Tushar Gupta and Shirin Bhambhani, Senior Member, IEEE, USA
The exponential growth of the Internet of Things (IoT) has introduced significant challenges in managing scalability, availability, and efficiency. With billions of interconnected devices generating vast amounts of data, traditional frameworks struggle to handle the complex requirements of modern IoT applications. Addressing these challenges is crucial to fully leverage the potential of IoT in various domains, including smart homes, healthcare, and industrial automation. This paper proposes a novel framework for integrating IoT applications with cloud environments to achieve scalability and high availability. The framework is structured into four layers: the device layer leverages Message Queuing Telemetry Transport (MQTT) as a low overhead communication protocol;the edge gateway layer aggregates data using lightweight Kubernetes; the ingestion and cloud infrastructure layer employs Kafka and Apache Spark for ingesting data and data transformation; and the data processing and analytics layer utilizes the cloud infrastructure layer to send the data to databases for data visualization. By leveraging cloud environments, this solution enhances scalability, availability, and overall system robustness. This paper also explores the challenges involved in implementing such an architecture, and provides insights into future advancements in IoT cloud integration.
Internet of Things (IoT), IoT architecture, Cloud Computing.
Tessa E Andersen, Ayanna Marie Avalos, Gaby G. Dagher, and Min Long, Department of Computer Science, Boise State University, Brigham Young University, California State University, Fresno
Large Language Models (LLMs) have seen an increased use across various applications throughout the world, providing more accurate and reliable models for Artificial Intelligence (AI) systems. While powerful, LLMs do not always produce accurate or up-to-date information. Retrieval Augmented Generation (RAG) is one of the solutions that has emerged to help LLMs to give more recent and accurate responses. While RAG has been successful in reducing hallucinations within LLMs, it remains susceptible to inaccurate and maliciously manipulated data. In this paper, we present Distributed-RAG (D-RAG), a novel blockchain-based framework designed to increase the integrity of the RAG system. D-RAG addresses the risks of malicious data by replacing RAG’s traditionally centralized database with communities, each consisting of a database and a permissioned blockchain. The communities are based on different subjects, each containing experts in the field who verify data through a privacy-preserving consensus protocol before it is added to the database. A Retrieval Blockchain is also designed to communicate between the multiple communities. The miners on this Retrieval Blockchain are responsible for retrieving documents from the database for each query and ranking them using an LLM. These rankings are agreed upon, with the top documents being provided to the LLM with the query to generate a response. D-RAG increases the integrity and security of RAG incorporated LLMs.
Blockchain, RAG, LLM, Privacy-Preserving.
Valentin Colliard, Alain Peres, and Vincent Corruble,Sorbonne Universit´e, CNRS, LIP6 Thales LAS France
In this paper, we introduce two deep reinforcement learning approaches designed to tackle the challenges of air defense systems. StarCraft II has been used as a game environment to create attack scenarios where the agent must learn to defend its assets and points of interest against aerial units. Our agent, by estimating a value for each weapon-target pair, shows an ability to be robust in multiple scenarios to truly distinguish and prioritize targets in order to protect its units. These two methods, one using multi- layer perceptrons and the other using the attention mechanism, are compared with rule-based algorithms. Through empirical evaluation, we validate their efficacy in achieving resilient defense trategies across diverse and dynamic environments.
Deep Reinforcement Learning, Weapon-Target Assignment, Simulation
Tessa E Andersen, Ayanna Marie Avalos, Gaby G. Dagher, and Min Long, Department of Computer Science, Boise State University, Brigham Young University, California State University, Fresno
Large Language Models (LLMs) have seen an increased use across various applications throughout the world, providing more accurate and reliable models for Artificial Intelligence (AI) systems. While powerful, LLMs do not always produce accurate or up-to-date information. Retrieval Augmented Generation (RAG) is one of the solutions that has emerged to help LLMs to give more recent and accurate responses. While RAG has been successful in reducing hallucinations within LLMs, it remains susceptible to inaccurate and maliciously manipulated data. In this paper, we present Distributed-RAG (D-RAG), a novel blockchain-based framework designed to increase the integrity of the RAG system. D-RAG addresses the risks of malicious data by replacing RAG’s traditionally centralized database with communities, each consisting of a database and a permissioned blockchain. The communities are based on different subjects, each containing experts in the field who verify data through a privacy-preserving consensus protocol before it is added to the database. A Retrieval Blockchain is also designed to communicate between the multiple communities. The miners on this Retrieval Blockchain are responsible for retrieving documents from the database for each query and ranking them using an LLM. These rankings are agreed upon, with the top documents being provided to the LLM with the query to generate a response. D-RAG increases the integrity and security of RAG incorporated LLMs.
Blockchain, RAG, LLM, Privacy-Preserving.
Dirk Friedenberger, Lukas Pirl, Arne Boockmeyer, and Andreas Polze,Hasso-Plattner Institute University of Potsdam,Potsdam, Germany
Model-based systems engineering can be one of the key enablers for developing increasingly complex systems. Although the topic is actively developed in academia and industry, a holistic approach that is based on openly available tools and that considers all phases of the development life cycle is yet to be established. Addressing this, we propose a new approach in ontology-based systems engineering. We introduce micro models for modeling and use transformation to simplify the usability of the models. The micro models are loosely coupled, domain-specific, and result in an overall composite model. This decomposition of the overall model reduces complexity, offers reusability of models, and allows fast itera- tions when designing systems. Using the transformations, assets, such as source code, documentation, or simulations, can be created from the models. The prospects for automation can support agile development processes. To ensure accessibility, we have based our work on open source resources only. As an evaluation, we have used our approach to verify, develop, test, and simulate the Train Dispatcher in the Cloud (ZLiC), a cloud-based approach to digitalize the German Zugleitbetrieb. The prototype is being used to derive the requirements for a productive system.
Model-based systems engineering, ontology-based systems engineering, railway.
