The development of intelligent Industrial Internet of Things (IIoT) systems promises to revolutionize operational and maintenance practices, driving improvements in operational efficiency. Anomaly detection within IIoT architectures plays a crucial role in preventive maintenance and spotting irregularities in industrial components. However, due to limited message and processing capacity, traditional Machine Learning (ML) faces challenges in deploying anomaly detection models in resource-constrained environments like LoRaWAN. On the other hand, Federated Learning (FL) solves this problem by enabling distributed model training, addressing privacy concerns, and minimizing data transmission. This study explores using FL for anomaly detection in industrial and civil construction machinery architectures that use IIoT prototypes with LoRaWAN communication. The process leverages an optimized autoencoder neural network structure and compares federated models with centralized ones. Despite uneven data distribution among machine clients, FL demonstrates effectiveness, with a mean F1 score (of 94.77), accuracy (of 92.30), TNR (of 90.65), and TPR (92.93), comparable to centralized models, considering airtime of Trainning messages of 52.8 min. Local model evaluations on each machine highlight adaptability. At the same time, the performed analysis identifies message requirements, minimum training hours, and optimal round/epoch configurations for FL in LoRaWAN, guiding future implementations in constrained industrial environments.

The evolution of the Internet of Things (IoT), progressing from a hyper-connected world of seamless device interaction to a paradigm that emphasizes the human factor as a central component, is driven by approaches like human-in-the-loop IoT (HITL-IoT). This shift enables the development of innovative systems in various environments. For example, in the context of a smart city, which comprises diverse solutions aimed at enhancing citizens’ quality of life and urban service efficiency, HITL-IoT approaches are employed to promote sustainability. Given the strong human involvement in these systems, challenges related to personal data protection, transparency, and participation must be addressed. Additionally, ensuring interoperability among different smart city entities is crucial for the coexistence and synergy of these solutions. This article presents a model that incorporates contemporary technologies like Blockchain and LoRa to integrate citizen-centric IoT privacy preservation solutions, fostering the vision of smart and sustainable cities. To demonstrate the feasibility of the proposed model, a prototype was designed, implemented, and deployed in a realistic scenario. Assessment results include metrics, such as latency, throughput, and communication aspects of the components.

New concepts based on the Internet of Things propose the integration of the human factor as a key component of novel interconnected ecosystems, to offer them new services and features. However, the data acquisition process, carried out by the devices that allow the deployment of these new paradigms, leads to concerns about what kind of information these entities are able to collect and/or infer. In fact, in most cases, users are not aware that their information is being gathered, nor can they properly control these data flows. Nowadays, the European Union’s General Data Protection Regulation calls for the adoption of transparent practices comprising, inter alia, adequate notification mechanisms, proper consent management schemes, and the development of models to empower the user as an active participant, with full control over the data flows, along with privacy preservation techniques. In this specific context, the aim of this manuscript is to present a review of existing user-centric privacy preservation models. This is, as far as we know, one of the few papers that addresses this particular privacy preserving dimension within an IoT context. Finally, the paper identifies and summarizes open issues, including a discussion of research opportunities in this area.

Citizens are a crucial factor in the sustainability of the cities because they are one of the pillars for balancing their environment. Smart city projects can offer citizens an opportunity to understand how they contribute to their city’s sustainability. Furthermore, an interactive smart city system can create synergies that benefit both the citizens and the city itself. This paper presents the Green Bear smart city platform, an IoT Human-in-the-Loop system that uses LoRaWAN nodes to gather information on city green spaces, bike lanes, and recycling depots. A user-centered mobile application that allows for active user participation, feedback, and incentivization is used to close the loop with humans. This system allows citizens to evaluate their participation in aspects that improve the city’s sustainability through a gamification scheme, obtaining points for different activities in the city’s public spaces and personal activities to improve their quality of life. This solution is being implemented in the city of Coimbra, Portugal. After an overview of the system, the paper describes each main system module. The Green Bear prototype was subject to functional and technological assessment, and the results are presented and discussed.

The last decade has witnessed a paradigm change in Industrial Control Systems (ICS), from closed, isolated systems to fully-connected, Internet-capable systems. At the same time, new wireless technologies, mainly coming from the Internet of Things domain, emerged. IEEE 802.15.4 wireless-based standards like WirelessHART, ISA100.11a, ZigBee, and WIA-PA are increasingly used for monitoring industrial processes. Nevertheless, along with this paradigm change, some new threats appeared that menace current industrial infrastructures and economies (e.g., Stuxnet, Mariposa, Slammer). Additionally, in order to keep up with the requirements of new Industry 4.0 applications, sensor nodes software and hardware are becoming more complex and, thus, more prone to faults. In this paper, using a monitoring architecture proposed in our previous work, we injected and subsequently proceeded to detect representative firmware and hardware anomalies (namely, buffer overflow attacks, SPI faults, under-voltage, and high temperature faults) that can be used by attackers to cause major losses or even damage industrial control systems. We evaluated the performance of several machine learning techniques commonly used to detect anomalies (i.e. OCSVM, kNN, AutoEnconder), in order to determine if they could be useful to detect such faults. The obtained results demonstrate that simple and broad scope classifiers, using features that consume little resources, can be developed to detect such faults.

