Mario Köppen

The Metaverse as a platform for virtual interaction underscores the critical importance of 3D assets in constructing virtual environments. This study proposes a Generative AI-based workflow for Metaverse asset creation by developing three main approaches: (1) 3D asset generation through a text-to-image and image-to-3D pipeline using SDXL, Hunyuan3D-2, Trellis, and TripoSR; (2) texture generation for 3D asset using StableProjectorZ and SDXL; and (3) 3D asset creation via scripts generated by Large Language Models (LLMs) integrated with Blender. The generative AI-assisted workflow is compared against a procedural generation method. Our experiments demonstrate the successful creation and implementation of 3D assets within a Metaverse platform, albeit with limitations such as the necessity for manual intervention and a dependency on the underlying pretrained models. Furthermore, the high complexity of AI-generated objects remains a primary challenge, necessitating a post-processing step to optimize these assets for use in a Metaverse environment. We achieved an average polygon count reduction of 89.29% in AI-generated objects while maintaining visual quality and ensuring usability within the OpenSimulator Metaverse environment. Furthermore, the average similarity to the original objects, measured by chamfer distance, reached 0.027 (L1) and 0.020 (L2). Our study highlights the trade-off of integrating generative AI into the 3D asset creation process, with careful consideration for the critical factors of diversity, quality, complexity, and usability.

Smart Farming (SF) has garnered interest from computer science researchers for its potential to address challenges in Smart Farming and Precision Agriculture (PA). This systematic review explores the application of Fuzzy Logic (FL) in these areas. Using a specific anonymous search method across five scientific web indexing databases, we identified relevant scholarly articles published from 2017 to 2024, assessed through the PRISMA methodology. Out of 830 selected papers, the review revealed four gaps in using FL to manage imprecise data in Smart Farming. This review provides valuable insights into FL for potential applications and areas needing further investigation in SF.

In today’s world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium’s behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs.