The increasing computational capabilities of IoT end devices push the deployment of application logic tasks directly on the extreme edge rather than the cloud or edge nodes. However, there are still unresolved issues on the Over-The-Air (OTA) software update operations for IoT end devices: (i) the hardware heterogeneity in IoT settings requires custom code for each different device type; (ii) the growing complexity of microcontroller code couples the development of high-level processing tasks with low-level operations; (iii) efficient methods for updating target IoT devices in a specific geographical area are absent. To address these issues, we propose an OTA firmware update framework that utilizes WebAssembly (WASM) and incorporates location-aware features. We split the application logic in WASM from the rest of the firmware written in native code, in order to create a greater separation of concerns. WASM's platform independence creates an abstraction layer for the underlying hardware, allowing the same application logic to be deployed virtually to any IoT device. We integrate a location-aware extension of the MQTT protocol in our framework to enable software updates targeting devices in specific geographical areas. Finally, our experiments demonstrate that location awareness does not add significant overhead to the system and that the performance of WASM in a microcontroller is comparable to native code and superior to Micropython.

Effective motor rehabilitation relies on correct exercise execution and timely feedback. Automatically assessing exercise quality remains a key challenge for remote rehabilitation systems. Although numerous approaches have been explored, each comes with its own set of limitations: camera-based systems can be costly and raise privacy concerns, while purely Inertial Measurement Unit (IMU)-based approaches often struggle with sensor drift and precise quality assessment. This paper investigates the feasibility of real-time exercise quality assessment using only simple wearable IMU sensors coupled with on-device edge intelligence. We propose a distributed, data-driven framework where Transformer-based Deep Learning (DL) models are deployed directly onto wearable IoT device placed on the patient joints when performing exercises. Each device processes local IMU data for initial exercise recognition and angle estimation. The system then consolidates these distributed inferences achieving a consensus on the performed exercise and to evaluate movement quality against physiotherapist-defined thresholds. Experimental results demonstrate high accuracy in exercise identification (97% F1-Score) and the capability for precise joint angle estimation (the MAE varied between 0.16 to 0.31 depending of the exercised performed).

Disinformation is one of the most insidious challenges of our times, and there is an increasing need for sophisticated systems that can deliver truthful content to users. Traditional centralized approach require the trust on a single entity, which often is subject to bias and cannot keep up the pace to the rate at which fake information is produced. Recent proposals aim to leverage the online communities in the process of fact-checking using blockchain-based systems, which can provide the traceability, immutability and transparency of interactions. In this paper, we propose SocialTrustr, a blockchain-based system aimed at ensuring the traceability and validity of content shared within social environments. SocialTrustr is designed to encourage online honesty by rewarding users who publish truthful content and perform honest validations, through a public consensus mechanism based on reputation. We implement our system, release it open source and evaluate it in a real deployment by simulating online user behavior. Additionally, we provide guidelines for future steps aiming to extend the platform to a multi-chain scenario.

Mobile Crowd Sensing (MCS) leverages the widespread availability of smart devices to collect and analyze environmental and social data. While MCS has been widely applied in smart cities to optimize vehicle traffic and safety, the needs of pedestrians remain largely unaddressed. To address this need, we propose CrossTime, a sensing application designed to estimate waiting times at pedestrian crossings. Using GPS data, accelerometer readings, and open-source intersection location data, CrossTime autonomously detects when a user is waiting at an intersection. To evaluate its feasibility and accuracy, we conducted a test case on three routes in an urban environment, comparing system-detected waiting times with manually recorded values. Our results show that CrossTime effectively captures pedestrian waiting behavior, although there are some discrepancies due to sensor limitations and environmental factors.

The rapid expansion of the Internet of Things (IoT) has led to significant data reliability and system transparency challenges, aggravated by the centralized nature of existing IoT architectures. This centralization often results in siloed data ecosystems, where interoperability issues and opaque data handling practices compromise both the utility and trustworthiness of IoT applications. To address these issues, we introduce ZONIA (Zero-trust Oracle Network for IoT Applications), a novel blockchain oracle system designed to enhance data integrity and decentralization in IoT environments. Unlike traditional approaches that rely on Trusted Execution Environments and centralized data sources, ZONIA utilizes a decentralized, zero-trust model that allows for anonymous participation and integrates multiple data sources to ensure fairness and reliability. This paper outlines ZONIA’s architecture, which supports semantic and geospatial queries, details its data reliability mechanisms, and presents a comprehensive evaluation demonstrating its scalability and resilience against data falsification and collusion attacks. Both analytical and experimental results demonstrate ZONIA’s scalability, showcasing its feasibility to handle an increasing number of nodes in the system under different system conditions and workloads. Furthermore, the implemented reputation mechanism significantly enhances data accuracy, maintaining high reliability even when 40% of nodes exhibit malicious behavior.

