Automatic Recognition of Identification Schemes for IoT Identifiers via Sequence-to-Sequence Model

In Internet of Things (IoT), each object requires a unique identifier to identify itself and index its detailed profile to support mutual recognitions among multiple objects. However, existing IoT identifiers belonging to different identification schemes are heterogeneous from each other, which create a great challenge for the applications that need to resolve the heterogeneous identifiers. To address this challenge, we propose an algorithm to automatically recognize the heterogeneous identification schemes used by various IoT identifiers, based on a sequence-to-sequence (seq2seq) model consisting of an encoder and a decoder. The encoder uses one Long Short-Term Memory (LSTM) to map the identifier sequence to a vector of fixed dimensionality, and the decoder uses another LSTM to unfold the vector into a target sequence representing the identification scheme of this identifier. To evaluate our algorithm, we create a new dataset named ID-20 with 20 categories of IoT identifiers and conduct experiments on it. The results demonstrate the superiority of our algorithm against other state-of-the-art methods, with an identifier recognition accuracy of up to 94.57%.