Achieving a balance between interoperability and security in blockchain systems has led to the development of various integration mechanisms. These include the use of specialized smart contracts to facilitate cross-chain interactions, enabling reliable connections and secure transfers of information and assets. While technical interoperability is effectively addressed at lower layers, achieving semantic interoperability at the application layer remains a significant challenge. This paper proposes a mechanism to address this challenge by leveraging a model-driven approach. Metamodels, models, and transformations are created, and smart contracts are defined abstractly and then semi-automatically generated for specific blockchain platforms. The proposed approach was validated by generating contracts between Ethereum and Hyperledger, enabling semantically compatible transactions. The mechanism was further evaluated using the Technology Acceptance Model with expert participants. This evaluation demonstrated its effectiveness in specifying and transforming contracts and fostering semantic interoperability across blockchains.

The Internet of Things enhances the quality of life by automating tasks and streamlining human-device interactions. However, manual device management remains time-consuming, especially in multiple or new environments that demand new settings and interactions. Learning systems aid in automating task management, but their learning times hinder personalization and struggle when the system has to interact with multiple IoT environments, impacting user experience. This paper aims to optimize knowledge sharing for IoT environments, proposing a framework that utilizes recommender systems to find optimal and reusable configurations among IoT environments and users. To that end, this work leverages teacher-student relationships in Knowledge Distillation, facilitating knowledge sharing and enhancing knowledge reuse in learning models. In addition, real-time processing eliminates training time. This approach achieves a remarkable 93.15% accuracy.

The exaggerated use of smartphones and growing informatization of the environment allows modeling people’s behavior as a process, namely, a social workflow, where both individual actions and interactions with other people are captured. This modelling includes actions that are part of an individual’s routine, as well as less frequent events. Although infrequent actions may provide relevant information, it is routine behaviors that characterize users. However, the extraction of this knowledge is not simple. Current process mining techniques face problems when analyzing large amounts of traces generated by many users. When very different behavioral patterns are integrated, the resulting social workflow does not clearly depict their behavior, either individually or as a group. Proposals based on frequent pattern mining aim to distinguish traces that characterize frequent behaviors from the rest. However, tools that allow grouping/filtering of users with a common behavior pattern are needed beforehand, to analyze each of these groups separately. This study presents the so-called federated process mining and an associated tool, SOWCompact, based on this concept. Its potential is validated through the case study called activities of daily living (ADL). Using federated process mining, along with current process mining techniques, more compact processes using only the social workflow’s most relevant information are obtained, while allowing (event enabling) the analysis of these social workflows.

We introduce our solution developed for data privacy, and specifically for cognitive security that can be enforced and guaranteed using blockchain technology in SAAL (Smart Ambient Assisted Living) environments. Personal clinical and demographic information segments to various levels that assures that it can only be rebuilt at the interested and authorized parties and no profiling can be extracted from the blockchain itself. Using our proposal the access to a patient's clinical process resists tampering and ransomware attacks that have recently plagued the HIS (Hospital Information Systems) in various countries. The core of the blockchain model assures non-repudiation possible by any of the involved information producers thus maintaining ledger fidelity of the enclosed historical process information. One important side effect of this data infrastructure is that it can be accessed in open form, for research purposes for instance, since no individual re-identification or group profiling is possible by any means.