Abstract-In this paper we present the development of a new self-reconfigurable robotic platform for performing on-line and on-board evolutionary experiments. The designed platform can work as an autonomous swarm robot and can undergo collective morphogenesis to actuate in different morphogenetic structures. The platform includes a dedicated power management, rich sensor mechanisms for on-board fitness measurement as well as very powerful distributed computational system to run learning and evolutionary algorithms. The whole development is performed within several large European projects and is open-hardware and open-software.
Industry 4.0 tries to digitalize the production process further. The digitalization is achieved by connecting different entities (machines, worker) to data-exchange, which needs to be dynamic and to adapt to different changing situations and members in the process. However, just exchanging data might lead to confidentiality issues. The data-exchange needs to be protected to secure the confidentiality and trust in the system. Therefore, security rules need to adapt to these dynamic situations. One part of a possible solution might be dynamic access control rules. However in many cases, existing "legacy" systems are reused, which can in not handle dynamic access control rules. Due to this gap between the required and provided functionality, we propose an approach, which integrates dynamic access control based on the system-context into legacy systems. Our approach uses a security adaption controller, which dynamically adapts the access control rules to a new situation and integrates them into an existing legacy system. We discussed our approach with industrial practitioners and related our approach to their existing legacy system. In addition, we performed a scalability analysis to demonstrate the applicability of our approach in a realistic environment.
CCS CONCEPTS• Security and privacy → Domain-specific security and privacy architectures; • Computer systems organization → Self-organizing autonomic computing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.