NETWORKS IN INTERNET OF THINGS

The Internet was from the start used to move data packs among customers and data sources with a specific IP address. Due to types of progress, the Internet is being used to split data between different little, resource obliged devices related in billions to contain the Internet of Things. A great deal of data from these contraptions powers overhead on the IoT network. Therefore, it is expected to offer responses for various association related issues in IoT including coordinating, energy protection, blockage, heterogeneity, versatility, reliability, nature of organization and security to in a perfect world use the open association. In this paper, a sweeping diagram on the association upgrade in IoT is presented. The paper draws a thought towards the establishment of IoT and its capability with various advancements, discussion on association smoothing out in IoT and computations gathering. Finally, front line systems for IoT explicitly to mastermind upgrade are discussed reliant on the new works and the overview is done up with open issues and troubles for network improvement in IoT. This paper not simply studies, considers and unions the new related works, yet moreover regards the maker's disclosures, plans and analyzes its support towards network improvement in IoT

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.Comment: 7 pages, 2 figures, 1 table, 1 listing. Submitted to the IEEE International Conference on Industrial Informatics 2019, INDIN'1

A knowledge based machine tool maintenance planning system Using case-based reasoning techniques

In advanced manufacturing systems, Computer Numerical Control (CNC) machine tools are important equipment to manufacture product components of high precision, whilst from equipment maintenance point of view, they are regarded as the ‘products’ provided by machine tool manufacturers. Therefore, the reliability of CNC machine tools affects not only the quality of the components they manufacture, but also the reputation and profits of equipment suppliers. This paper presents a novel knowledge-based maintenance planning system to facilitate information and knowledge sharing between all stakeholders including machine tool manufacturers, users (manufacturing systems), maintenance service providers and part suppliers (for machine tools), in the emerging ‘Product-Service’ business model. Case Based Reasoning principles have been implemented to improve the efficiency of maintenance planning. Ontologies were adopted to represent field knowledge using adaptation guided retrievals based on semantic similarity and correlation. The adaption algorithm has been developed based on the Casual Theory and the dependence relationship to generate the solution for required maintenance problems. The proposed system was implemented using Content Management technologies, which proved to have advantages over traditional database systems in managing engineering knowledge, and has been verified using an example CNC machine tool. The results were commented by industrial collaborators as very promising and further exploitation in industry was recommended

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data