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The interest to the concept of the Internet of Things is growing rapidly in the scenario of wireless telecommunications. The main idea behind this concept is the usage of the Internet as a common platform for connection of billions of Things around us. The main missing attribute in the Internet of Things concept is still the lack of interaction techniques with or between these Things. Hence, the Internet of Conversational Things makes this concept to happen and brings to the idea of direct interaction with all these devices. In this paper, we will describe and analyse the techniques of Internet of Conversational Things concept by examining the state-of-the-art approaches. At the end, we will come up with the new concept of Internet of Speech that will provide more connectivity between Things.

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Searching for digital images in large-scale multimedia database is a hard problem due to the rapid increase of the digital assets. Metric Permutation Table is an efficient data structure for large-scale multimedia indexing. This data structure is based on the Permutation-based indexing, that aims to predict the proximity between elements encoding their location with respect to their surrounding. The main constraint of the Metric Permutation Table is the indexing time. With the exponential increase of multimedia data, parallel computation is needed. Opening the GPUs to general purpose computation allows to perform parallel computation on a powerful platform. In this paper, we propose efficient indexing and searching algorithms for the Metric Permutation Table using GPU and multi-core CPU. We study the performance and efficiency of our algorithms on large-scale datasets of millions of images. Experimental results show a decrease of the indexing time while preserving the quality of the results.

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