Cluster-Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks

Abstract EN

In wireless sensor networks (WSNs), data provenance records the data source and the forwarding and the aggregating information of a packet on its way to the base station (BS).

To conserve the energy and wireless communication bandwidth, the provenances are compressed at each node along the packet path.

To perform the provenances compression in resource-tightened WSNs, we present a cluster-based arithmetic coding method which not only has a higher compression rate but also can encode and decode the provenance in an incremental manner; i.e., the provenance can be zoomed in and out like Google Maps.

Such a decoding method raises the efficiencies of the provenance decoding and the data trust assessment.

Furthermore, the relationship between the clustering size and the provenance size is formally analyzed, and then the optimal clustering size is derived as a mathematical function of the WSN’s size.

Both the simulation and the test-bed experimental results show that our scheme outperforms the known arithmetic coding based provenance compression schemes with respect to the average provenance size, the energy consumption, and the communication bandwidth consumption.