The proliferation of non-interoperable smart objects within the Internet of Things (IoT) has led to a fragmented and heterogeneous landscape. The Web of Things (WoT), particularly the W3C WoT, has emerged as a promising solution to this challenge, enabling seamless integration across IoT platforms and domains by extending known web standards. This paper introduces Zion, an open-source scalable W3C Thing Description Directory (TDD) designed to efficiently address the indexing and querying of Thing Descriptions (TDs) and the associated Web Things (WTs). Zion offers a standard API for performing CRUDL operations while supporting metadata querying through JSONPath. We further demonstrate its practical utility through real-world deployments, applying Zion to Structural Health Monitoring (SHM) and integrating it with IoT devices alongside blockchain technology. Comparative analysis with the other TDD implementations complying with the W3C standards – e.g., WoT Hive and TinyIoT – demonstrates that Zion outperforms both. It exhibits response times approximately ten times lower than those observed in the compared TDDs under high workloads.

Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habits. Thus, there is a need for innovative solutions that not only improve water utilization but also raise residents' awareness about this issue. This paper presents a promising solution leveraging the Internet of Things (IoT) and Machine Learning (ML) techniques to detect water wastage stemming from sink usage automatically. We have designed and developed a low-cost prototype equipped with an array of sensors, including a microphone, an ultrasonic sensor, and a PIR, to monitor sink usage. A deep learning model based on Gated Recurrent Units (GRU) has been trained to classify the wastage events. To validate our concept, we have gathered a small dataset relative to nine common daily water usage activities through the IoT prototype. Our preliminary findings demonstrate the feasibility of our solution, with an average accuracy exceeding 90% in detecting wastage events.

In edge-cloud IoT scenarios, proactive caching strategies constitute an effective solution to optimize the use of resources while ensuring adequate Age of Information (Aol). However, the implementation of these strategies introduces significant privacy constraints, primarily stemming from the transmission of sensitive data to the cloud. To address such issue, Federated Learning (FL) has emerged as a promising approach which processes data at the edge, transmitting only the model updates to the cloud. This paper introduces CACHUUM (Cache Architecture for Cloud and Heterogeneous edge in the ContinUUM), a proactive and privacy-aware architecture designed to facilitate the deployment of various edge caching strategies within distributed edge environments. Our architecture supports three families of strategies: local, global and federated, each tailored to meet specific privacy requirements. Furthermore, our architecture is continuum-aware, accommodating different data caching locations, whether it be at the edge node, in the cloud, or somewhere in between. We demonstrate the effectiveness of CACHUUM on simulated IoT environments, by collecting metrics on forecast accuracy, caching precision and data overhead, for different strategies. The latter anticipate the optimal cache update timings for each IoT device, ensuring that Aol aligns with application requirements upon data request.

Train model identification can enhance the structural monitoring of railway infrastructures by providing contextual information about train passages. While approaches relying on timetables are impractical due to delays, camera-based solutions present challenges related to deployment costs and privacy concerns. In this paper, we propose RATTLE, a self-contained framework for train tracking and identification based on audio signal fingerprinting. We have developed a prototype IoT system tailored for train tracking and ground truth assessment, enabling the acquisition of a real-world dataset spanning four months of measurements. Then, we conducted a comparative analysis of several traditional Machine Learning (ML) and Deep Learning (DL) algorithms for audio features classification, mel spectrogram classification, and image classification (serving as baselines). Our findings highlight that mel-trained CNN algorithms achieve high accuracy (97%) comparable to the best video-based DL solution, while substantially reducing model size. Furthermore, we explored the potential for migrating the classification task to the edge through quantisation techniques.

