LAUSR

Recently, research has been active in various fields in relation to multimedia data retrieval to reduce the accuracy and calculation costs of multimedia retrieval. In particular, various search methods using multimedia has been developed. However, in many applications currently operating in the system, large multimedia data is distributed across servers or sites at different locations, so assembling multimedia data only as a hash presents huge computational, communication, and storage costs. In this paper, when searching for multimedia information stored in distributed environments using IoT devices, multiple hash chain-based multimedia retrieval techniques are proposed to minimize accuracy and calculation costs. The proposed technique is aimed at improving the accuracy of multimedia by using IoT devices to divide-and-conquer multimedia information that is collected and stored distributed. The proposed technique provides probability values for each segmented multimedia information by disaggregating the various attribute information that is composed of multimedia as much as possible, thereby increasing the accuracy of the multimedia that users want. Suggestion techniques determine the group size of multimedia data according to the priority of layered multimedia data. Multimedia group data include multimedia data contained in higher layers and subdivides the various attribute information constituting multimedia data. In addition, the proposed techniques improve the accessibility of multimedia data over existing ones by linking priority information between multimedia data.