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We present a new adaptive and energy-efficient broadcast dissemination model that supports flexible responses to client requests. In current broadcast dissemination models, clients must specify precisely what documents they require, and servers disseminate exactly those documents. This approach can be impractical, since in practice, clients may know the characteristics of the documents, but not the document names or IDs. In our model, clients specify the required document using attributes, and servers broadcast documents that match client requests at a prespecified level of similarity. A single document may satisfy several clients, so the server broadcasts a minimal set of documents that achieves a desired level of satisfaction in the client population. We introduce a mechanism for the server to obtain randomized feedback from clients to adapt its broadcast program to client needs. Finally, the server integrates a selective tune-in scheme based on approximate index matching to allow clients to conserve energy. Our simulation results show that our model captures client interest patterns efficiently and accurately and scales very well with the number of clients, while reducing overall client average waiting times. The selective tune-in scheme can considerably reduce the consumption of client energy with moderate waiting time overhead.
Wireless systems, Content Analysis and Indexing, Dissemination, Relevance feedback, Similarity measures

C. V. Ravishankar and W. Wang, "Adaptive Broadcasting for Similarity Queries in Wireless Content Delivery Systems," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 504-518, 2007.
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