Building automation is a representative AIoT-driven cyber-physical scenario, where intelligent systems interact with physical devices to manage lighting, climate, and appliances in real time. Traditional machine learning struggles with ambiguous, multilingual, and colloquial user inputs, limiting effectiveness in dynamic building environments. Recent advances in large language models (LLMs) enable more natural command interpretation, but high resource demands challenge their sustainable deployment on edge nodes. This paper proposes a serverless architecture based on event-driven microservices and container orchestration that dynamically manages the deployment, execution, and scaling of compact, fine-tuned LLMs across distributed edge nodes for building automation. We fine-tune compact LLMs with ambiguous and colloquial command examples to enhance robustness and enable context-aware deployment at the edge. Platform elasticity, enabled by Knative, allows rapid model adaptation without persistent resource allocation. We evaluate the system on a multilingual building automation dataset (Chinese, English, French) with ambiguous and colloquial commands, using an automated framework to assess interpretation and execution. Results show that the fine-tuned model outperforms the baseline Qwen-2.5-14B on five of six metrics and performs comparably on output format compliance. It also generalizes well across languages, although fuzzy instructions remain challenging.

The structural change toward the digital transformation of online sales elevates the importance of parallel processing techniques in recommender systems, particularly in the pandemic and post-pandemic era. Matrix factorization (MF) is a popular and scalable approach in collaborative filtering (CF) to predict user preferences in recommender systems. Researchers apply Stochastic Gradient Descent (SGD) as one of the most famous optimization techniques for MF. Paralleling SGD methods help address big data challenges due to the wide range of products and the sparsity in user ratings. However, these methods’ convergence rate and accuracy are affected by the dependency between the user and item latent factors, specifically in large-scale problems. Besides, the performance is sensitive to the applied learning rates. This article proposes a new parallel method to remove dependencies and boost speed-up by using fractional calculus to improve accuracy and convergence rate. We also apply adaptive learning rates to enhance the performance of our proposed method. The proposed method is based on Compute Unified Device Architecture (CUDA) platform. We evaluate the performance of our proposed method using real-world data and compare the results with the close baselines. The results show that our method can obtain high accuracy and convergence rate in addition to high parallelism.

The density estimation of diverse sensor types in a heterogeneous sensor network is a useful and challenging service that can be applied in clustering schemes, node redeployment, and sleep mode scheduling. Energy efficiency is one of the main requirements for any wireless sensor network service. Besides, the service has to provide a fresh version of the estimation to each node. Network dynamics, especially node mobility, introduce new challenges. Moreover, churn makes the problem even more complicated. In this paper we introduce a gossip-based approach for the density estimation of sensor diversity in clustered dynamic networks. The devised method supports node mobility and churn, as well as redeployment of new nodes. It is fully distributed and adaptive to network dynamics. We analyze the effect of mobility as well as scalability in the number of clusters and the quantity of nodes. Our algorithm has a fast convergence speed and provides more accurate estimation compared to similar approaches.