In our increasingly tech-saturated world, from mobile apps and health sensors to autonomous cars and factory robots, we expect these devices to seamlessly integrate into our lives, enhancing safety and convenience. However, as these devices proliferate and their autonomy grows, ensuring they provide unobtrusive, yet effective support becomes crucial. The Horizon Europe KST multi-partner project “Distributed Multi-Sensor Systems for Human Safety and Health” (DistriMuSe) intends to support human health and safety by improved sensing of human presence, behaviour, and vital signs in a collaborative or common environment by means of multi-sensor systems, distributed processing and MachinelDeep Learning (ML/DL) techniques. In this paper, we focus on the DistriMuSe's approach on sports performance and health assessment, focusing on monitoring the physical activity of non-professional and hobby athletes, people who like sports and care about their health, elderly healthy people, and subjects affected by neurological disability (e.g., Parkinson's disease). The overall goal is to measure activity and exertion, estimating performance levels and determining maximum effort. We discuss the overall system-of-systems architecture, focusing on the adopted technologies.

The OPtimization of Electric Vehicle Autonomy (OPEVA) project enhances data aggregation for Electric Vehicles (EVs) by collecting critical real-time data (i.e., vehicle performance, battery health, charging behaviours) through heterogeneous data acquisition devices built on robust HW and integrated with Internet of Things (IoT) protocols. By combining internal sensor data and driver-specific behaviours with external information (e.g., road conditions, charging station availability), OPEVA maximizes vehicles performance, establishing secure and seamless data communication between EVs and the infrastructure, and using IoT and cloud computing tools alongside Vehicle-to-Everything (V2X) devices and networks. This paper focuses on the extensible data model ensuring semantic data integrity considering in- and out-vehicle factors, presenting data acquisition solutions dealing with OPEVA's semantic data model and their use in various Artificial Intelligence (AI)-powered use cases (e.g., range prediction, route optimization, battery management).

This paper provides an overview of the domain challenges, use cases, objectives, high-level concepts, intended innovations, and expected impact of the DistriMuSe project. The project’s main aim is to enhance human health and safety by improved sensing of human presence, behaviour, intentions and vital signs in a collaborative or common environment by means of multi-sensor systems, distributed processing and machine learning. The use cases address challenges in health monitoring of elderly, sleep and exercise, of drivers and vulnerable road users in traffic and of people interacting with robots in a factory environment. Technical development in the project focuses on unobtrusive monitoring sensors, multi-sensor systems, distribution of computation and intelligence, and domain specific needs for the use cases.

This paper describes the NextPerception project’s outcomes organized as examples motivated by user stories. The project developed next-generation perception sensors and enhanced the distributed intelligence paradigm to build versatile, secure, reliable and proactive human monitoring systems, in turn applied in use cases in health and automotive domains.

Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such prices is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as \(R^{2}\) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.

In Internet of Things (IoT)-like contexts, there is often the need to leverage traffic routing mechanisms among heterogeneous devices, especially when classical (and well-known) addressing paradigms cannot be adopted or supported by constrained IoT devices deployed on the field (e.g., due to memory footprint, internal limitations, etc.). This is even more true (and necessary) when nodes interact in unstructured networks (e.g., mesh-like) lacking a specific topology (e.g., exploiting flooding approaches to transfer information) and external “smart” devices should be allowed to interact with these networks. To this end, in this paper a multi-sink routing protocol, denoted as Routing on Mesh Bluetooth Low Energy (), is proposed. Our implementation relies on BLE advertisement channels and allows sink nodes to control topology formation and data collection (with both unicast and broadcast communications), with nodes identified with compressed addresses. A relevant experimental application to environmental lighting management is presented.

Localization services for precise and continuous monitoring of the locations of both humans and vehicles in industrial environments are among the most relevant applications in Industrial Internet of Things (IIoT) contexts, to maximize safety and optimize operational activities. Unfortunately, localization in industrial scenarios is particularly challenging because targets can generally move freely in both indoor and outdoor areas. In this paper, we propose a localization monitoring architecture based on a prototypical wearable IoT device equipped with Ultra-Wide Band (UWB), inertial, and GNSS/RTK technologies for seamless localization in heterogeneous environments. We focus on a Web of Things (WoT) approach, verifying suitability and limitations in a real use case scenario. Our approach shows that the proposed architecture can effectively enhance the safety of workers, detecting potentially dangerous events and triggering alarms (e.g., via smart buzzers or gas concentration warning devices) based on a cloud WoT architecture.

Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.

Given the massive deployment of Internet of Things (IoT) applications over the last decade, the need for gateways able to efficiently route information flows across multiple heterogeneous networks has emerged, bringing new challenges. Therefore, the design and implementation of IoT gateways is crucial. In this paper, with reference to the architecture of a prototypical Multi-Interface Gateway (MIG) (based on Commercial-Off-The-Shelf, COTS, devices), we evaluate its performance: (i) analytically, through an innovative Markov chain-based model; (ii) by simulation, with a Python simulator; (iii) experimentally, through the (starting) COTS device-based prototype. In detail, the MIG is equipped with heterogeneous wireless communication interfaces (namely LoRaWAN, BLE, cellular 4G Cat. 4, and IEEE 802.11 Wi-Fi 2.4 GHz) and is applicable to multiple IoT scenarios. The obtained simulation and experimental results show the validity of the proposed analytical model. Further improvements of the proposed framework are eventually discussed.

