Urban scenarios present various concerns to micromobility users such as cyclists, which are an important part of the mobility infrastructure. As smarter cities increasingly focus on enhancing citizen well-being and infrastructure efficiency, navigation systems have primarily been designed for car drivers, with a focus on traffic conditions, while disregarding metrics that are important for micromobility users. As a matter of fact, they tend to privilege other aspects of their journey that enhance its comfort, rather than the mere path length. To address this gap, this paper presents a holistic architectural framework for micromobility-oriented navigation systems, incorporating nominal, data-driven and crowdsensed metrics in the loop. The paper presents the definition of metrics and a real world case study for each category of metrics, providing a real implementation. We demonstrate the effectiveness of our approach in a controlled environment before implementing and deploying the complete system in a real-world city, where we provide a detailed analysis of the results.

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.

In industrial environments, most production-related activities performed by human operators are often complex. Accurate detections of these activities are pivotal as it can greatly help to assess productivity that can lead to improvement in worker training, as well as in other scenarios ensure a safe work environment and reducing injuries. Existing datasets on wearable Internet of Things (IoT) for human activity recognition primarily focuses on general activities, such as walking, running, etc., and therefore, related machine learning models and datasets are not suitable for application to industrial environments. In this paper, we present a novel dataset for classifying human operator activities in a meat processing plant where production line operators use knives to cut, process and produce meat products. Our dataset contains human operator activity data captured using wearable IoT sensors collected from a meat processing production facility. Through extensive experiments using machine and deep learning, we demonstrate that our dataset is effective and useful for detecting different activities of a human operator working in an industrial environment. To the best of our knowledge, this is the only real-world IoT dataset that will be made publicly available to support further research into industrial activities recognition. Our dataset and related experiments are available at https://digitalinnovationlab.github.io/mppdataset.

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 Crowdsensing (MCS) has recently taken up an important role in sensor data collection paradigms because of its reduced costs and flexibility. It allows crowdsourcers to recruit a number of mobile users to execute sensing tasks in an area without deploying physical sensors. However, MCS algorithms and policies are very different depending on the application and testing them in the real world is impractical, due to the difficulties in recruiting large crowds of volunteers. For this purpose, the research is mostly oriented to simulations, however, to date, there is no simulation platform that focuses enough on different aspects of MCS, often disregarding some in favor of others. In this paper we focus on TACSim, an extensible simulator and we propose an additional mobility module that fills the gap of accurate road network representation. Participants navigate a real road network offering an improved realism and yielding more accurate results. We also propose a caching system that helps in reducing the processing time of simulations and demonstrate its effectiveness through extensive benchmarks.

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.

Internet of Things (IoT) sensor data, which capture time series physical measurements such as temperature and humidity, often lack proper classification. This limits their effective understanding, integration, and reuse. While sensor metadata—textual descriptions of the measurements—is sometimes available, it is frequently incomplete or ambiguous. As a result, classification often depends solely on the time series data. Leveraging both time series sensor readings and textual metadata for automated and accurate classification remains a challenge due to the heterogeneity and inconsistency of these data sources. In this paper, we propose DeepMetaIoT, a multimodal deep learning framework that integrates time series and textual data for classification. DeepMetaIoT employs a cross-residual architecture comprising a time series encoder and a text encoder based on a pre-trained large language model, enabling effective fusion of both modalities. Experimental results on real-world IoT sensor datasets show that DeepMetaIoT consistently outperforms state-of-the-art machine learning and deep learning baselines.

Engineering tools support the process of creating, operating, maintaining, and evolving systems throughout their lifecycle. Toolchains are sequences of tools that build on each others’ output during this procedure. The complete chain of tools itself may not even be recognized by the humans who utilize them, people may just recognize the right tool being used at the right place in time. Modern engineering processes, however, do not value such ad-hoc choice of tooling, because of their uncontrolled nature. Building upon the Extended Automation Engineering Model defined by the IEC 81346 standard, this paper proposes to automate the toolchain building and execution process for Cyber-Physical System of Systems (CPSoS), utilizing key principles of the Eclipse Arrowhead framework. The proposed toolchain automation solution addresses issues such as tool interoperability, interaction, automation, and dynamic choreography. The feasibility of this set of integrated concepts is validated through an Arrowhead-based toolchain choreography demonstration.Note to Practitioners—The paper discusses approaches to the automated execution of various industry-related processes. As the processes are becoming more complex and involve numerous systems which have to be orchestrated, a simple and preprogrammed workflow is not enough anymore. Therefore, building on top of the principles of the Eclipse Arrowhead framework, an adequate model of toolchains, allowing for their automated execution, is proposed. Different approaches to supervision of toolchain execution are discussed showing the benefits of reaching higher automation levels. Further, four adoption levels are introduced, which are a measure of the toolchain automation progress. Finally, a simplified demonstrator is shown and steps to elevate it to higher adoption levels are highlighted. To ensure that the approach is industry-oriented, several examples of how the proposed methodology can be used in the industrial context are discussed.

Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.

Road surface quality is a major concern for bicycle riders and plays an important role in the mobility infrastructure. In the era where smarter cities aim to increase the well-being of citizens and the efficiency of infrastructures, navigation systems relying on Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for road traffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of an MCS-based navigation system for bicycle riders that accounts for the road surface quality. The MCS paradigm leverages the sensor data produced by the personal devices of participating citizens to describe phenomena of common interest. Our system classifies road segments using inertial sensor data gathered by users, using a combination of supervised and unsupervised methods, as human labeling in this context is impractical and too subjective. We prove the efficacy of our method in a controlled environment, and then we implement and deploy the full system in a real city, finally reporting on its results.

In recent years, there has been a growth in the development of numerous software algorithms dedicated to pedometers (or step counters). This surge has subsequently spurred the creation of various context-aware smartphone applications for sports, healthcare, and other fields. Most works that compare commercial offerings do not adopt a sound and rigorous method, as human testers are asked to stick to a defined set of constraints, and experiments are carried out within controlled environments. However, each application is still tested separately, with no guarantee that the conditions are really the same, plus these conditions cannot resemble the real environment where pedometers are going to be used. Our proposal features a software solution that records the sensor readings of human testers and inject the exact same sensor values into different pedometer applications to produce a sound result by using the same testing conditions. We implement our solution and perform with it a comparison study.

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.

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario.

Human Activity Recognition (HAR) using wearable Internet of Things (IoT) devices represents a well investigated researched field encompassing various application domains. Many current approaches rely on cloud-based methodologies for gathering data from diverse users, resulting in the creation of extensive training datasets. Although this strategy facilitates the application of powerful Machine Learning (ML) techniques, it raises significant privacy concerns, which can become particularly severe given the sensitivity of HAR data. Moreover, the labeling process can be extremely time-consuming and even more challenging for IoT wearable devices due to the absence of efficient input systems. In this paper, we address both aforementioned challenges by designing, implementing, and validating edge-based Human Activity Recognition (HAR) systems that operate on resource-constrained IoT devices, which relies on the utilization of Self-Organizing Maps (SOM) for activity detection. We incorporate a feature selection process before training to reduce data dimensionality and, consequently, the SOM size, aligning with the resource limitations of wearable IoT devices. Additionally, we explore the application of Federated Learning (FL) techniques for HAR tasks, enabling new users to leverage SOM models trained by others on their respective datasets. Our federated Extreme Edge (EE)-aware HAR system is implemented on a wearable IoT device and rigorously tested against state-of-the-art and experimental datasets. The results demonstrate that our C++-based SOM implementation achieves a consistent reduction in model size compared to state-of-the-art approaches. Furthermore, our findings highlight the effectiveness of the FL-based approach in overcoming personalized training challenges, particularly in onboarding scenarios.

The recognition of the activity of texting while driving is an open problem in literature and it is crucial for the security within the scope of automotive. This can bring to life new insurance policies and increase the overall safety on the roads. Many works in literature leverage smartphone sensors for this purpose, however it is shown that these methods take a considerable amount of time to perform a recognition with sufficient confidence. In this paper we propose to leverage the smartphone front camera to perform an image classification and recognize whether the subject is seated in the driver position or in the passenger position. We first applied standalone Convolutional Neural Networks with poor results, then we focused on object detection-based algorithms to detect the presence and the position of discriminant objects (i.e. the security belts and the car win-dow). We then applied the model over short videos by classifying frame by frame until reaching a satisfactory confidence. Results show that we are able to reach around 90 % accuracy in only few seconds of the video, demonstrating the applicability of our method in the real world.

