The Smart Mobility vision calls for dynamic resource and service discovery to cope with the intrinsic topology volatility of Internet of Things (IoT) platforms without sacrificing the required business continuity and service flexibility. For an extended automation of collaboration within and across enterprise boundaries, trust management is equally important, granting security, reliability and scalability at the same time. To tackle the above challenges, this paper proposes the integration of a semantic-based service management layer in an IoT infrastructure grounded on the Hyperledger Sawtooth blockchain. Every service in the outlined framework is annotated with reference to a domain ontology, so that smart contracts can exploit knowledge representation and non-standard reasoning for service registration, discovery, outcomes explanation and service selection. A case study on power management of Plug-in Electric Vehicles (PEVs) is proposed to clarify the benefits of the proposal. Early performance evaluation results support the feasibility and sustainability of the approach.

• Five-star framework to assess On-Device AI in Internet of Everything scenarios • Mafalda 2.0 federated incremental learning model with semantic explainability • Evaluation of Mafalda 2.0 against state-of-the-art On-Device AI approaches • Concrete examples of global and local explainability and federated learning As the Internet of Things (IoT) evolves into an Internet of Everything (IoE), adapting Artificial Intelligence (AI) and Machine Learning (ML) approaches to pervasive computing devices is not enough. Collaborative intelligence is required, calling for on-device AI frameworks combining adequate accuracy and computational efficiency levels with incremental learning on continuous data streams, federated learning in distributed architectures and symbolic explainability formalisms to foster trustworthiness with interpretable trained models and comprehensible prediction outcomes. To fill this gap, the paper introduces a five-star rating for on-device AI based on the Semantic Web of Everything (SWoE) paradigm, and presents the five-star Mafalda 2.0 framework. It combines statistical data processing with Knowledge Graph technologies for information representation and automated reasoning to support: semi-automatic or fully data-driven ontology definition; on-device training to generate highly interpretable semantics-based models; prediction framed as a semantic matchmaking problem, exploiting non-standard reasoning services endowed with logic-based justifications to provide comprehensible results as well as counterfactual and contrastive explanations. An experimental campaign on four publicly available datasets has been carried out to validate the efficiency and accuracy of the proposal, along with federated learning and explainability examples.

• Automated analysis methods for semantic reasoners in Mobile Edge Computing. • On an Android device, 6 reasoners analyzed on correctness, time and memory usage. • Correctness, time, memory and energy footprint of 3 reasoners tested on a single-board computer. • Hardware-based and software-based energy profilers have been used and compared. • Multiplatform automated benchmarking tool for semantic reasoners upgraded. The integration of Semantic Web technologies into Mobile Edge Computing (MEC) platforms is enhancing the capabilities of real-time, context-aware applications across diverse domains. MEC brings processing closer to the network edge, reducing latency and allowing for the improvement of data privacy, while Semantic Web technologies provide machine-interpretable knowledge representation and reasoning capabilities. Despite their potential, deploying semantic reasoners on edge devices is challenging due to their resource-intensive nature, which requires significant memory availability, computational power, and energy. Furthermore, correctness, performance and energy consumption are simultaneously important, as MEC semantics-based applications often call for real-time queries for autonomous agent decision or user-oriented decision support. This paper presents an extensive experimental evaluation of Web Ontology Language (OWL) reasoners deployed in MEC environments, assessing correctness, processing time, memory usage, and energy consumption across both a reference tablet and a single-board computer. For energy measurement, both software profiling and hardware monitoring have been exploited and compared. The study is supported by a modular, cross-platform benchmarking framework that automates data collection and ensures reproducibility. The findings highlight the trade-offs between reasoning capabilities and resource consumption, offering valuable insights for refining testing methodologies as well as optimizing semantic reasoners in MEC settings.

