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BEES: Bandwidth- and Energy- Efficient Image Sharing for Real-time Situation Awareness
Pengfei Zuo, Yu Hua, Xue Liu, Dan Feng, Wen Xia, Shunde Cao, Jie Wu, Yuanyuan Sun and Yuncheng Guo
Huazhong University of Science and Technology, Huazhong University of Science and Technology, McGill Univerisity, Huazhong University of Science and Technology, Huazhong University of Science and Technology, Huazhong University of Science and Technology, Huazhong University of Science and Technology, Huazhong University of Science and Technology, Huazhong University of Science and Technology

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 bandwidthand 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. 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.