Global Navigation Satellite Systems (GNSS) are a key enabler for many new technologies, ranging from autonomous vehicles to shared mobile devices. In order to ensure high precision for those applications, GNSS augmentation systems are needed to provide correction data to reach an accuracy that is in the order of centimeters. Those systems can be provided as paid services where correction data are broadcast over a satellite link. In order to protect those systems and restrict their access only to paying users, we must adopt encryption mechanisms designed to avoid that pirates redistribute or resell the decryption key to unauthorized parties. At the same time, integrity protection mechanisms are needed to avoid that active attackers inject malicious GNSS augmentation data, which could disrupt or mislead the positioning. These objectives are made challenging by the peculiarities of satellite communication for the GNSS augmentation, in which bandwidth is scarce and receivers are resource-constrained. In this paper, we propose APBE (Anti-Piracy Broadcast Encryption) and SIA (Succinct Immediate Authentication), two methods to enhance the GNSS augmentation service security by providing protection against respectively pirate customers and active attackers. Both methods are specifically tailored to minimize bandwidth and processing time on the receiver. We demonstrate their feasibility via a proof-of-concept implementation on an ESP32 embedded system. We also measure APBE and SIA performance under various configuration, each giving a different security/performance tradeoff. APBE and SIA are candidate mechanisms to be included in the future versions of SPARTN, which is an open industry standard for GNSS augmentation.

The increasing adoption of Internet of Things (IoT) devices and growing demand for real-time applications have driven a shift in the computation paradigm from Cloud computing toward Edge computing, giving rise to theCloud-to-Things Continuum (C2TC). Many real-time IoT applications involve Mobile Nodes (MNs), such as mobile robots or autonomous vehicles, which may join or leave the system dynamically. In addition, future reconfigurable IoT systems will be characterized by multiple applications with heterogeneous requirements dynamically deployed or removed on the same infrastructure. To support such environment, dynamic management mechanisms are needed to ensure that the requirements of different real-time applications are guaranteed despite frequent system reconfigurations (e.g., application deployment and removal). This paper proposes DJ-NECORA, an online algorithm for joint allocation of networking and computing resources in C2TC, capable of guaranteeing the requirements of real-time applications while efficiently managing system reconfigurations and a varying number of MNs. DJ-NECORA is evaluated via simulations in a realistic scenario against two state-of-the-art solutions and an Ideal algorithm. Results show that DJ-NECORA manages system reconfigurations efficiently, provides better Quality of Service (QoS), and performs close to the Ideal algorithm.

The use of personal protective equipment (PPE) is essential to improve workplace safety. Despite specific regulations requiring the use of PPE, workers often neglect to wear it due to factors such as inattention, urgency, or convenience. Monitoring the correct use of PPE is especially critical in high-risk tasks. Computer vision technology can automate this process, leveraging deep neural models. This study investigates the performance of modern object detection models in identifying the correct use of PPE, focusing on their accuracy and execution speed. Specifically, the YOLOv11 and RT-DERT models are employed, trained on a real-world PPE dataset. Deployment on low-cost hardware, specifically an NVIDIA Jetson Nano, is evaluated using three deployment frameworks, namely PyTorch, OpenVINO, and TensorRT. The results show that YOLOv11n, with 2.6 million parameters, provides slightly lower average accuracy than more complex models. It stands out for its speed, reaching performances of 6.6 Frames Per Second (FPS) with PyTorch, 2.3 FPS with OpenVINO, and 10.6 FPS with TensorRT. On the other hand, YOLOv11l and YOLOv11x with, respectively, 46.5 and 86.7 million parameters offer higher accuracy, especially evident in small class identification, where simpler models tend to struggle. However, they show lower throughput, with 1.2 and 0.7 FPS on PyTorch. RT-DETR has competitive accuracy but lower performance on edge devices.

