The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.

Internet of Things (IoT) technologies, when adequately integrated, cater for logistics optimisation and operations' environmental impact monitoring, both key aspects for today's EU ports management. This article presents Obelisk, a scalable and multi-tenant cloud-based IoT integration platform used in the EU H2020 PortForward project. The landscape of IoT protocols being particularly fragmented, the first role of Obelisk is to provide uniform access to data originating from a myriad of devices and protocols. Interoperability is achieved through adapters that provide flexibility and evolvability in protocol and format mapping. Additionally, due to ports operating in a hub model with various interacting actors, a second role of Obelisk is to secure access to data. This is achieved through encryption and isolation for data transport and processing, respectively, while user access control is ensured through authentication and authorisation standards. Finally, as ports IoTisation will further evolve, a third need for Obelisk is to scale with the data volumes it must ingest and process. Platform scalability is achieved by means of a reactive micro-services based design. Those three essential characteristics are detailed in this article with a specific focus on how to achieve IoT data platform scalability. By means of an air quality monitoring use-case deployed in the city of Antwerp, the scalability of the platform is evaluated. The evaluation shows that the proposed reactive micro-service based design allows for horizontal scaling of the platform as well as for logarithmic time complexity of its service time.

New protocols and technologies are continuously competing in the Internet of Things. This has resulted in a fragmented landscape that complicates the integration of different solutions. Standardization efforts try to avoid this problem, however within a certain ecosystem, multiple standards still require integration to enable trans-sector innovation. Moreover, existing devices require transformations to fit in an ecosystem. In this paper, we discuss several integration problems in the field of Low Power Wide Area Networks in the context of the Port of the Future and propose a new distributed platform architecture, called FLINT. FLINT is a framework to program flexible and configurable flows on a per device basis. A flow is constructed from fine-grained components, called adapters. Due to the modularity of an adapter, users can easily integrate existing software. We evaluated FLINT based on five levels of interoperability and show that FLINT can be used to interconnect non-interoperable systems and protocols on every level. We have also implemented FLINT in a container based environment and demonstrated that a basic configuration has a 99% forwarding rate of 17.500 513-byte packets per second, showing that the architecture can deliver good performance.

Despite their maturity and robustness, container orchestration platforms still suffer from some limitations. One of those concerns the lack of runtime adaptability of the scheduler to the overall cluster status as i) it instantiates containers with local optimization in mind i.e. it only considers the container-specific predefined requirements which may lead to a sub-optimal overall cluster state and ii) it does not reshuffle the deployed containers at runtime based on container observed behavior and interdependencies. These limitations become even more apparent within volatile load contexts. This work proposes an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. To this extend, two distinct combinatorial optimization heuristics, Simulated Annealing and Particle Swarm Optimization, are evaluated and compared on their respective effectiveness and efficiency as well as their relative performance towards the optimal solution obtained by Integer Linear Programming. Additionally, the impact of the proposed system on application service time is validated by means of two complementary use-cases, an event-based IoT data-hub platform and a web-based e-commerce app, with an average improvement of the end-to-end service time of 21.6% and 13.1% respectively.