Abstract
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.
Highlights
Wireless sensor networks (WSNs) are composed of a large number of low-cost, low-power, and randomly distributed wireless sensor nodes, which are intended to monitor physical or environmental data from the detecting areas and cooperatively pass the data to the base station (BS) or a desired actuator through wireless communication
As to the dictionary-based scheme (DP) [10], it has been compared with the arithmetic coding based provenance scheme (ACP) and the dynamic Bayesian network based provenance scheme (DBNP) schemes in [11] and the results show that the dictionary-based provenance encoding scheme (DP) scheme is sensitive to the WSN’s topology change, whereas the cluster-based lossless provenance arithmetic encoding scheme (CBP) scheme keeps stable when the WSN’s topology changes
In the test-bed experiments, Figure 14 shows the average provenance sizes for the ACP and the CBP schemes with respect to the number of packet transmission hops
Summary
Wireless sensor networks (WSNs) are composed of a large number of low-cost, low-power, and randomly distributed wireless sensor nodes (nodes, for short), which are intended to monitor physical or environmental data from the detecting areas and cooperatively pass the data to the base station (BS) or a desired actuator through wireless communication. In large-scale WSNs, the provenances generally cannot be directly and completely transmitted due to both the bandwidth and the energy constraints on wireless sensor nodes. To mitigate the average provenance size increases as well as utilize the provenance data efficiently, we propose a CBP (cluster-based provenance) encoding scheme for WSNs. The CBP scheme focuses on encoding and decoding the provenance incrementally (like Google Maps, can be zoomed in and out according to the user’s requirement) at the BS. (i) We proposed a cluster-based lossless provenance arithmetic encoding scheme (CBP) for WSNs. Our approach has the ability of encoding and decoding the provenance incrementally, and achieves a higher average provenance compression rate.
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