Deepfakes are a type of synthetic media created using artificial intelligence, specifically deep learning algorithms. This technology can for example superimpose faces and voices onto videos, creating hyper-realistic but artificial representations. Deepfakes pose significant risks regarding misinformation and fake news, because they can spread false information by depicting public figures saying or doing things they never did, undermining public trust. In this paper, we propose a method that leverages BLS signatures (Boneh, Lynn, and Shacham 2004) to implement signatures that remain valid after image cropping, but are invalidated in all the other types of manipulation, including deepfake creation. Our approach does not require who crops the image to know the signature private key or to be trusted in general, and it is ${\mathcal{O}}(1)$ in terms of signature size, making it a practical solution for scenarios where images are disseminated through web servers and cropping is the primary transformation. Finally, we adapted the signature scheme for the JPEG standard, and we experimentally tested the size of a signed image.

In Industrial IoT (IIoT) environments and especially for applications relying on Mobile Nodes (MNs), ensuring communication security is a critical requirement, in order to prevent attacks that aim to obtain sensitive data or compromise MNs. In this paper, we focus on the security of IIoT systems based on the 6TiSCH architecture defined by the IETF, in the presence of node mobility. We propose a method for provisioning key material to MNs, enabling the enforcement of a trade-off between security and overhead. Specifically, we consider three security settings, characterized by different security levels and communication/storage overhead. For each setting, we evaluate the impact on performance through simulation. Finally, we provide a set of guidelines to help network operators in the selection of the setting to use.

Critical industrial applications call for very stringent delay and high reliability requirements. To support them, even in the presence of Mobile Nodes (MNs), the 5G cellular technology provides the Ultra-Reliable Low-Latency Communications (URLLC) service class, which guarantees packet delivery with a maximum delay of a few ms and a reliability of 99.999%. In this paper, we investigate how to provide similar performance guarantees using IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) based on short-range wireless communication. To this aim, we propose an original resource scheduling algorithm, namely Enhanced Shared-Downstream Dedicated-Upstream (E-SD-DU). In addition, the paper provides the readers with the original contribution of an analytical model to determine the MNs that can be supported while satisfying the specified application requirements (and the related deployment cost). The obtained results show that our scheduling algorithm can guarantee a delay of $\mathbf{1 0 ~ m s}$ with a reliability of $\mathbf{9 9. 9 9 9 \%}$.

The rapid evolution of mobile health technologies has significantly transformed healthcare delivery. With smart-phones, smartwatches and various specialized sensors becoming commonplace, there is now a unique opportunity to continuously gather diverse biomedical data in a seamless and unobtrusive manner, for instance, to monitor patients affected by chronic disease. Adopting telemedicine on a large-scale, however, still requires novel approaches to ensure scalability. In this paper, we present a novel telemedicine platform for monitoring patients' affected by chronic disease that aims at efficiently managing how data is uploaded based on patients' information and the current system/network status. The proposed system employs heterogeneous non-intrusive commercial devices to collect health data. Each device is programmed to adjust the data collection rate based on the status of the patient and to change the data transmission rate depending on metrics measuring the current system/network status. This ensures that data from critical patients are collected in a fine-grained manner and received by the system with priority over the others, thus allowing the clinician to monitor patients' conditions in real-time. A proof-of-concept of the proposed platform is implemented and tested. Results demonstrate that the platform is capable of adjusting the transmission rate to ensure the priority of data from patients in critical condition, even without network support.

The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint allocation of networking and computing resources in C2TC that is capable of guaranteeing the requirements of real-time applications and efficiently managing possible changes in the system configuration. We evaluated DJ-NECORA through simulation in a realistic scenario. The results show that DJ-NECORA effectively handles application dynamics and, in some scenarios, outperforms offline resource allocation solutions by supporting 14% more MNs.

This demo presents a multi-modal system for the remote, continuous, and non-intrusive monitoring of patients with Chronic Obstructive Pulmonary Disease (COPD), developed within the framework of the European project TOLIFE. The proposed architecture integrates six complementary heterogeneous sensing devices: a smartphone, a smartwatch, a portable spirometer, smart shoes, a smart mattress cover, and a monitoring environmental unit. This kit collects heterogeneous physiological and contextual data in a continuous manner with minimal interference in the patient's daily activities. Data are offloaded to a collection and analysis platform based on InfluxDB, supporting scalable time-series data storage and retrieval. A web-based dashboard is exposed to enable real-time visualization, data analysis, and generation of automated alerts for both clinical and technical anomalies. The goal of the demo is to highlight the system's ability to support patient monitoring in a fine-grained manner, offering a robust and extensible solution for digital health applications targeting chronic respiratory conditions.

