As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems.

Indoor temperature forecasting in large public buildings is affected by heating, ventilation, and air-conditioning (HVAC) control, occupancy, airflow, and zone use, not only by recent temperature histories. This study asks whether operationally derived functional-zone proxy labels—room groupings derived heuristically from HVAC statistics rather than from field-verified building metadata—can provide a useful learnable context for temporal convolutional network (TCN)-based room-level forecasting when verified semantic metadata are unavailable. Using the public BEAR (Building Efficiency and Renewables) multizone building HVAC dataset, we construct a reproducible operational proxy scenario: eighty rooms are grouped from occupancy, airflow, damper, control-command, and temperature-variability statistics, and Niagara-inspired point-naming conventions are used only as structured metadata descriptors, not as tags exported from a deployed system. The proposed SemanticTCN augments a TCN backbone with learnable room, temperature-variability proxy embeddings. Experiments compare persistence, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), TCN, DLinear, PatchTST, a TCNOnly variant, SemanticTCN, and ablations for 1 h, 6 h, and 24 h forecasts, evaluated by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination ($R^2$). Persistence is strongest at 1 h and 24 h, while DLinear is strongest at 6 h, indicating strong thermal inertia and daily regularity. Within the TCN family, SemanticTCN improves over TCNOnly in the locked run, shows a statistically significant within-distribution MAE improvement across multiple seeds (most consistently at 6 h), and reduces large-error rates; however, a leave-one-zone-out experiment shows that this advantage does not transfer to rooms unseen during training, and a five-seed operational-versus-random-proxy control shows that the specific operational grouping rule is not statistically distinguishable from arbitrary grouping. The bounded conclusion is that providing room/group context to the TCN backbone helps predict rooms whose identity was observed during training, especially at 6 h, but the gain reflects room-identity context rather than generalizable operational semantics: the proxy labels are heuristic operational constructs rather than verified building semantics; they do not outperform arbitrary grouping under multi-seed control, and they do not generalize to unseen rooms under this split.