awareness, and detecting early signs of attacks. However, a major drawback in provenance for WSNs is its size. It is thus critical to develop efficient techniques for provenance encoding. A major issue of previously proposed provenance encoding techniques is that the size of the provenance either expands too fast with increases in the number of packet transmission hops or is very sensitive to the WSN’s topology, i.e., the size of the provenance expands drastically with changes in the WSN’s topology. In this article, we propose a novel provenance encoding technique based on dynamic Bayesian network and overlapped arithmetic coding scheme, which addresses such an issue. Through theoretical analysis, simulation, and testbed experiments, we show that our scheme outperforms other WSN lightweight provenance schemes with respect to provenance size and energy consumption.