Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Responsive Production in Manufacturing: A Modular Architecture

[EN] This paper proposes an architecture aiming at promoting the convergence of the physical and digital worlds, through CPS and IoT technologies, to accommodate more customized and higher quality products following Industry 4.0 concepts. The architecture combines concepts such as cyber-physical systems, decentralization, modularity and scalability aiming at responsive production. Combining these aspects with virtualization, contextualization, modeling and simulation capabilities it will enable self-adaptation, situational awareness and decentralized decision-making to answer dynamic market demands and support the design and reconfiguration of the manufacturing enterprise.The research leading to these results has received funding from the European Union H2020 project C2 NET (FoF-01-2014) nr 636909.Marques, M.; Agostinho, C.; Zacharewicz, G.; Poler, R.; Jardim-Goncalves, R. (2018). Responsive Production in Manufacturing: A Modular Architecture. 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Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

Energy efficient assignment and deployment of tasks in structurally variable infrastructures

The importance of cyber-physical systems is growing very fast, being part of the Internet of Things vision. These devices generate data that could collapse the network and can not be assumed by the cloud. New technologies like Mobile Cloud Computing and Mobile Edge Computing are taking importance as solution for this issue. The idea is offloading some tasks to devices situated closer to the user device, reducing network congestion and improving applications performance (e.g., in terms of latency and energy). However, the variability of the target devices’ features and processing tasks’ requirements is very diverse, being difficult to decide which device is more adequate to deploy and run such processing tasks. Once decided, task offloading used to be done manually. Then, it is necessary a method to automatize the task assignation and deployment process. In this thesis we propose to model the structural variability of the deployment infrastructure and applications using feature models, on the basis of a SPL engineering process. Combining SPL methodology with Edge Computing, the deployment of applications is addressed as the derivation of a product. The data of the valid configurations is used by a task assignment framework, which determines the optimal tasks offloading solution in different network devices, and the resources of them that should be assigned to each task/user. Our solution provides the most energy and latency efficient deployment solution, accomplishing the QoS requirements of the application in the process.Plan Propio de Investigación de la UMA Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec