Special Issue: Identification, Information, and Knowledge in the Internet of Things
Hao Wu, Rongfang Bie, Charith Pereira, OMER F. RANA · Software Practice and Experience · 2020
Realizing the full potential of the Internet of Things (IoT) requires solving technical and business challenges including the identification of things, their organization, and integration. The subsequent management of large data volumes that are generated from such systems, and the effective use of knowledge-based decision systems that can make use of IoT resources remains a challenge at present. Various representation formats already exist for specifying sensors and devices that are part of the IoT ecosystem. However, many of these are either specific to use within a particular application area (e.g., environmental monitoring), or specific to a middleware platform. Overcoming device, firmware, and data format heterogeneity remains a significant challenge in real world IoT systems. Consequently, dealing with data that are generated from such systems and reasoning with these data is constrained due to these limitations. IoT-based platforms also offer a variety of different communication protocols (for both long range [at low data rates] and short range [at high data rates]), such as SigFox, LoRaWAN, NB-IoT, Wifi Direct, and so on. These protocols generally offer different decision points around energy used, distance covered, and data rates observed. Another aspect of heterogeneity in IoT systems therefore relates to dealing and switching between these protocols based on context of use and application requirements. We received 13 papers aligned with the theme of this special issue. In particular, the benefit of using deep learning to solve “traditional” problems is being recognized by a number of researchers. All papers were initially screened by the editors to ensure alignment with the theme of this special issue, and high-quality papers were sent to reviewers. In total, seven papers were selected for inclusion in the special issue. The submitted papers combine the use of novel methods and demonstrate effective use of experimental techniques. The papers included in this special issue are: A physiological data-driven model for learners' cognitive load detection using HRV-PRV feature fusion and optimized XGBoost classification