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.
Highlights
In recent years, mobile ad hoc networks have been increasingly popular in building temporary network connections in special areas, such as battlefields or disaster spots, where infrastructures are destroyed or too expensive to be built
Within the scope of ad hoc networks, most of the previous research focuses on the routing protocols that adapt to the dynamic network topology [6]; information retrieval is becoming a hot issue in a variety of recent applications [3,37]
The semantic contents can be obtained through the recognition of objects [34], which is performed in two phases:
Summary
Mobile ad hoc networks have been increasingly popular in building temporary network connections in special areas, such as battlefields or disaster spots, where infrastructures are destroyed or too expensive to be built. An ad hoc network is a collection of cooperative mobile nodes that communicate with each other without the intervention of accessing points. These mobile nodes are capable of storing and processing data, and performing complex operations through their communications, such as on-demand routing [4,5] or multimedia data streaming [2]. The data set of the LSA includes two sets of entities: the objects whose semantics are already known (training samples), and multimedia segments whose semantics remain unknown [8]. The latent semantic information obtained in the analysis process can be trained to show the personalized concepts from users [11], improving the accuracy of content representation
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