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.

Wearable Internet of Things (IoT) devices with inertial sensors can enable personalized and fine-grained Human Activity Recognition (HAR). While activity classification on the Extreme Edge (EE) can reduce latency and maximize user privacy, it must tackle the unique challenges posed by the constrained environment. Indeed, Deep Learning (DL) techniques may not be applicable, and data processing can become burdensome due to the lack of input systems. In this paper, we address those issues by proposing, implementing, and validating an EE-aware HAR system. Our system incorporates a feature selection mechanism to reduce the data dimensionality in input, and an unsupervised feature separation and classification technique based on Self-Organizing Maps (SOMs). We developed the system on an M5Stack IoT prototype board and implemented a new SOM library for the Arduino SDK. Experimental results on two HAR datasets show that our proposed solution is able to overcome other unsupervised approaches and achieve performance close to state-of-art DL techniques while generating a model small enough to fit the limited memory capabilities of EE devices.

Public Internet of Things (IoT) platforms, such as Thingspeak, significantly increased the availability of open IoT data and enabled faster and cheaper development of novel IoT applications by reducing or even eliminating the need for deploying their own IoT sensors and platforms. However, open IoT data is often heterogeneous, sparse, fuzzy, and lacks accurate description (which we refer to as IoT metadata). These limitations make open IoT data challenging to integrate and use, and prevent the efficient development of IoT applications. In fact, while several sensor data description models have been proposed and standardized, open IoT data currently lack or include only partial metadata description. Therefore, novel techniques for automatically annotating open IoT data are needed to fully unleash the power of open IoT. This article proposes a novel metadata-assisted cascading ensemble classification framework (MACE) for the automatic annotation of IoT data. MACE is capable of sequentially combining standalone classifiers, enabling it to cope with heterogeneous IoT data and different domains of information (e.g., numerical and textual), which have not been considered previously. MACE incorporates a novel ensemble approach for automatically selecting, sorting, filtering, and assembling classifiers in a way that improves annotation performance. This article presents extensive experimental evaluations of MACE using public IoT data sets. Results demonstrate that the MACE framework significantly outperforms existing solutions for open IoT data by as much as 10% in classification accuracy.

Mobile Crowdsensing (MCS) is a paradigm involving a crowd of participants, called workers, into sensor data gathering campaigns through their personal devices. Some campaigns require workers to contribute with small amounts of geolocalized data at a constant rate, while being not directly aware of the global conditions of the system. In the scope of this reduced awareness, it is crucial to consider the privacy preservation of single workers at design time, as the disclosure of their exact location may lead to severe privacy issues. In this paper we design a privacy by design MCS framework that leverages variable rewards for workers willing to submit their location with an higher precision than others. Privacy is ensured through a negotiation phase that estimates the reward of the workers for different levels of location precision. This way, it helps them decide autonomously the spatial granularity of their data in order to preserve their privacy, yet obtaining a reward for their data. We design a metric based on k-anonymity to evaluate the level of privacy achieved, and validate the proposed framework over a real dataset. Our results show the efficacy of the framework as well as interesting effects caused by the topology of the environment.

Mobile Crowdsensing has become an important paradigm in the last decade for on-demand monitoring scenarios in Smart Cities and vehicular networks, when the deployment of a dedicated sensor network is no longer affordable. To foster the participation of a large user base, it is common to reward them on top of the amount and the quality of data provided. Regardless of the MCS policy adopted, this requires the crowdsourcer to keep track of the participants. Since the contributed data inherently carries sensitive spatio-temporal information, privacy problems arise if a malicious entity gains access to it; still, in some cases, the spatio-temporal precision is crucial for the benefit of the application and cannot be distorted. In this paper we propose a privacy preserving framework for opportunistic MCS scenarios that includes data collection and rewarding phases. The framework both retains the precision of spatio-temporal information and limits the sensitivity of information disclosed through an algorithm that clusters the data points into low correlated sets. The framework is agnostic about how correlation is calculated, and we propose three exemplary correlation functions. We evaluate our framework against six real world datasets, assessing its efficacy and envisioning its implementation in practical deployments.