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical “virtualization” of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes' side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

Bluetooth Low Energy (BLE) is a wireless communication technology for power-constrained Internet of Things (IoT) applications. BLE data can be transmitted via either the IPv6 or the Generic ATTribute (GATT) Profile protocol, with the former supporting dynamic IoT structures and the latter being application-friendly. In fact, GATT requires the data layout to be known in advance by peer devices, in order to properly interpret the received data. In this paper, we introduce DynGATT, a protocol that achieves the benefits of both IPv6 and GATT, by extending GATT in a seamless fashion to support dynamic IoT structures. The key idea of DynGATT is to use GATT descriptors, originally intended to specify data in static IoT scenarios, to also specify IoT systems whose structures may dynamically evolve. Peer devices reading these descriptors will know how to interpret the data of GATT characteristics provided by devices joining the IoT network. Because no additional data have to be transmitted, the connection time is then reduced with respect to classical BLE. DynGATT has been implemented and tested in an agricultural IoT application, with different types of sensor nodes. Our experimental evaluation shows that DynGATT is very power-efficient, despite its added flexibility. Its worst-case power consumption is only around 19.37 µA per data transmission and around 41.37 µA overall. This consumption can be further reduced by using the methods discussed in this paper. To the best of our knowledge, this work is the first to support dynamic IoT structures in a GATT-based setting.

Transmission latency is a key performance metrics in most wireless sensor network (WSN) applications. Nodes in a WSN often keep their radio transceivers off, and turn them on periodically using a duty cycling mechanism. The latter is a major source of delay in the network, because transmissions must wait for the next receiver wake-up. In this paper, we present a cross-layer approach to minimize latency of a request-response (RR) protocol adopted in an IEEE 802.15.4-based WSN where the IPv6 routing protocol for low-power and lossy networks (RPLs) is used. Extra wake-ups are generated dynamically to match the predicted arrival time of the response packet, in order to reduce the duty cycling delay. The proposed approach is verified with the Cooja simulator, relying on the Contiki operating system (OS). The observed experimental results show a shorter RR delay with respect to a phase alignment (PA) approach.

Research efforts in the field of Internet of Things (IoT) are providing solutions in building new types of “network of networks,” going beyond the technological barriers due to intrinsic limitations of the constrained devices typically used in this context. Thanks to the improvement in communication/networking protocols and the hardware cost reduction, it is now possible to define new IoT architectures, combining the “micro” IoT paradigm, based on short-range radio technologies (e.g., IEEE 802.15.4 and IEEE 802.11), with the rising “macro” IoT paradigm, based on sub-GHz radio technologies. This allows the implementation of scalable network architectures, able to collect data coming from constrained devices and process them in order to provide useful services and applications to final consumers. In this paper, we focus on practical integration between micro and macro IoT approaches, providing architectural and performance details for a set of experimental tests carried out in the campus of the University of Parma. We then discuss challenges and solutions of the proposed micro-macro integrated IoT systems.

The current specification of the Segment Routing (SR) architecture requires enhancements to the intradomain routing protocols (e.g. OSPF and IS-IS) so that the nodes can advertise the Segment Identifiers (SIDs). We propose a simpler solution called PMSR (Poor Man's Segment Routing), that does not require any enhancement to routing protocol. We compare the procedures of PMSR with traditional SR, showing that PMSR can reduce the operation and management complexity. We analyze the set of use cases in the current SR drafts and we claim that PMSR can support the large majority of them. Thanks to the drastic simplification of the control plane, we have been able to develop an open source prototype of PMSR. In the second part of the paper, we consider a Traffic Engineering use case, starting from a traditional flow assignment optimization problem, which allocates hop-by-hop paths to flows. We propose a SR path assignment algorithm and prove that it is optimal with respect to the number of segments allocated to a flow.

The Internet of Things (IoT) aims at connecting billions of devices in an Internet-like structure. This gigantic information exchange enables new opportunities and new forms of interactions among things and people. A crucial enabler of robust applications and easy smart objects' deployment is the availability of mechanisms that minimize (ideally, cancel) the need for external human intervention for configuration and maintenance of deployed objects. These mechanisms must also be scalable, since the number of deployed objects is expected to constantly grow in the next years. In this work, we propose a scalable and self-configuring peer-to-peer (P2P)-based architecture for large-scale IoT networks, aiming at providing automated service and resource discovery mechanisms, which require no human intervention for their configuration. In particular, we focus on both local and global service discovery (SD), showing how the proposed architecture allows the local and global mechanisms to successfully interact, while keeping their mutual independence (from an operational viewpoint). The effectiveness of the proposed architecture is confirmed by experimental results obtained through a real-world deployment.