smart building system integrators

Abstract: By exploiting the spatial selectivity of wireless channels, physical layer security (PLS) protects confidential wireless transmissions. Flexible reflectors (FRs), which are passive metallic plates mounted on movable platforms, offer a low-cost and low-complexity solution for reshaping the propagation environment. However, their potential for secrecy-oriented signal propagation shaping has not been explored. In this paper, we study an FR-aided secure communication over non-line-of-sight links and jointly optimize the positions and rotations of FRs to maximize the secrecy rate under geometric reflection and mutual-blocking constraints. For the single-FR case, we propose a Fibonacci segmentation search (FSS) method, which partitions the feasible region into multiple unimodal intervals and obtains the optimal solution with Fibonacci search over the unimodal intervals. To reduce the computational overhead, we also propose a low-complexity cross-entropy search (CES) method, which employs Gaussian mixture model-based adaptive importance sampling to localize promising search intervals before deterministic refinement. After deriving the blocked placement interval induced by the deployed FRs and integrating it into a sequential deployment scheme, we extend the FSS and CES methods to the multi-FR case. Simulation results show that our proposed FSS method achieves significant secrecy rate performance gains over various benchmark schemes, and our proposed CES method achieves a favorable runtime-performance tradeoff. These results indicate that FRs provide a low-cost and reconfigurable mechanism for PLS.

Abstract: This work investigates how site-specific digital twins (DTs) can support wireless localization using simplified distributed infrastructure. We propose PRISM (Probabilistic Refinement using Inverse learning and a Site-specific Model), a DT-guided probabilistic localization scheme with single-antenna access points (APs). Localization is formulated as a probabilistic inverse problem in which a conditional proposal distribution over UE locations is learned from multi-node channel impulse responses (CIRs). The proposal generates candidate locations, with its spread regularized during training and DT consistency used to reweight candidates and refine the estimate at inference. The DT serves as an approximate forward-consistency model, avoiding assumptions of exact channel fidelity or differentiability with respect to user position. Numerical experiments demonstrate gains over data-driven and classical fingerprinting benchmarks, while achieving a median error of approximately $0.7$ m using 10 single-antenna APs. Without retraining, PRISM remains effective under degraded inference-time DT features. PRISM also yields uncertainty estimates from its proposal covariance, with empirical $χ^2$ calibration approaching ideal behavior as APs increase.

Abstract: Multi-zone HVAC control is a shared-resource problem: zones on a floor share one fan and cooling coil, so each zone's demand affects its neighbours and the building's peak power. One controller per zone scales well and isolates faults, but each controller then observes only its own zone, not the shared air loop. In this work, we present a decentralised controller that assigns each zone an independent active-inference agent, which plans by minimising expected free energy and learns its zone dynamics online. The agents exchange no messages, so none depends on peers or a coordinator; each accounts for the unobserved coupling through a latent disturbance and a congestion signal derived from the metered power of the other floors. Fifteen agents are evaluated in EnergyPlus on a Sinergym office building against three rule-based controllers and three MADDPG fleets, one given the same floor meters. On five noisy weather years unseen during training, the fleet attains the lowest mean building peak (88.3 kW, 3 to 17 % below the baselines) and staggers the floor peaks better than any baseline, with a coincidence factor (building peak over the sum of the floor peaks) of 0.929 versus 0.936 to 0.994. Compared with a reactive rule at similar comfort (0.912 versus 0.916), it lowers the sum of monthly peaks, on which demand charges are billed, by 33 kW (4 %) in every year, at the cost of about 10 % more energy. It costs 9% less per year than the proactive rule (at lower comfort) and 1% less than the MADDPG fleet given the same floor meters, in every test year. The whole fleet runs on a single low-cost Raspberry Pi 5, closing the observation-to-action loop over MQTT in 442 ms at p95, far below the 15-minute control step.

Abstract: The telecommunications industry is shifting toward an experience-driven era defined by immersive applications that impose stringent network demands. While passive optical network (PON)-based Fiber-To-The-Home (FTTH) architectures have historically delivered high-capacity broadband, a growing performance gap between access and indoor networks necessitates driving fiber deeper into premises. Complementing existing copper and WiFi technologies, Fiber-to-the-Room (FTTR) is emerging as a critical enabler. This article examines the drivers of this architectural shift, specifically focusing on collaborative extended reality (XR). We analyze the multi-modal traffic characteristics and Quality-of-Experience (QoE) requirements of XR, alongside the resulting network performance implications. To address these demands, we propose a novel resource allocation framework integrating coarse-grained traffic forecasting with intelligent in-premise roaming. This framework facilitates the flexible deployment of artificial intelligence models tailored to the computational capabilities of the FTTH, FTTR, and WiFi segments. Numerical evaluations of a representative multi-tier network demonstrate that long-horizon, multi-step-ahead traffic forecasts significantly reduce inference frequency. This approach enables coordinated resource allocation and network-orchestrated seamless mobility across rooms, effectively satisfying collaborative XR QoE requirements. Finally, we highlight strategies to amortize FTTR capital expenditure by extending the optical infrastructure's role beyond basic connectivity. By supporting diverse services over a unified physical layer, FTTR enhances infrastructure utilization, unlocks novel revenue streams, and maximizes the value of in-premise fiber deployments.

Abstract: In this paper, we tackle the problem of jointly estimating the system states and partially unknown dynamics within distributed sensor-equipped networks, particularly in scenarios where only partial state observations are available. To address this issue, we propose an observer-based dynamic cooperative learning framework incorporating online distributed Gaussian Process (GP) regression, which enables accurate estimation despite incomplete in measurements and deficient GP models. In addition, a novel data collection strategy is introduced, with theoretical conditions ensuring feasible data acquisition. Moreover, we also derive an error upper bound encompassing state estimation and model estimation, leveraging the deterministic error bounds of GPs. Empirical simulations demonstrate the superiority of our approach compared to existing distributed GP-based methods.