Over the past few years the semantics community has developed ontologies to describe concepts, relationships between different entities in various application domains, including Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users, developers is that semantic techniques increase the complexity, processing time, therefore they are unsuitable for dynamic, responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability, discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good, scalable semantic model design, follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis,, assess IoT-Lite against another solution in terms of round time trip (RTT) performance for query-response times.

Advances in communication technologies and artificial intelligence are accelerating the paradigm of industrial Internet of Things (IIoT). With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. Although IIoT is considered a subset of IoT, it has its own peculiarities in terms of higher levels of safety, security, and low-latency communication in an environment of critical real-time operations. Under these circumstances, discriminative deep learning (DL) algorithms are unsuitable due to their need for large amounts of labeled and balanced training data, uncertainty of inputs, etc. To overcome these issues, researchers have started using deep generative models (DGMs), which combine the flexibility of DL with the inference power of probabilistic modeling. In this article, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust-boundary protection, network traffic prediction, and platform monitoring. Following an analysis of existing IIoT DGM implementations, we identify challenges (i.e., weak discriminative capability, insufficient interpretability, lack of generalization ability, generated data vulnerability, privacy concern, and data complexity) that need to be investigated in order to accelerate the adoption of DGMs in IIoT and also propose some potential research directions.

Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.

With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.