Function-as-a-Service (FaaS) is a serverless computing model that enables applications to be composed of self-contained functions triggered by events. The edge-cloud con-tinuum extends this paradigm by allowing function deployment closer to users and loT devices, reducing communication latency. However, large-scale FaaS deployment at the edge demands energy-efficient resource management to ensure cost reduction and sustainability. To address this challenge, we present$E^{2}FIS$: Energy-Efficient Function Invocation Scheduling, a framework that optimizes resource management and minimizes energy consumption in edge FaaS platforms.$E^{2}FIS$formulates function scheduling as a Mixed Integer Linear Programming (MILP) problem, minimizing energy consumption by consolidating work-loads while ensuring execution deadlines are met. Through simu-lations with real-world traces and experiments on the Serverledge FaaS platform,$E^{2}FIS$demonstrates to outperform the Earliest Deadline First (EDF) baseline, reducing energy consumption up to 92 % while maintaining timely function execution.

In the era of Smart Cities, video surveillance stands as a pivotal tool for enhancing urban security, optimizing resource management, and improving the quality of urban life. When video surveillance is seamlessly integrated with image analysis systems, raw visual data are transformed into actionable insights, significantly enhancing the capability of Smart Cities to ensure public safety and optimize urban operations. Image analysis systems mainly rely on the cloud: images are offloaded to a cloud infrastructure to be processed, analyzed and segmented for inference. The analysis of images in external systems, however, is not always recommended, due to privacy/security concerns, e.g., human action recognition. In this paper, we investigate the opportunity to adopt edge computing to implement such systems, where images are analyzed directly on-premises. To investigate the suitability of this approach, we carried out an extensive experimentation using two large-scale Fed4Fire+ testbeds, namely, Grid’5000 and Virtual Wall. Specifically, we considered different cloud-edge configurations using different inference models, and evaluated the impact of those models on performance and resource utilization. Based on these results, we provide a set of guidelines for the adoption of different models depending on the requirements of the specific application.

In the Industrial Internet of Things (IIoT) landscape, where the cloud-to-things continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve mobile nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this article, we propose joint network and computing resource allocation (J-NECORA), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results.

The use of personal protective equipment (PPE) is essential to strengthen the safety of workers in the workplace. Although there exist specific regulations requiring the use of PPEs, due to carelessness, haste or comfort, workers sometimes neglect to wear them. Thus, it is crucial to monitor the appropriate use of PPE, especially in dangerous processes. Computer vision technology can help perform this task automatically, exploiting appropriate deep neural models for recognizing PPE. YOLO (You Only Look Once) deep neural models ensure good accuracy against a limited complexity, which allows running them on devices with limited computational capacity. In this paper, we evaluate the performances, in terms of accuracy and processing speed, of the most popular models implemented in the version 5 of YOLO (YOLOv5) in the task of recognizing whether workers correctly wear PPE. To this aim, all models are trained on a PPE dataset on a dedicated server. Then, each model is deployed on low-cost hardware, which includes a Raspberry Pi4 Model B equipped with an Intel Neural Compute Stick 2, used as a processing unit. The outcomes show that YOLOv5n, with only 1.9 million parameters, is the fastest model which allows processing 7.9 frames per second, while YOLOv5l and YOLOv5x, respectively with 46.5 and 86.7 million parameters, are the most accurate but slowest models, processing 1.3 and 0.7 frames per second. We also compare the performance of the YOLOv5 models with the ones in version 4 of YOLO, showing how models in version 5 in general outperform the previous version with higher accuracy, especially in the detection of small objects.

