Bigraphs provide a versatile modeling framework by combining spatial and connectivity relationships, making them valuable in graph transformations, model transformations, the design of programming languages, and the simulation of reactive systems. However, practical approaches to bigraph rewriting often rely on constraint satisfaction or SAT-solving techniques, which can be computationally intensive and limit usability in large-scale applications. This article addresses the challenge of efficient bigraph rewriting by leveraging the graph transformation engine GrGen.NET . We propose a novel approach to translate bigraphical rules into SPO-based graph transformation rules, enabling fast bigraph rewriting. Our solution is implemented in the tool BiGGer , which includes a command-line interface and a Java library for seamless integration. Through experimental evaluation, we demonstrate the correctness of our approach and its ability to significantly improve execution efficiency. Additionally, we implemented tracking rules that maintain semantic fidelity during the translation process and enable identity tracing across reactions, adding a feature to bigraphs that extends their applicability beyond static analysis.

The number of Internet of Things (IoT) devices increases and will become an ever important source of information made available through their sensors. As a result, devices form denser networks producing a huge variety and volume of data. If intercommunication and interaction between many decentralized resources are not considered as primary objective by vendors, networking distributed IoT devices will be complicated due to their heterogeneity. Thus, mastering the challenge of collecting and processing data with low-latency is a difficult task. In this paper, we present a practical and easy to employ reference software architecture for fog computing application scenarios, enabling the communication between a multitude of devices which require an efficient and robust real-time system. As proof, we conduct a practical demonstration—a three-dimensional mouse is constructed, called the Cube-It, to control a six-joint robot (i.e., the UR10). The findings of this work are expected to aid researchers studying the integration of heterogeneous IoT devices within fog computing environments comprising many sensors and actuators.