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In order to save human lives and reduce injury and property loss, Situation Awareness (SA) information is essential and important for rescue workers to perform the effective and timely disaster relief. The information is generally derived from the shared images via widely used smartphones. However, conventional smartphone-based image sharing schemes fail to efficiently meet the needs of SA applications due to two main reasons, i.e., real-time transmission requirement and application-level image redundancy, which is exacerbated by limited bandwidth and energy availability. In order to provide efficient image sharing in disasters, we propose a bandwidth- and energy- efficient image sharing system, called BEES. The salient feature behind BEES is to propose the concept of Approximate Image Sharing (AIS), which explores and exploits approximate feature extraction, redundancy detection, and image uploading to trade the slightly low quality of computation results in content-based redundancy elimination for higher bandwidth and energy efficiency. Nevertheless, the boundaries of the tradeoffs between the quality of computation results and efficiency are generally subjective and qualitative. We hence propose the energy-aware adaptive schemes in AIS to leverage the physical energy availability to objectively and quantitatively determine the tradeoffs between the quality of computation results and efficiency. Moreover, unlike existing work only for cross-batch similar images, BEES further eliminates in-batch ones via a similarity-aware submodular maximization model. The response time of querying similar images is reduced via leveraging a geographic coordinate based index partitioning scheme. We have implemented the BEES prototype which is evaluated via three real-world image datasets. Extensive experimental results demonstrate the efficacy and efficiency of BEES. We have released the source codes of BEES at GitHub. [ABSTRACT FROM AUTHOR]

Data reduction has become increasingly important in storage systems due to the explosive growth of digital data in the world that has ushered in the big data era. One of the main challenges facing large-scale data reduction is how to maximally detect and eliminate redundancy at very low overheads. In this paper, we present DARE, a low-overhead deduplication-aware resemblance detection and elimination scheme that effectively exploits existing duplicate-adjacency information for highly efficient resemblance detection in data deduplication based backup/archiving storage systems. The main idea behind DARE is to employ a scheme, call Duplicate-Adjacency based Resemblance Detection (DupAdj), by considering any two data chunks to be similar (i.e., candidates for delta compression) if their respective adjacent data chunks are duplicate in a deduplication system, and then further enhance the resemblance detection efficiency by an improved super-feature approach. Our experimental results based on real-world and synthetic backup datasets show that DARE only consumes about 1/4 and 1/2 respectively of the computation and indexing overheads required by the traditional super-feature approaches while detecting 2-10 percent more redundancy and achieving a higher throughput, by exploiting existing duplicate-adjacency information for resemblance detection and finding the “sweet spot” for the super-feature approach. [ABSTRACT FROM PUBLISHER]