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Special Issue: Identification, Information, and Knowledge in the Internet of Things
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-09-18 , DOI: 10.1002/spe.2901
Hao Wu 1 , Rongfang Bie 1 , Charith Pereira 2 , Omer Rana 2
Affiliation  

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:

中文翻译:

特刊:物联网中的识别、信息和知识

实现物联网 (IoT) 的全部潜力需要解决技术和业务挑战,包括事物的识别、事物的组织和集成。此类系统产生的大量数据的后续管理,以及能够利用物联网资源的基于知识的决策系统的有效利用,目前仍是一个挑战。已经存在各种表示格式,用于指定作为物联网生态系统一部分的传感器和设备。然而,其中许多要么特定于在特定应用领域(例如,环境监控)内使用,要么特定于中间件平台。克服设备、固件和数据格式的异构性仍然是现实世界物联网系统中的重大挑战。最后,由于这些限制,处理从此类系统生成的数据以及对这些数据进行推理受到限制。基于物联网的平台还提供各种不同的通信协议(适用于长距离 [低数据速率] 和短距离 [高数据速率]),例如 SigFox、LoRaWAN、NB-IoT、Wifi Direct 等. 这些协议通常围绕使用的能量、覆盖的距离和观察到的数据速率提供不同的决策点。因此,物联网系统中异构性的另一方面涉及基于使用上下文和应用程序要求在这些协议之间进行处理和切换。我们收到了与本期特刊主题一致的 13 篇论文。特别是,使用深度学习解决“传统”问题的好处正在被许多研究人员所认可。所有论文最初都经过编辑筛选,以确保与本期特刊的主题保持一致,并将高质量的论文发送给审稿人。总共有七篇论文被选入特刊。提交的论文结合了新方法的使用,并展示了实验技术的有效使用。本期特刊收录的论文有:
更新日期:2020-09-18
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