Interoperable and secure data management techniques are fundamental for most of large-scale Structural Health Monitoring (SHM) systems. Indeed, given the relevance of SHM critical measurements, data integrity must be protected against tampering or falsifications. In this paper, we propose a four-layer SHM architecture that allows to build an effective data pipeline from sensors to consumer applications, passing through the cloud. The architecture is built on top of the MODRON platform and exploits the recent advances of the W3C Web of Things (WoT) standard for interoperability. We then discuss how third-party services can take benefit of the W3C WoT architecture to retrieve the SHM critical data and to publish them on the Ethereum Blockchain through an SHM-specific Smart Contract, for data protection and traceability purposes. We test the effectiveness of the Smart Contract implementation in terms of latency and costs under simulated workloads.

The Internet of Things is permeating our everyday life and the number of sensors and actuators around us is increasing at an exponential pace. Data generated by such heterogeneous devices is hard to organize, therefore, in pervasive scenarios like Smart Cities, there is an increasing need for service infrastructures that play the role of intermediary between citizen and things. Often, end users call for customized services that are tailored to their specific need rather than general-purpose ones. For this reason, in this paper we propose a service architecture based on End-User Service Composition (EUSC), through which individuals can aggregate primary sources of data to compose services. Furthermore, we investigate the requirements for service reusability and inherently leverage a hierarchical paradigm by introducing a specific class of composition languages. Finally, we show our Proof-of-Concept (PoC) middleware implementation, namely SenSquare, to show how this is achievable in a real deployment through visual programming, specifically illustrating how hierarchization is achieved.

Digital Twins are crucial in Industry 4.0 IoT scenarios, as they replicate physical assets and enable important tasks such as predictive analytics, what-if scenarios and real time monitoring. The heterogeneity of IoT use cases usually makes the development of digital twins extremely application-specific as well as prone to interoperability issues. To overcome these two challenges, we propose WoTwins, a framework that, on one side, leverages the W3C Web of Things (WoT) standard to model data and entities, and, on the other side, generates automatically Digital Twins of existing Web Things by modeling their state space through a Markov Decision Process (MDP) graph and by predicting its behavior though Machine Learning techniques. We conduct experiments on a simulated use cases related to IoT robotics to evaluate our proposal.

Nowadays, several Internet of Things (IoT) deployments use publish-subscribe paradigms to disseminate IoT data to a pool of interested consumers. At the moment, the most widespread standard for such scenarios is MQTT. We also register an increasing interest in IoT-enabled Location-Based Services, where data must be disseminated over a target area and its spatial relevance and the current positions of the consumers must be taken into account. Unfortunately, the MQTT protocol does not support location awareness, and hence it may result in notifying consumers that are geographically far from the data source, causing increased network overhead and poor Quality of Service (QoS). We address the issue by proposing LA-MQTT , an extension to standard MQTT supporting spatial-aware publish-subscribe communications on IoT scenarios. LA-MQTT is broker-agnostic and fully backward compatible with standard MQTT. As monitoring the position of subscribers over time may cause privacy concerns, LA-MQTT carefully supports location privacy preservation, for which the optimal tradeoff with the QoS of the spatial notifications is addressed via a learning-based algorithm. We demonstrate the effectiveness of LA-MQTT by experimentally evaluating its features via large-scale hybrid simulations, including real and virtual components. Finally, we provide a Proof of Concept real implementation of an LA-MQTT scenario.

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.

Sensory data generated around us in the context of IoT is huge and heterogeneous. To fully unleash the potential of IoT Open Data there is a need for service infrastructures that facilitate the interaction of users with such data, especially when they are able to customize such services to fit their needs. In this paper we propose to use our tool SenSquare for IoT End-User Service Composition, by presenting its main features and its recent advances towards providing a data historian and importing other services, as well as evaluating the performance of its implementation in parallel.

The fourth industrial revolution (Industry 4.0) elevates the complexity and autonomy of industrial systems and engineering environments to levels not seen before. The novel challenges involve not only the software running on the partaking autonomous devices, but also architectural considerations and the technological infrastructure around the entire engineering process. In this paper, complementing the trends in industrial systems design, we propose an approach to toolchain modeling, i.e. an integrated specification for the interoperability of tools along with the holistic architectural framework, designed in the context of the Arrowhead Framework. In particular, we propose an intuitive, yet founded definition for toolchains and their mappings to a versatile engineering process model. Those definitions then serve as a basis for proposing our comprehensive toolchain modeling approach. The methodology is demonstrated using (simplified) real-world engineering case studies based on the Arrowhead Framework and platform.

