Similarity-Based Clustering Strategy for Mobile Ad Hoc Multimedia Databases

Abstract

Multimedia data are becoming popular in wireless ad hoc environments. However, the traditional content-based retrieval techniques are inefficient in ad hoc networks due to the multiple limitations such as node mobility, computation capability, memory space, network bandwidth, and data heterogeneity. To provide an efficient platform for multimedia retrieval, we propose to cluster ad hoc multimedia databases based on their semantic contents, and construct a virtual hierarchical indexing infrastructure overlaid on the mobile databases. This content-aware clustering scheme uses a semantic-aware framework as the theoretical foundation for data organization. Several novel techniques are presented to facilitate the representation and manipulation of multimedia data in ad hoc networks: 1) using concise distribution expressions to represent the semantic similarity of multimedia data, 2) constructing clusters based on the semantic relationships between multimedia entities, 3) reducing the cost of content-based multimedia retrieval through the restriction of semantic distances, and 4) employing a self-adaptive mechanism that dynamically adjusts to the content and topology changes of the ad hoc networks. The proposed scheme is scalable, fault-tolerant, and efficient in performing content-based multimedia retrieval as demonstrated in our combination of theoretical analysis and extensive experimental studies.