Video sensors are widely used in many applications such as security monitoring and home care. However, the growth of the number of sensors makes it impractical to stream all videos back to a central server for further processing, due to communication bandwidth and server storage constraints. Multi-view video summarization allows us to discard redundant data in the video streams taken by a group of sensors. All prior multi-view summarization methods, however, process video data in an off-line and centralized manner, which means that all videos are still required to be streamed back to the server before conducting the summarization. This paper proposes an on-line, distributed multi-view summarization system, which integrates the ideas of Maximal Marginal Relevance (MMR) and MS-Wave, a bandwidth-efficient distributed algorithm for finding k-nearest-neighbors and k-farthest-neighbors. Empirical studies show that our proposed system can discard redundant videos and keep important keyframes as effectively as centralized approaches, while transmitting only 1/6 to 1/3 as much data.

In M2M applications, it is very common to encounter the ad hoc snapshot query that requires fast responses from many local machines in which all the data are distributed. In the scenario when the query is more complex, the communication cost for sending it to all the local machines for processing can be very high. This paper aims to address this issue. Given a reference set of multiple and large-size patterns, we propose an approach to identifying its k nearest and farthest neighbors globally across all the local machines. By decomposing the reference patterns into a multi-resolution representation and using novel distance bound designs, our method guarantees the exact results in a communication-efficient manner. Analytical and empirical studies show that our method outperforms the state-of-the-art methods in saving significant bandwidth usage, especially for large numbers of machines and large-sized reference patterns.