The Cloud-to-Things (C2T) continuum combines the proximity of edge infrastructure to the devices with cloud resources to optimize data processing and response time in the Internet of Things (IoT). Proactive edge caching is a potential solution for meeting latency constraints in C2T environments, enabling efficient data processing and storage while reducing redundant computation and cost. However, while 5G/6G infrastructural aspects and caching strategies are extensively studied, no frameworks facilitate the design and deployment of caching strategies or address IoT’s unique requirements. This paper proposes CACHE-IT, a proactive edge caching framework that decouples the caching strategy algorithm from the underlying architecture, enabling customization based on application-specific requirements. Through extensive simulations, we analyzed the impact of different configurations on system metrics and verified that the CACHE-IT positively impacts the system latency and hit rate. By implementing a scenario-specific caching strategy, we illustrate the CACHE-IT deployment in a real-world Structure Health Monitoring (SHM) system. The evaluation demonstrates that CACHE-IT impacts positively in terms of latency, hit rate, and the number of requests sent to data providers.

Assessing the integrity of industrial and civil appliances has become a priority worldwide. Noteworthy, this goal requires a strong synergy between multiple tools, disciplines, and approaches to be attained via a joint hardware-software co-design of the different Structural Health Monitoring (SHM) system components. This work proposes the$\sf{MAC4PRO}$architecture, a sensor-to-cloud monitoring platform that seamlessly integrates sensing and software technologies for accurate data measurement, transmission, and analysis. The developed solution stands out for its interoperability and versatility, making it a promising candidate for integration in the next generation of smart structures. Our platform was validated during extensive experimental campaigns targeted at various industrial scenarios. The results show that the$\sf{MAC4PRO}$architecture can identify subtle changes, such as 1mm size leakage events in pipeline circuits, or less than 1% frequency drifts in civil buildings after seismic excitation, while ensuring more than 90% reduction in the edge-to-cloud data transfer process.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages the pervasive presence of mobile devices to collect and analyze data from the environment. However, the choice of a push- or pull-based architecture for MCS can result in a loss of flexibility and limitations for the creators of the campaigns (crowdsourcers). To address this issue, we propose a hybrid push-pull architecture for MCS campaigns that leverages the W3C Web of Things (WoT) to standardize the interfaces and interactions of devices through well-consolidated Web technologies. Furthermore, we present the design and implementation of a WoT-enabled Android application for MCS. We evaluate our proposal through simulations in a vehicular scenario based on a real dataset, showing that the hybrid architecture provides greater flexibility to crowdsourcers, supporting simultaneously the push and pull paradigms.

The Internet of Things is getting momentum and generating new demands over infrastructure, systems, and platforms. One of the main aspects that hamper the large-scale development of IoT-based systems is the lack of interoperability. IoT Platforms aim to solve this issue by providing a uniform interface to access data from heterogeneous sources. However, integrating new protocols and applications can impose additional overhead, hindering the platform’s overall performance and scalability. This study provides an insight into the trade-off between interoperability and performance of IoT platforms. First, we present a qualitative analysis of three open-source platforms - FIWARE, ThingsBoard, and Konker - analyzing their interoperability features. Second, we conduct a performance evaluation emulating two IoT-based environments – smart cities and smart health – to understand each platform’s scalability, response time, and computer resource usage. Finally, we analyze the possible trade-offs between interoperability features and scalability based on the qualitative and quantitative analysis. The results show that interoperability features do not have a direct impact on the performance of the platform.

Condition Monitoring is one of the most critical applications of the Internet of Things (IoT) within the context of Industry 4.0. Current deployments typically present interoperability and management issues, requiring human intervention along the engineering process of the systems; in addition, the fragmentation of the IoT landscape, and the adoption of poor architectural solutions often make it difficult to integrate third-party devices in a seamless way. In this paper, we tackle these issues by proposing a tool-driven architecture that supports heterogeneous sensor management through well-established interoperability solutions for the IoT domain, i.e. the Eclipse Arrowhead framework and the recent Web of Things (WoT) standard released by the W3C working group. We deploy the architecture in a real Structural Health Monitoring (SHM) scenario, which validates each developed tool and demonstrates the increased automation derived from their combined usage.

The Internet of Things (IoT) is nowadays affected by significant interoperability issues. One of the most popular countermeasures is the Web of Things (WoT), proposed recently in a consistent standardization effort. On the other hand, several IoT-oriented frameworks are already established in industrial scenarios and provide SOA-like features such as discovery and orchestration. In this paper, we study how to bridge these two worlds by proposing a tool that enables a two-way translation between a WoT ecosystem and a System-of-Systems composed of well-described Web services. We evaluate the efficiency and scalability of our solution over the Eclipse Arrowhead framework through a series of experiments that assess the scalability of our solution under realistic workloads.

