Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2510.15708

Sugar Shack 4.0: Practical Demonstration of an IIoT-Based Event-Driven Automation System

Thomas Bernard·François Grondin·Jean-Michel Lavoie
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper presents a practical alternative to programmable-logic-controller-centric automation by implementing an event-driven architecture built with industrial Internet of Things tools. A layered design on a local edge server (i) abstracts actuators, (ii) enforces mutual exclusion of shared physical resources through an interlock with priority queueing, (iii) composes deterministic singular operations, and (iv) orchestrates complete workflows as state machines in Node-RED, with communication over MQTT. The device layer uses low-cost ESP32-based gateways to interface sensors and actuators, while all automation logic is offloaded to the server side. As part of a larger project involving the first scientifically-documented integration of Industry 4.0 technologies in a maple syrup boiling center, this work demonstrates the deployment of the proposed system as a case-study. Evaluation over an entire production season shows median message time of flight around one tenth of a second, command issuance-to-motion latencies of about two to three seconds, and command completion near six seconds dominated by actuator mechanics; operation runtimes span tens of seconds to minutes. These results indicate that network and orchestration overheads are negligible relative to process dynamics, enabling modular, distributed control without compromising determinism or fault isolation. The approach reduces material and integration effort, supports portable containerized deployment, and naturally enables an edge/cloud split in which persistence and analytics are offloaded while automation remains at the edge.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thomas Bernard, François Grondin, Jean-Michel Lavoie. 2025-10-17. Sugar Shack 4.0: Practical Demonstration of an IIoT-Based Event-Driven Automation System. https://arxiv.org/abs/2510.15708

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Structured Stochastic Representations of Integrated Dynamic Strategies

Dynamic allocation decisions couple present resource use to evolving internal conditions, delayed returns, and future costs. We represent this interaction by four probability localizations linked through regime-indexed, graph-constrained column-stochastic operators. Pre-action state or context selects a locally affine model, while action-dependent changes update subsequent regimes, yielding a causal switched representation of nonlinear evolution. We characterize operator identifiability relative to the graph, the stochastic constraints, and the sampled embedding, separating coefficient recovery from predictive equivalence on the decision domain. Decision making is then formulated through implementable return--cost acceptability regions. Finite-horizon error propagation supplies conservative classification margins, and simultaneous intervals distinguish model-relative near-optimality from certified $\epsilon$-optimality over a declared finite policy class. Regime-indexed stochastic feedback is admitted when it satisfies the same certification test. Reproducible synthetic laboratories for personal preparation, supplier participation, and customer retention illustrate exact, operator-supplied, and noisy feedback cases. Multinomial experiments show improving recovery of the feedback function and fewer unresolved decisions with increasing sample size, while unrestricted off-policy recovery remains limited. The contribution is a structure-preserving representation--identification--decision workflow, not a domain-specific physiological or commercial calibration.

eess.SY↗
arXiv2026

Fusion Estimation in Multi-sensor Systems for Data Packets with Disrupted Identities

In this paper, we explore the problem of fusion estimation for a multi-sensor system where the identity of the data packet received by each sensor may be disrupted or incorrect due to confusion in device identity allocation, communication protocol defects, or the lack of a clear sensor identifier. This can result in a random shuffle of the data components during the fusion estimation process, compromising the performance of the fusion estimation. To address this issue, we introduce the concepts of permutations and symmetry groups to describe this phenomenon as data packet permutation. We construct statistics to simplify the information set, developing two algorithms: a Bayesian approach, which performs fusion using posterior arrangement probabilities, and a greedy approach, which effectively improves estimation performance by guessing the likely data arrangement. We compare these two algorithms and demonstrate that both are expectation error-bounded. We improve algorithms for information-scarce scenarios. By employing the expectation-maximization algorithm, we fill in the prior information of data arrangement where the correct convergence is proven. Finally, we present numerical simulations to validate our results.

eess.SY↗
arXiv2026

Quantifying the Reality Gap for RL-Based UAV Placement at mmWave and Sub-THz

Reinforcement learning (RL) policies for unmanned aerial vehicle (UAV) placement in mmWave and sub-terahertz networks are typically trained on simplified analytical channels. We quantify the resulting sim-to-real gap on a real urban map of Doha, Qatar, at carriers {28, 140, 183, 300} GHz and altitudes {50, 75, 100, 125} m, evaluating three channel pipelines: an analytical model (FSPL + atmospheric absorption + cuboid LoS), full Monte-Carlo ray tracing in Sionna RT with ITU-R P.676-13 absorption, and a deterministic-LoS hybrid that reuses Sionna's mesh under a closed-form path-gain expression. We formalize the gap on the spatial SNR distribution via four metrics, namely bias, RMSE, Jensen-Shannon divergence, and optimum-deployment displacement. Three findings emerge: at 28/140 GHz, $\sim$70% of the apparent -5.6/-4.8 dB Sionna bias is Monte-Carlo undersampling and shrinks to -1.7/-1.5 dB after mitigation; at 183 GHz a -9.2 dB residual isolates the atmospheric absorption / ITU-R P.676 line-shape disagreement; at 300 GHz the stochastic ray tracer agrees with the analytical model only coincidentally, with a +3.8 dB structural offset exposed by the deterministic-LoS pipeline. Across all carriers the linear-domain regret of the analytical-trained policy stays $\geq$ 0.93, indicating practical near-optimality but with a carrier-resolved SNR bias that warrants explicit reporting.

eess.SY↗