Abstract. In this paper, we propose and evaluate RIDA, a novel informationdriven architecture for distributed data compression in a sensor network, allowing it to conserve energy and bandwidth and potentially enabling high-rate data sampling. The key idea is to determine the data correlation among a group of sensors based on the value of the data itself to significantly improve compression. Hence, this approach moves beyond traditional data compression schemes which rely only on spatial and temporal data correlation. A logical mapping, which assigns indices to nodes based on the data content, enables simple implementation, on nodes, of data transformation without any other information. The logical mapping approach also adapts particularly well to irregular sensor network topologies. We evaluate our architecture with both Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on publicly available real-world data sets. Our experiments on both simulation and real data show that 30% of energy and 80-95% of the bandwidth can be saved for typical multi-hop data networks. Moreover, the original data can be retrieved after decompression with a low error of about 3%. Furthermore, we also propose a mechanism to detect and classify missing or faulty nodes, showing accuracy and recall of 95% when half of the nodes in the network are missing or faulty.
Abstract. Ensuring that every sensor node has the same code version is challenging in dynamic, unreliable multi-hop sensor networks. When nodes have different code versions, the network may not behave as intended, wasting time and energy. We propose and evaluate DHV, an efficient code consistency maintenance protocol to ensure that every node in a network will eventually have the same code. DHV is based on the simple observation that if two code versions are different, their corresponding version numbers often differ in only a few least significant bits of their binary representation. DHV allows nodes to carefully select and transmit only necessary bit level information to detect a newer code version in the network. DHV can detect and identify version differences in O(1) messages and latency compared to the logarithmic scale of current protocols. Simulations and experiments on a real MicaZ testbed show that DHV reduces the number of messages by 50%, converges in half the time, and reduces the number of bits transmitted by 40-60% compared to DIP, the state-of-the-art protocol.
The ability to create super high-resolution video is becoming relative easy to do either through a single high-definition video camera or panoramic video that automatically stitches multiple views together. As an example of the former, the motion picture industry now has 6000 × 4000 pixel full-rate video cameras available. This means that supporting region-of-interest cropping will become more important in the future. In this article, we propose a mechanism to support region-of-interest adaptation of stored video. The proposed approach creates a compression-compliant stream (e.g., MPEG-2), while still allowing it to be cropped. Fortunately, video standards like MPEG-2 specify the format of a compliant stream, and not the algorithm to get there. As a result, there is an opportunity to allow system researchers and implementers ways to optimize for applications. We show various fundamental tradeoffs that are made in order to support region-of-interest cropping with super high-resolution video which we received from a local motion-picture firm.
CORIE is a pilot environmental observation and forecasting system (EOFS) for the Columbia River. The goal of CORIE is to characterize and predict complex circulation and mixing processes in a system encompassing the lower river, the estuary, and the near-ocean using a multi-scale data assimilation model.
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