As an application and service platform, the World Wide Web spans from simple informational websites to rich social media and Software-as-a-Service (SaaS) clients. While innovative capabilities are increasingly provided by Deep Learning (DL) Artificial Intelligence (AI) architectures such as pre-trained trans-formers, so far Web applications and services have integrated them only via cloud-based implementations. Deep-Learning-as-a-Service (DLaaS) is establishing itself for professional and personal use, with prevalent business models including pay-per-use and monthly subscriptions. With growing concerns over data privacy, response latency, and service costs, executing DL inference directly within the user's browser appears as a com-pelling alternative to cloud-based solutions. This paper introduces local intelligence via Browser Extension for Real- Time applications (liBERTa), a modular browser extension-based architecture for real-time client-side DL inference. By operating entirely within the browser, liBERTa reduces reliance on external servers. Its modular design consists of independent layers for data extraction, model inference, and results presentation, granting flexibility and adaptability across different kinds of applications and services. Experimental results from a case study on website privacy policy classification demonstrate the feasibility of the approach, showing that lightweight transformer models can achieve competitive accuracy while maintaining inference times suitable for real-world use on commodity hardware.

The evolution of metering technologies has enabled the collection of a vast amount of energy consumption data, offering new opportunities for more efficient energy management. While utility providers increasingly leverage machine learning and data visualization to simplify and optimize data analysis, current systems often present barriers in understanding collected data. This paper introduces a novel multi-agent architecture that integrates Large Language Models (LLMs) to enhance the interpretability of smart meter data. Developed within the Digital Enterprise initiative by Lutech S.p.A., the proposed framework enables natural language interactions and the generation of energy-related reports. An early evaluation has been conducted through a series of basic interaction tests, demonstrating the feasibility of the approach and its potential to improve data-driven decision-making.

Hyperautomation aims to digitize end-to-end business processes, but most of the current solutions and platforms still rely on pre-defined workflows to carry out complex tasks. However, business process automation can greatly benefit from recent technological innovations at the intersection of Large Language Models (LLMs) and Multi-Agent systems (MAS). In this paper, we present an Agentic Artificial Intelligence (AI) framework where a central LLM-driven orchestrator dynamically plans and delegates tasks to specialized LLM agents, which in turn exploit tools to act on business platforms and other external systems. A case study based on document management illustrates how the approach is able to deal with complex requests involving source file parsing and report generation, leveraging an approach based on Retrieval Augmented Generation (RAG) to enable knowledge sharing among specialized agents. Finally, the proposed framework introduces a practical blueprint for scalable, explainable Agentic AI in enterprise hyperautomation environments.

Serverless computing enables greater flexibility and efficiency in the cloud-to-edge continuum. Artificial Intelligence and Machine Learning (AI/ML) applications benefit greatly from this paradigm, as they need to gather, preprocess, aggregate and analyze data at various scales. In such contexts, the increasing hardware/software resource availability of Internet of Things (IoT) devices provides the opportunity to exploit them not only as data sources in AI/ML infrastructures, but also as computational nodes for model training and inference; nevertheless, comprehensive frameworks are still mostly missing. This work introduces an innovative serverless computing architecture which expands the cloud-to-edge continuum toward IoT devices. The same functions can run on IoT, edge and cloud nodes with minimal to no code modification and they can be invoked through a uniform interface. A federated learning framework is defined based on the proposed architecture, exploiting an existing IoT-oriented ML algorithm in a novel way. Notably, IoT nodes are used for both federated training and local inference tasks. A full prototype implementation has been built with off-the-shelf technologies and devices. A case study on federated machine learning for activity recognition and experiments have been conducted to validate key elements of the proposal.

Mobile reasoners play a pivotal role in the so-called Semantic Web of Things. While several tools exist for the Android platform, iOS has been neglected so far. This is due to architectural differences and unavailability of OWL manipulation libraries, which make porting existing engines harder. This paper presents Mini-ME Swift, the first Description Logics reasoner for iOS. It implements standard (Subsumption, Satisfiability, Classification, Consistency) and non-standard (Abduction, Contraction, Covering, Difference) inferences in an OWL 2 fragment. Peculiarities are discussed and performance results are presented, comparing Mini-ME Swift with other state-of-the-art OWL reasoners.

