IoT-DDL explicitly tools things to self-discover and securely share their own capabilities, entities, and services, including the various cloudbased accessories that may be attached to them. We introduce the IoT Device Description Language (IoT-DDL), a machine- and human-readable descriptive language for things, seeking to achieve such integration and homogenization. The IoT’s fragmented and widevarying nature introduces the need for additional effort to homogenize these things so they may blend together with the surrounding space to create opportunities for powerful and unprecedented IoT applications. We argue that the success of the Internet of Things (IoT) vision will greatly depend on how its main ingredient-the “thing”-is architected and prepared to engage. Results show that the introduced Funnel Fukunaga–Koontz transform outperforms other dimension reduction approaches, regardless of the input features and the number of locations. We present a new dimension reduction scheme consisting of an unsupervised funnel on top of a supervised dimension reduction approach. The study evaluates different input features and investigates the target positioning accuracy for multiple unsupervised and supervised dimensionality reduction methods. Hinged on a novel long-term evolution protocol dedicated for machine-type communications, the results focus on a unique packet exchange with a single access point to save battery life and simplify deployment. This article extends the application of CSI for indoor–outdoor detection on an unprecedented large area and considers mMTC-oriented long-term evolution and fifth-generation Internet of Things in the sub-GHz frequency band. Many works limited the solution assessment to a single room in a fully indoor testbed. Earlier studies highlighted the efficiency of positioning approaches based on channel-state information (CSI) in different environments. The massive machine-type communication will be at the core of ambient connectivity, requiring for energy-efficient systems.
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