Industry 4.0 outlines the trend of the massively adoption of Internet of Things (IoT) nodes in supply chains, manufacturing, and factories in general. The industry digitalization is the key enabler to ease the productive process, drastically reduce its costs, and boost up the associated business. In this context, Arrowhead Tools (AHT) is a H2020 EU project provided by ECSEL that targets automation and digitalization solutions for the industry in Europe. AT is based on a framework, named Arrowhead Framework (AHF), developed and provided by the previous Arrowhead (AH) project. AHF is open source and addresses IoT-based automation and integration by abstracting IoT objects to services. AHF enables IoT interoperability and provides real time data handling, security features, automation system engineering, and automation systems scalability. In this paper, after a rapid overview of the AT project and the AHF architecture, we originally introduce the concept of Tool and Tool Chain for Industry 4.0 in AH. We also present a vertical AHT use case along with its implementation, as well as all the steps to turn a service/application into an AH-compliant Tool.

The Industrial Internet of Things (IIoT) is a key pillar of the Fourth Industrial Evolution or Industry 4.0. It aims to achieve direct information exchange between industrial machines, people, and processes. By tapping and analysing such data, IIoT can more importantly provide for significant improvements in productivity, product quality, and safety via proactive detection of problems in the performance and reliability of production machines, workers, and industrial processes. While the majority of existing IIoT research is currently focusing on the predictive maintenance of industrial machines (unplanned production stoppages lead to significant increases in costs and lost plant productivity), this paper focuses on monitoring and assessing worker productivity. This IIoT research is particularly important for large manufacturing plants where most production activities are performed by workers using tools and operating machines. With this aim, this paper introduces a novel industrial IoT solution for monitoring, evaluating, and improving worker and related plant productivity based on workers activity recognition using a distributed platform and wearable sensors. More specifically, this IIoT solution captures acceleration and gyroscopic data from wearable sensors in edge computers and analyses them in powerful processing servers in the cloud to provide a timely evaluation of the performance and productivity of each individual worker in the production line. These are achieved by classifying worker production activities and computing Key Performance Indicators (KPIs) from the captured sensor data. We present a real-world case study that utilises our IIoT solution in a large meat processing plant (MPP). We illustrate the design of the IIoT solution, describe the in-plant data collection during normal operation, and present the sensor data analysis and related KPI computation, as well as the outcomes and lessons learnt.

Mobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Typically, a large number of participants is required to make a sensing campaign successful. For such a reason, it is often not practical for researchers to build and deploy large testbeds to assess the performance of frameworks and algorithms for data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we present CrowdSenSim 2.0, a significant extension of the popular CrowdSenSim simulation platform. CrowdSenSim 2.0 features a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms. All these improvements boost the performances of the simulator and make the runtime execution and memory utilization significantly lower, also enabling the support for larger simulation scenarios. We demonstrate retro-compatibility with the older platform and evaluate as a case study a stateful data collection algorithm.

Internet of Things (IoTs)-based monitoring applications usually involve large-scale deployments of battery-enabled sensor nodes providing measurements at regular intervals. In order to guarantee the service continuity over time, the energy-efficiency of the networked system should be maximized. In this paper, we address such issue via a combination of novel hardware/software solutions including new classes of Wake-up radio IoT Nodes (WuNs) and novel data- and hardware-driven network management algorithms. Three main contributions are provided. First, we present the design and prototype implementation of WuN nodes able to support two different energy-saving modes; such modes can be configured via software, and hence dynamically tuned. Second, we show by experimental measurements that the optimal policy strictly depends on the application requirements. Third, we move from the node design to the network design, and we devise proper orchestration algorithms which select both the optimal set of WuN to wake-up and the proper energy-saving mode for each WuN, so that the application lifetime is maximized, while the redundancy of correlated measurements is minimized. The proposed solutions are extensively evaluated via OMNeT++ simulations under different IoT scenarios and requirements of the monitoring applications.

The collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as smart cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such Internet of Things (IoT) scenarios need in order to be pervasive. In this paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, mobile crowdsensing management for environmental data, information representation, and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

This paper proposes deployment strategies for consumer unmanned aerial vehicles (UAVs) to maximize the stationary coverage of a target area and to guarantee the continuity of the service through energy replenishment operations at ground charging stations. The three main contributions of our work are as follows. 1) A centralized optimal solution is proposed for the joint problem of UAV positioning for a target coverage ratio and scheduling the charging operations of the UAVs that involves travel to the ground station. 2) A distributed game-theory-based scheduling strategy is proposed using normal-form games with rigorous analysis on performance bounds. Furthermore, a bio-inspired scheme using attractive/repulsive spring actions are used for distributed positioning of the UAVs. 3) The cost-benefit tradeoffs of different levels of cooperation among the UAVs for the distributed charging operations is analyzed. This paper demonstrates that the distributed deployment using only 1-hop messaging achieves approximation of the centrally computed optimum, in terms of coverage and lifetime.

The Internet of Things (IoT) environments are no more a vision as they are already surrounding the everyday life of citizens. Its pervasive nature brings IoT ecosystems closer and closer to the end users, facilitating their domestic lives through home automation appliances and platforms. Several M2M communication technologies and data representation techniques have been standardized and often established by the manufacturers. For such reasons, many Home Automation Systems (HAS) are irreconcilable due to the incompatibility of their communication technologies. In order to take a step towards HAS interoperability, in this paper we propose RouteX, an experimental cross-technology platform able to manage home devices belonging to different networks and using different technologies. It leverages the potential of Service Oriented Architectures (SOA), providing the user with an abstraction layer over a multitude of home sensors and actuators. We also present a practical user interface, through which the user is able to administrate efficiently all his or her home appliances no matter which technology they use.

The integration of automotive communications in 5G systems must build on a clear understanding of the performance of services for connected vehicles in today's LTE deployments. In this paper, we carry out a simulation-based performance evaluation of automotive communications in LTE, with particular attention to realism: to that end, we investigate the impact of different road traffic models, employ a state-of-the-art commercial LTE tool, and study a practical service use case. Our results demonstrate that unrealistic road traffic datasets can bias network simulations in urban vehicular environments, and provide insights on the limitations of the current radio access architecture, when confronted to connected vehicles.

The study and engineering of Electro Mobility (EM) involves many industrial and academic players due to its potential significant benefits on society, economy, transportation and eco-sustainability. This article proposes a solution for EM automation, a crucial aspect for the future evolution of the EM market. The solution is based on a service-oriented, IoT and cloud centric ecosystem of charging stations conceived to support EM scenarios. The Eclipse Kura framework ensures the information exchange between the charging stations and the cloud infrastructure, providing remote management and data processing. The cloud platform is based on Eurotech EC and provides an internal service abstraction that offers efficient and secure mechanisms to collect raw data from the field. Kura is used to process data and store them on the cloud as semantically referenced information. Cloud services are accessible through a simple REST interface and published on the Arrowhead Framework, which is responsible for the integration of the multi-domain EM scenario. A real use case about the automation of a rural fast charging infrastructure is described and the benefits of applying the proposed solution to the use case are discussed with the help of the simulation results.

The Internet of Energy (IoE) for Electric Mobility is an European research project that aims at deploying a communication infrastructure to facilitate and support the operations of Electric Vehicles (EVs). In this paper, we present three research contributions of IoE. First, we describe a software architecture to support the deployment of mobile and smart services over an Electric Mobility (EM) scenario. The proposed architecture relies on an ontology-based data representation, on a shared repository of information (Service Information Broker), and on software modules (called Knowledge Processors -KPs) for standardized data access/management. As a result, information sharing among the different stakeholders of the EM scenario (i.e. EVs, EVSEs, City Services, etc) is enabled, and the interoperability of smart services offered by heterogeneous providers is guaranteed by the common ontology. Second, we rely on the proposed architecture to develop a remote charging reservation system, that runs on top of mobile smarthphones, and allows drivers to monitor the current state-of-charge of their EV, and to reserve a charging slot at a specific EVSE. Finally, we validate our architecture through a benchmark framework, that supports the embedding of mobile EV applications and of real KPs into a simulated vehicular scenario, including realistic traffic, wireless communication and battery models. Evaluation results confirm the scalability of our architecture, and the ability to support EVs charging operations on a large-scale scenario (i.e. the downtown of Bologna).