Vehicular ad-hoc networks (VANETs) are a challenging Internet of Things scenario. While research is proposing increasingly sophisticated hardware and software solutions for on-board context detection, probably high-level context information sharing has not been adequately addressed so far. This paper proposes a novel logic-based framework enabling a contextual data management and mining in VANETs. It grounds on a knowledge fusion algorithm based on nonstandard, nonmonotonic inference services in Description Logics, adopting standard Semantic Web languages. Ontology-referred context annotations produced by individual VANET nodes are merged with automatic reconciliation of inconsistencies. An efficient information dissemination protocol complements the proposal. The approach has been implemented in a vehicular network simulator and early experimental results proved its effectiveness and feasibility.

A new mobile healthcare system for neuro-cognitive function monitoring and treatment is presented. The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering, and in-cloud distribution to specialized medical personnel. As such, it presents tradeoffs typical of other cyber-physical systems, where hardware, algorithms, and software implementations have to come together in a coherent fashion. The system is based on spatio-temporal detection and characterization of a specific brain potential called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects ( n = 12 healthy, n = 3 mildly cognitive impaired, and n = 2 with Alzheimer's disease involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s), yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.

Supply chains can be seen as cyber-physical networks grounded on object identification and tracking. Conventional trust models featuring centralized information management architectures and simplistic things classification lend two of the most relevant limitations to current solutions. Blockchain introduces novel and a valuable trust approaches while semantic technologies better permit a things description. This paper introduces a semantic-enhanced blockchain platform allowing a flexible object discovery. It is based on validation by consensus of smart contracts and adopt a semantic matchmaking between queries and object annotations expressed w.r.t. ontology models. Early experiments assess the good behaviour of the proposed framework.

The progress of building automation devices and networks is curbed by application models with limited device autonomy, static configuration scenarios, and frequent explicit user interaction. This paper presents a novel approach inspired to social network interactions for increased object self-configuration and self-orchestration in home and building automation. Objects become autonomous social agents, interacting and coordinating automatically as new information about the environment is available. The framework is grounded on a service-oriented architecture for encapsulating, exchanging, and composing device capabilities and on semantic-based service description, discovery and aggregation exploiting nonstandard inference services. The proposed approach was implemented in two different testbeds and experimental evaluations of feasibility and performance were carried out with respect to a home automation case study.

The Semantic Web and Internet of Things visions are converging toward the so-called Semantic Web of Things (SWoT). It aims to enable smart semantic-enabled applications and services in ubiquitous contexts. Due to architectural and performance issues, it is currently impractical to use existing Semantic Web reasoners. They are resource consuming and are basically optimized for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper presents Mini-ME, a novel mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios are presented and an experimental performance evaluation is provided both on a PC testbed (against other popular Semantic Web reasoners) and on a smartphone.

Home and building automation (HBA) trends toward the Ambient Intelligence paradigm, which aims to autonomously coordinate and control appliances and subsystems in a given environment. Nevertheless, HBA is based on an explicit user-home interaction and basically enables static and predetermined scenarios. This paper proposes a more flexible multi-agent approach, leveraging semantic-based resource discovery and orchestration for HBA applications. Backward-compatible enhancements to EIB/KNX domotic standard allow to support the semantic characterization of user profiles and device functionalities, thus enabling: 1) negotiation of the most suitable home services/functionalities according to implicit and explicit user needs and 2) device-driven interaction for adapting the environment to context evolution. A power-management problem in HBA is presented as a case study to better clarify the proposal and assess its effectiveness.

In semantic-based pervasive computing, annotated information is tied to micro-devices, such as RFID tags and wireless sensors, deployed in an environment. Compression techniques are essential, because of the verbosity of standard XMLbased languages for ontologies and semantic annotations. Beyond compression ratio, query efficiency is a key aspect. This paper presents a framework for querying knowledge bases expressed in OWL, serialized in RDF/XML syntax and compressed with a homomorphic encoding, in order to allow query evaluation without requiring decompression. Formalization of a significant set of queries demonstrates feasibility of the approach, while practical examples highlight its usefulness.

