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.
