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The cumulating carbon fiber-reinforced polymer (CFRP) composite wastes need to be disposed efficiently. So far, the most effective thermal-based recycling technique, namely pyrolysis, has grown exponentially towards industrial scaling in developed countries such as the UK and Germany. Typically, even the slightest mistakes can cause unfavorable results and delays in workflow within such a massive operating environment (e.g., >1 tonnes/day operating capacity). The existing semi-automated and, in some cases, fully automated plants should be continuously updated to resemble the varying classes and volume of the CFRP composite wastes. To overcome such research gaps and imprecise manual errors, Internet-of-Things (IoT) based framework is proposed. This paper studies the theoretical implementation of an IoT-based framework into the pyrolysis process to recycle CFRP composite waste to manage the process based on the principles of cyber-physical systems. The proposed framework consists of sensors and actuators that will be used to collect the data and communicate with a central management constructed as a platform that will articulate and manipulate data to satisfy the requirements of the recycling process, computationally modeled through logical relations between physical entities. In this case, the management unit can be either controllable or monitored remotely to increase the operation time of the plant. Our objective is to propose a scalable method to improve the recycling process, which will also help future decision-making in handling recycled carbon fiber. Specifically, this study will go beyond the state-of-the-art in the field by (i) automatically calculate the mass of the waste and adjust the operating time, temperature, atmospheric pressure, and inert gas flow (if needed), (ii) regenerating heat so that after the first batch is recycled, the resin high in calorific value will be burned and will be releasing energy, whose generated heat needs to be trapped inside the furnace and then regenerated into the system, and (iii) decrease energy consumption and fasten the process flow time. In summary, the proposed framework aims to provide a user-friendly control and temperature monitoring that can increase the overall efficiency of the process and avoid possible process shut down or even char formation by a controlled atmosphere in the pyrolytic reactor. Post-print / Final draft

The world population is increasing at a rapid speed and so are the requirements for everyday life like energy, technology, and industries. In the past few years, there has been a huge increase and development in the industrial sector and new technology like the Internet of Things (IoT) has played a major role in it. The industrial sector is consuming a large portion of the energy worldwide and is contributing a large amount of greenhouse gas emissions. Hence, they face significant economic, social and environmental pressures to create energy-efficient processes and systems of production and directly manage their energy consumption, looking at aspects beyond direct costs. One of the effective ways to reduce energy consumption, cost, and greenhouse gas emissions is to use an industrial energy management system (IEnMS). The IEnMS uses IoT for (big) data collection, processing, storing, and visualization. In this case, one key challenge the industries face is the selection of an IoT platform among the hundreds of IoT platforms in the market. In this paper, we adapt our previously proposed general framework to choose an IoT platform focusing here on the requirements of IEnMS. The proposed framework provides an objective methodology that can be used to select the most suitable IoT platform for different IEnMS based on their particular requirements.

The concept of Internet of Things (IoT) implies that objects are connected to share information with each other and/or with a platform based on standardized communication protocols such as MQTT and HTTP. Recently, their performance has received much attention due to more widespread utilization. But, the vital aspect of the adoption of these IoT protocols is not just performance per se but also their feature set. However, comparative analysis that takes into account both the features and performance aspects are rarely presented. This paper investigates a combination of a qualitative comparison model (based on communication, operational and security attributes) with quantitative metrics in terms of CPU/memory utilization and time consumption. Quantitative metrics are collected for two of the four IoT protocols. Our analysis shows that there is no onesize-fits-all solution in terms of application layer IoT protocols. Additionally we emphasize that, on a higher level, both qualitative and quantitative measures are necessary for IoT stakeholders to consider before selecting the most appropriate protocol for the specific application to be deployed. We illustrate the results using a smart grid example simulating a collection of 1000 smart meters connected to the grid. Post-print / Final draft

Security and usability have evolved independently, therefore, expertise in both of these domains are hard to find in one person. This research aims to assist security and usability designers and developers by influencing their decision-making abilities when it comes to the conflicts between security and usability. It does so by proposing the use of usable security patterns for assisting the developers and designers in making accurate choices when handling the conflicts. A novel methodology is presented for identifying usable security patterns from existing implementations, which are effectively managing the security and usability trade-offs. The aim is to identify such implementations while documenting the suitable trade-offs in the format of patterns for use by other developers and designers. To instantiate the methodology, a case study was conducted whose results are also presented in the paper.

This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and machine learning algorithms. We propose a general framework based on three layers (physical, data and decision) that defines the possible designing options so that the rare events/anomalies can be detected ultra-reliably. This general framework is then applied in a well-known benchmark scenario, namely Tennessee Eastman Process. We then analyze this benchmark under three threads related to data processes: acquisition, fusion and analytics. Our numerical results indicate that: (i) event-driven data acquisition can significantly decrease the number of samples while filtering measurement noise, (ii) mutual information data fusion method can significantly decrease the variable spaces and (iii) quantitative association rule mining method for data analytics is effective for the rare event detection, identification and diagnosis. These results indicates the benefits of an integrated solution that jointly considers the different levels of data processing following the proposed general three layer framework, including details of the communication network and computing platform to be employed., Grant numbers : FIREMAN - Framework for the Identification of Rare Events via MAchine learning and IoT Networks.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Part 3: Workshop on Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes; International audience; Academic research and existing implementations of various systems and services identify instances of conflict between security and usability. Engineering the right trade-offs between security and usability is often not an easy task. Engineering of such trade-offs is mainly reliant on developers’ skills, who are either experts in security or usability. This research aims to assist the developers in engineering the right trade-offs by proposing the use of patterns. Patterns provide benefits like means of common vocabulary, shared documentation, reuse, among others. The use of patterns can assist security and usability developers by influencing their decision-making abilities when dealing with conflicts in other but similar context of use. For the identification of such patterns, the paper presents a three-stage methodology. To instantiate the methodology, a case study was conducted whose results are also presented in the paper.