Current technologies for Home and Building Automation (HBA) basically require an explicit interaction with the user and allow a static set of operational scenarios defined during system implementation. On the contrary, novel HBA solutions should enable so-called ambient intelligence, deriving from a flexible and automatic control of appliances and subsystems dipped into an environment. To this aim, this paper proposes backward-compatible enhancements to one of the most widespread domotic standards, i.e., EIB/KNX ISO/IEC 14543-3, able to support advanced, knowledge-based and context-aware functionalities, grounded on the semantic annotation of both user profiles and device capabilities. Such an approach enables novel resource discovery, matchmaking, and decision support features in HBA. Main benefits are in: (i) determining the most suitable services/functionalities according to implicit and explicit user needs and (ii) allowing device-driven interaction for autonomous adaptation of the environment to context modification. A case study is presented to better clarify the proposed framework also highlighting main characteristics, while performance evaluation is provided to assess its effectiveness.

Reasoning engines are largely used in resource discovery and matchmaking scenarios where, given a request, they are able to provide a list of compatible items arranged in relevance order. A significant added value is the possibility to explain match

A ubiquitous Knowledge Base (u-KB) is a distributed and decentralized knowledge base where the factual knowledge (i.e. individuals) is scattered among objects disseminated within the environment with no centralized coordination. This paper presents an extended framework to enable u-KBs in mobile scenarios where a semantic discovery is carried out using metadata stored in RFIDs without fixed repositories. A dissemination protocol allows an on-demand retrieval of suitable descriptions directly from tags located on the objects. Such a vision allows to build a truly pervasive environment where autonomous objects compose a self-organized evolving discovery architecture suitable for u-Commerce purposes.

The paper focuses on an implementation of concept abduction with an Object-oriented Database Management System (OODBMS). OWL-DL Knowledge Bases have been translated to an OO version to enable standard and non-standard inference services as queries over a DB suitable for handheld devices. The framework has been implemented and tested: early experiments are reported.

In this paper we propose an extended version of the open source jUDDI implementation by the Apache Software Foundation, adapted to pervasive RFID contexts. The registry \nadopts an OWL-S 1.1 Pro¯le instance annotation of mobile services and resources. Ontology-based metadata are \nexploited in order to perform a semantic-based service discovery w.r.t. a given request. The proposed framework \nhas been devised for pervasive RFID-based logistics environments. A case study is presented along with experimental results on a prototype implementation.

In a generic semantic-based matchmaking process, given a request, it is desirable to obtain a ranked list of compatible services/resources/profiles in order of relevance. Furthermore, a match explanation can provide useful information to modify or refine the original request in a principled way. Though the feasibility of such an approach has been proved with fixed reasoning engines, it is a challenging subject to perform inference tasks on handheld devices. Here we propose abduction and contraction algorithms in Description Logics specifically devised for applications in mobile environments. A simple interaction paradigm based on Bluetooth protocol stack has also been implemented and tested in a mobile dating case study.

We propose a novel object discovery framework integrating the application layer of Bluetooth and RFID standards. The approach is motivated and illustrated in an innovative u-commerce setting. Given a request, it allows an advanced discovery process, exploiting semantically annotated descriptions of goods available in the u-marketplace. The RFID data exchange protocol and the Bluetooth service discovery protocol have been modified and enhanced to enable support for such semantic annotation of products. Modifications to the standards have been conceived to be backward compatible, thus allowing the smooth coexistence of the legacy discovery and/or identification features. Also noteworthy is the introduction of a dedicated compression tool to reduce storage/transmission problems due to the verbosity of XML-based semantic languages.

We present a novel resource discovery framework for u-commerce. Both the original RFID data exchange protocol and the Bluetooth Service Discovery Protocol have been extended in order to enable support for the semantic annotation of products and goods. Given a request, the approach we propose allows an enhanced discovery process exploiting the semantics of resource descriptions available in a u-marketplace. The enhancement is backward compatible with both the original discovery protocols, thus allowing the smooth coexistence of the resource discovery and/or identification approaches. We present and motivate our approach in an innovative u-commerce framework, and show its benefits.