Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Data Credence in IoR: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

Data Credence in IoT: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

TOWARDS DIGITAL TWINS FOR OPTIMIZING METRICS IN DISTRIBUTED STORAGE SYSTEMS - A REVIEW

With the exponential data growth, there is a crucial need for highly available, scalable, reliable, and cost-effective Distributed Storage Systems (DSSs). To ensure such efficient and fault tolerant systems, replication and erasure coding techniques are typically used in traditional DSSs. However, these systems are prone to failure and require different failure prevention and recovery algorithms. Failure recovery of DSS and data reconstruction techniques take into consideration different performance metrics optimization in the recovery process. In this paper, DSS performance metrics are introduced. Several recent papers related to adopting erasure coding in DSSs are surveyed together with highlighting related performance metrics introduced in the context of these papers. Next, we present recent literature where Digital Twins (DTs) are involved in monitoring DSSs and assisting the data center managers in intelligent decision-making. Finally, important open issues are identified to inspire future studies for fully efficient DSSs