Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach.

Equipment Quality Data Integration and Cleaning Based on Multiterminal Collaboration

With the advancement of digital manufacturing technology, data-driven quality management is getting more and more attention, and it is developing rapidly under the impetus of technology and management. Quality data are growing exponentially with the help of increasingly interconnected devices and IoT (Internet of Things technologies). Aiming at the problems of insufficient quality data acquisition and poor data quality of complex equipment, the research on quality data integration and cleaning based on digital total quality management is carried out. The data integration architecture of complex equipment quality based on multiterminal collaboration is constructed. The architecture integrates a variety of integration methods and standards, such as XML, OPC-UA, and QIF protocol. Then, to unify the data view, a cleaning method of complex equipment quality data based on the combination of edit distance and longest common subsequence similarity calculation is proposed, and its effectiveness is verified. It provides the basis for the design of the digital total quality management system of complex equipment.

A Data Integration Architecture for Smart Cities

The data generated by smart cities have low integration, as the systems that produce them are usually closed and developed for specific needs. Moreover, the large volume of data, and the semantic and structural changes in datasets over time make the use of data to support decision-making even more difficult. In this work, we identify the main requirements of a data integration system to support decision-making in cities, focusing on its challenges. We analyze some existing data integration solutions, to uncover their features and limitations. Based on these results, we propose a new microservice architecture to support the development of software platforms for integrating smart cities’ heterogeneous data and a guideline to assess their performance.

Components of a Digital Specimen Architecture for Biological Collections

In 2020, we began developing software components for an Application Programming Interface (API)-based integration architecture (the “Specify Network”) to leverage the global footprint of the Specify 7 collections management platform (www.specifysoftware.org) and the analytical services of the Lifemapper (lifemapper.org) and Biotaphy (biotaphy.org) Projects. The University of Kansas Lifemapper Project is a community gateway for species distribution and macroecological modeling. The Biotaphy Project, an extension of Lifemapper, is the product of a six-year, U.S. National Science Foundation-funded collaboration among researchers at the Universities of Michigan, Florida, and Kansas. Biotaphy's primary scope is to use big data methods and high-performance computing to integrate species occurrence data with phylogenetic and biogeographic data sets for large taxonomic and spatial scale analyses. Our initial integrations between Biotaphy and the Specify Network enable Specify users to easily discover remote information related to the specimens in their collection. The widely-discussed, digital specimen architecture being championed by DiSSCo (Distributed System of Scientific Collections www.dissco.eu) and others (https://bit.ly/3jfsAgz) will change data communications between biodiversity collections and the broader biodiversity data community. Those network interactions will evolve from being predominantly one-way, batch-oriented transfers of information from museums to aggregators, to an n-way communications topology that will make specimen record discovery, updates and usage much easier to accomplish. But museum specimens and their catalogs will no longer be an intellectual endpoint of species documentation. Rather, records in collections management systems will increasingly serve as a point of departure for data synthesis, which takes place outside of institutional data domains, and which will overlay the legacy role of museums as authoritative sources of information about the diversity and distribution of life on Earth. Biological museum institutions will continue to play a vital role as the foundation of a global data infrastructure connecting aggregators, collaborative databases, analysis engines, journal publishers, and data set archives. In this presentation, we will provide an update on the components and capabilities that make up integrations in the Specify Network as an exemplar of the global architecture envisaged by the biodiversity research community.

On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives

Digitization and automation have engulfed every scope and sphere of life. Internet of Things (IoT) has been the main enabler of the revolution. There still exist challenges in IoT that need to be addressed such as the limited address space for the increasing number of devices when using IPv4 and IPv6 as well as key security issues such as vulnerable access control mechanisms. Blockchain is a distributed ledger technology that has immense benefits such as enhanced security and traceability. Thus, blockchain can serve as a good foundation for applications based on transaction and interactions. IoT implementations and applications are by definition distributed. This means blockchain can help to solve most of the security vulnerabilities and traceability concerns of IoTs by using blockchain as a ledger that can keep track of how devices interact, in which state they are and how they transact with other IoT devices. IoT applications have been mainly implemented with technologies such as cloud and fog computing, and AI to help address some of its key challenges. The key implementation challenges and technical choices to consider in making a successful blockchain IoT (BIoT) project are clearly outlined in this paper. The security and privacy aspect of BIoT applications are also analyzed, and several relevant solutions to improve the scalability and throughput of such applications are proposed. The paper also reviews integration schemes and monitoring frameworks for BIoT applications. A hybrid blockchain IoT integration architecture that makes use of containerization is proposed.

A Proposed Integration Architecture for University Research Data Repository to Support University and University Hospital on Medical Digital Image Management and Analytics using Hadoop

Big Data has been used in university and hospital due to its enormous potential in managing large volume and many types of data. However, university that also has hospitals may need to integrate their data repository to have a single site access for easier system administration and management. The needs of image analytics for both researchers in the university and physicians in the university hospital demand the need of Big Data platform such as Hadoop framework. Based on the literatures, there are no papers that describe in detail the integration of big data for university, which include its own teaching hospital. Therefore, this paper focuses on the proposed research data architecture for university and university hospital to support data repository for both with capability of image analytics using Hadoop technology.