Digital Twins (DTs) are the digital equivalent of physical entities that facilitate, among others, monitoring and decision-making, thus helping extend the longevity of the twinned entity. DTs with automated decision-making capabilities require explainable inference mechanisms, especially in the case of critical infrastructures such as water networks. Here we introduce 3K, a DT framework that aims for knowledge-enriched inference that is explainable and fast, by synthesizing knowledge representation (semantics) and knowledge discovery methods. 3K constructs a knowledge graph, which is becoming a mainstream way of metadata storage in DTs, and proposes a new method that can run on both sensor data and knowledge graphs to learn semantic association rules. The rules represent the expected working conditions of the DT and we argue that when combined with domain knowledge in the form of ontological axioms, semantic association rules can help perform various downstream tasks in DTs, including extending the longevity of the twinned entities such as an Internet of Things (IoT) system. Furthermore, we demonstrate the 3K framework in a water distribution network use case and show how it can be used for downstream tasks.

Evolving application programming interfaces (APIs) in Internet of Things (IoT) devices cause system failures and challenge the longevity and reliability of IoT systems. To mitigate these issues, this paper aims to categorize IoT API changes and assess their impact on deployed systems. We introduce a detailed taxonomy that classifies API changes into 26 characteristics within five key dimensions: Data Payload Modifications, Communication Protocol Modifications, API Endpoint Modifications, Security Modifications, and Parameter Modifications. We apply this taxonomy by annotating and analyzing a comprehensive dataset developed through an empirical study of change reports and integration documentation from long-running open-source IoT integration platforms. We analyze the frequency and characteristics of evolving IoT APIs and the effects of changes within IoT device integration scenarios. Results reveal that 74.21% of all API changes cause backward incompatibility, with Data Payload and API Endpoint Modifications being the most prevalent types, and that cloud-based platform integrations are most vulnerable to changes that threaten system longevity. This work contributes to IoT system design by providing a comprehensive taxonomy of IoT API changes, facilitating the development of more robust and adaptable IoT architectures that can withstand long-term operational changes and enhance system reliability.

Scripts, automation and interoperability of Smart Homes are brittle and break for all kinds of reasons. We propose and evaluate the Web of Things Proxy Layer (WoT-PL). This semantic middleware solution deploys digital twins for each device so that automation can function independently of the physical devices. We use Large Language Models (LLMs) to semantically translate various IoT device data into standardised Web of Things (WoT) Thing Descriptions; this approach unifies previously heterogeneous data structures and proprietary data types. Our experiments suggest that while LLMs seem promising in device data translation, consistent accuracy is challenging to achieve. The proposed WoT-PL framework aims to improve the adaptability and longevity of smart home systems.