A vector quantizer is a system for encoding the original data to reduce the bits needed for communication and storage saving\nwhile maintaining the necessary fidelity of the data. Signal processing over distributed network has received a lot of attention in\nrecent years, due to the rapid development of sensor network. Gathering data to a central processing node is usually in feasible for\nsensor network due to limited communication resource and power. As a kind of data compression methods, vector quantization\nis an appealing technique for distributed network signal processing. In this paper, we develop two distributed vector quantization\nalgorithms based on the Linde-Buzo-Gray (LBG) algorithm and the self-organization map (SOM). In our algorithms, each node\nprocesses the local data and transmits the local processing results to its neighbors. Each node then fuses the information from the\nneighbors. Our algorithms remarkably reduce the communication complexity compared with traditional algorithms processing all\nthe distributed data in one central fusion node. Simulation results show that both of the proposed distributed algorithms have good\nperformance.
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