The chaotic growth of the Internet of Things (IoT) determined a fragmented landscape with a huge number of devices, technologies and platforms available on the market, and consequential issues of interoperability on many system deployments. The recent W3C Web of Things (WoT) standards aimed to ease the deployment of heterogeneous systems by introducing uniform and well-defined software interfaces among the systems’ components. Although the WoT reference architecture is generic and agnostic to the target devices, its widespread adoption depends on the availability of specific tools named Servients, which enable the run-time operations of WoT applications. In this paper we aim at contributing to the adoption of the W3C WoT standards by presenting WoT Micro-Servient (WMS), a framework for bringing the WoT paradigm to the extreme edge of an IoT environment. Through WMS, developers can design, compile and install WoT applications on micro-controllers and embedded systems with constrained hardware capabilities. We describe the architecture and functionalities of the tool, and demonstrate its effectiveness in terms of reduced latency and energy consumption compared to the state-of-art proxy-based solution enabled by Node-wot, i.e. the official implementation of W3C WoT. Finally, we discuss a real-world application related to smart home, where WMS is used to enable a WoT-based remote monitoring and control of indoor plants, by enabling seamless integration between micro-controllers and mobile devices.

The rapid and exponential growth of the Internet of Things (IoT) has been generating a new breed of technologies that introduce several different protocols and interfaces. The Web of Things (WoT) architecture stands out as an emerging and potential solution to improve interoperability across IoT platforms by describing well-defined software interfaces. However, few studies analyze and compare WoT to other interoperability solutions proposed in the IoT literature. In this paper, we attempt to bridge the gap by three main contributions. First, we qualitative compare the WoT approach with the well-known FIWARE-based interoperability solution.Second, based on the previous analysis, we design and implement a connector to bridge the WoT architecture to the FIWARE ecosystem. Third, we conduct a performance analysis emulating a real IoT-based environment to understand scalability, response time, and computer resource usage of the two interoperability solutions. The results reveal that conceptual design choices impact the applications’ performance: the WoT architecture effectively enables interoperability across IoT Platforms, though it incorporates several characteristics that hinder the implementation of applications. On the other hand, the FIWARE IoT Agent solution is platform-specific. Hence new implementations are needed for each different IoT data model.

The concepts of Software Defined Networks (SDN) and Network Function Virtualization (NFV) have promoted network chaining, or Service Function Chain (SFC), quickly and simply. In dynamic infrastructure scenarios, the management of SFC introduces challenges both for the connection of these elements and for understanding their behavior when automatic elasticity is required. Currently, most existing approaches have addressed this challenge with probabilistic heuristics or AI-based techniques, considering only static management. This paper presents an approach for profiling SFC that can be used for predictive NFV orchestration purposes. We conducted a performance evaluation study based on testbed experimentation and queueing modeling. Our results confirm that an analytical model can be used for managing SFC orchestration, not only as a validation technique for NFV, but also as a heuristic for predictive resource allocation in production environments.

The Internet of Things (IoT) is getting momentum, which drives us to design solutions able to deal with huge amounts of data coming from different sorts of sensors in order to make decisions to adapt system behavior automatically. While in recent years many IoT-based reasoning systems have already been proposed, there are no comprehensive results reporting their performance, particularly in complex environments. As an answer to that challenge, developers often choose an architecture design based on previous experience that have an impact on the system performance and scalability. This paper shows experimental results of a performance analysis study of different implementations of context-aware management architectures for IoT-based smart cities. Results show that different architectural choices affect system scalability and that automatic real time decision-making is feasible in an environment composed of dozens of thousands of sensors continuously transmitting data.

The advent of the Internet of Things (IoT) and its innumerous applications for Smart Cities emphasizes the need for context-aware systems able to adapt behavior automatically to instant environment conditions. Currently, there is a gap in terms of understanding how context information is interrelated, as well as tracking which events occurred under which conditions within certain context scopes. Visualizing, specifying, tracking and monitoring typical contexts involved in IoT-based applications are still challenging activities since context modeling remains a low level process that requires much human expertise. In this paper we propose a new context life cycle that involves context design and context tracking. We developed a context-aware management framework, where contexts are modeled as graphs and can be explicitly designed and their occurrences can be tracked down. We believe that this feature of designing and tracking context graphs may help developers, administrators, and end-users in harnessing the wealth of information generated by highly scalable IoT systems.