Author

Alistair Barros

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Open access Sep 2026

A semiotics-informed conceptual maturity model for industrial IoT integration

This study aims to develop a descriptive maturity model for evaluating the integration of IIoT technologies within enterprise environments. Although IIoT enables data-driven and automated operations, integration remains complex because of technological and organisational interdependencies. Existing models lack conceptual consistency and provide limited insight into operational integration. The study therefore proposes a theory-informed maturity model, specifically focused on IIoT integration into business operations. The study uses a maturity model development methodology guided by design science research. It draws on literature-based insights to develop the initial model components and applies a theoretical foundation to interpret and structure the model dimensions. The model was then evaluated and refined through two design cycles involving expert interviews. The maturity model comprises four dimensions: physical object, data, process and business service, organised and interpreted using the Semiotic Ladder and five maturity levels: pre-pilot, pilot, operational, organisation-scale and ecosystem-scale. It also identifies the key capabilities required to effectively integrate IIoT into business operations. This study develops a theory-informed maturity model based on the semiotic ladder of organisational semiotics, offering a novel semiotics-based perspective for conceptualising and assessing IIoT maturity. It explicitly focuses on the integration of IIoT into business operations, emphasising the key dimensions required to support this integration.

Himashi Sandamini Widana Kankanamge, Alireza Nili, K. Desouza et al. · Journal of Enterprise Inform... · 0 citations

Conference Jul 2026

A Causal-Driven Hierarchical Decentralised Federated Learning Framework for Resilient Load Forecasting in Distributed Microgrids

Modern microgrids require distributed intelligence and edge computing to handle variable demand and renewable generation, but heterogeneity, communication limits, and privacy hinder centralised forecasting. This paper proposes a causally guided hierarchical decentralised federated learning (H-DFL) framework for resilient short-term load forecasting, integrating a hybrid TCN–BiLSTM with MCMC-based probabilistic causal feature selection. A three-tier architecture enables local training and hierarchical aggregation without raw data sharing, improving scalability and communication efficiency through sparse, interpretable feature selection driven by key factors such as solar and weather dynamics. Experiments on the Ausgrid dataset show improved stability and efficiency over Granger Causality (GC), Dynamic Causal Modeling (DCM), and Markov chain Monte Carlo (MCMC) baselines, with intervention tests confirming robustness under solar, demand, and outage disturbances. Overall, the results demonstrate that combining probabilistic feature sparsity with hierarchical decentralised federated learning to enable scalable, privacy-preserving, and resilient load forecasting for future microgrid systems.

M. Mahi, R. Naha, Alistair Barros · International Conference on... · 0 citations