Industrial Control Systems are now exploring the use of Internet of Things technologies not only to make them fitter to their job but also to explore the advantages that come from connecting them to the Internet. Nevertheless, with this paradigm shift, new threats appear, of which the stuxnet worm is just an example, and Intrusion Detection Systems architectures and solutions were and still are being considered. However, most existing projects concentrate on high level system aspects and thus neglect security aspects at wireless communication standards level, such as WirelessHART (the standard with largest market share), choosing not to address security solutions to common, known attacks identified by the community. In this paper, using a monitoring architecture proposed in our previous work, we will monitor a WirelessHART testbed, and at the same time, conduct network attacks from an outsider perspective. As main contributions this paper presents a new exhaustion attack for WirelessHART that, until now, to the best of our knowledge, has not been yet described. Additionally, the presented work proves that using classifiers like One Class Support Vector Machines (OSVM), and our monitoring architecture we are capable to detect the new exhaustion attack and more common attacks like jamming and collision.

Use of wireless sensor networks (WSNs) in conjunction with mobile devices is growing on a daily basis. Mobile phones are not only personal devices that provide communication services, but also versatile and resourceful elements in the Internet of Things (IoT). Nevertheless, despite the referred growth, one key aspect for real-world IoT deployments is still largely overlooked: reliability. In this respect, there is a clear gap between theoretical models and real implementations, as no practical reliability mechanisms are in place for IoT. Proposing, studying, and providing IoT reliability solutions is, thus, fundamental for the massive deployment of IoT technology across all sectors of society. In this context, the main purpose of this paper is to propose, explore, and assess reliability mechanisms for IoT scenarios, based on the use of redundant routes and devices that take advantage of mobile devices, as key elements of IoT. These mechanisms were evaluated in a variety of failure scenarios, both theoretically and through implementations constructed with real, off-the-shelf equipment, showing their good capability to maintain effective communication even in the presence of link failures.

Over the last years, Wireless Sensor Networks (WSN) went from being a promising technology for countless industrial applications to a de facto technology used in todays' applications. WSNs have been gaining momentum over costly wired technologies, offering low installation costs, self-organization, and added functionality. As a consequence of their enormous potential, WSNs were subject to standardization and some industrial standards and open source solutions like WirelessHART, Zigbee, ISA100, IEEE802.15.4 and OpenWSN were announced. However, despite considerable efforts to provide mechanisms that increase the availability, reliability, security and maintainability of this type of networks, WSNs have kept one of their main characteristics: fault-proneness. As a result, the offer of post-deployment diagnostic tools has been increasing in the last decade in order to diagnose WSN failures as soon as possible. Nevertheless, current WSN diagnostic tools still have many limitations and cannot be considered “ready to use” in real-world scenarios. In this paper we present an autonomous diagnostic tool that addresses these limitations in a real industrial Internet of Things (IIoT) scenario. Our tool is based on simple metrics, a logging tool, a data-mining algorithm, and available network metrics, and it monitors the condition of the sensor nodes firmware, hardware and the network itself. The proposed demonstration was tested and validated using the WirelessHART IIoT standard.

This paper presents Hermes, a new modular platform that can be used in a variety of scenarios, including the role of body area network coordinator, enabling temperature, movement and heartbeat sensing, local and remote communication, and indoor and outdoor localization. Hermes is based on two main modules that can operate as a single system in a coordinated fashion or individually, depending on the application scenario. One of the modules uses a Telos-inspired architecture, with new processing, communication, sensing, storage and energy subsystems, and executing TinyOS. The other module is designed around a PIC 24F MCU, also supporting communication, sensing, storage, and executing a custom operating system. Both modules have expansion capabilities. The main innovative aspects of the implemented platform are its modularity and its capability to provide device abstraction, which considerably eases application development. The paper addresses the platform motivation, requirements, hardware architecture, implementation details, and performance evaluation.

Horses played a key role in the history of mankind, having been instrumental in the rise and fall of great empires. In modern days, they are still very important assets, from economical, operational or even sentimental points of view. Their importance is, thus, the main motivation for developing the iHorse equine monitoring system presented in this paper. iHorse is a deployed, fully-operational, commercially-available, wireless-sensor-network-based system and application, developed and tested over a three-year period. The aim of this paper is to provide information on the main features of the system, including its architecture, implementation details and performance data. The paper also discusses the lessons learnt throughout the system development and deployment phases.

Mobility is one of the most important issues in next generation networks. As wireless sensor networks are becoming the next elements of the future Internet, it is crucial to study new models that also support mobility of these nodes. This paper presents and studies three paradigms to support mobility in sensor nodes. This study is supported by prototyping. As a result a set of proposals are proposed and discussed. The work presented in this paper proposes the use of IP in wireless sensor networks as a unifying protocol and it is in compliance with the IETF 6LowPAN group.

According to the application domain, Wireless Sensor Networks (WSNs) differ in a number of ways (e. g., deployment strategy, node mobility, available resources, node heterogeneity, topology, sensor coverage, Quality of Service (QoS), network size, lifetime, connectivity). This diversity is an obstacle to the development of a standard platform (hardware / software) that eases the task of building sensor networks applications. In this paper we present the design principles of a middleware solution that can be adapted to different application scenarios. We also present a mobile sensor and actuator platform that has been developed at the University of Coimbra in order to evaluate the performance of middleware solutions.