The Synchronized Single-hop Multiple Gateway (SHMG) framework has been recently proposed to support mobility in 6TiSCH, the network architecture defined by the IETF for the Industrial Internet of Things (IIoT). SHMG includes a scheduling policy to allocate communication resources to Mobile Nodes (MNs) in order to satisfy the stringent requirements of industrial applications. Current scheduling algorithms, however, manage mobility by simply over-allocating communication resources, without taking into account the position of MNs. In this paper, we propose a Location-Aware Scheduling Algorithm with Rescheduling (LASA-R) that leverages the position of MNs, reported via periodic Position Notification (PN) messages, to optimize the allocation of communication resources. LASA-R also includes a conflict resolution mechanism to modify the schedule, as conflicts are detected. Finally, a mathematical methodology is developed to determine the optimal PN period. LASA-R is assessed through simulations. The results obtained show that it can guarantee a high reliability and a bounded latency, even with a very large number of MNs.

Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains, and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4Time-Slotted Channel Hopping (TSCH)protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop Multiple Gateway (SHMG) architecture. We first define a flexible scheduling algorithm, calledShared Downstream-Dedicated Upstream (SD-DU), that can be configured to adapt to different types of traffic in industrial applications. Then, we develop a mathematical framework to formalize the problem of Border Routers (BRs) placement to guarantee the complete coverage of the deployment area, in the presence of obstacles and unreliable communication. A methodology for network sizing is also proposed to calculate the maximum number of MNs that can be supported by the network without violating the application requirements. Finally, we evaluate the performance of the proposed solutions, both analytically and through simulations. Our results show that the proposed enhancements allow a very effective management of node mobility, by providing mobility transparency without a significant impact on performance.

The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits the maximum number of nodes supported by the network. To overcome this problem, we propose a Location-Aware Scheduling Algorithm (LASA) that takes into account the position of MNs to build and maintain an efficient communication schedule. Specifically, LASA tries to prevent conflicts arising due to node mobility, in a preventive manner, so as to minimize packet dropping. We evaluate LASA via simulation experiments. Our results show that LASA allows to increase the number of MNs by more than four times, with respect to SD-DU, yet guaranteeing a Packet Delivery Ratio higher than 98%.

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Internet of Things (IIoT). While many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers), 6TiSCH does not provide any mechanism to manage node mobility. However, recently, a Synchronized Single-hop Mul-tiple Gateway (SHMG) architecture has been proposed to allow an efficient management of mobile nodes in 6TiSCH networks. The SHMG architecture is quite flexible and can be customized to meet the requirements of different IIoT applications. However, find the appropriate configuration to guarantee the application requirements, may not be trivial. In this paper, we present Mobile6TiSCH, a simulation tool based on OMNeT ++, that implements the SHMG architecture. The proposed tool is general and allows to evaluate the Quality of Service (QoS) achieved by mobile nodes, in different scenarios. As such, it can be used to evaluate different solutions in a simulated environment before implementing them in practice. We describe the organization and implementation of Mobile6TiSCH and show the simulation results to display its effectiveness.

Cyber-physical systems (CPSs) are systems in which digital and physical entities cooperate. When CPSs execute safety-critical tasks, monitoring of their behavior is of main concern. The CPS should be moved to a safe state in case of malfunctions. This demo shows how an on-line supervisor can be developed for a Line Follower Robot, starting from a digital model of the robot.

Monitoring flash floods and providing just-in-time notification to city officials for taking appropriate action and prompt intervention is crucial for any smart city located in flood-prone areas around the world. Flood monitoring systems that exploit image analysis via Machine Learning (ML) techniques have been already proposed in literature. Such systems, however, adopt a cloud-based approach that generates significant data traffic and could be susceptible to failures due to network outages. In such a framework, images are continuously offloaded from cameras deployed in flood-prone areas of the city towards a cloud infrastructure where a service is deployed to analyze the images and detect the rise of water in rivers or city canals in a timely way. In this paper, we present the activities of the project EdgeFlooding, which aims at investigating the opportunity of adopting a distributed approach based on edge computing for the implementation of more resilient and reliable flash flood monitoring systems, that helps mitigate the limitations of the cloud-based systems. We have developed a prototype of an edge computing flood monitoring system based on micro-services, and we run an extensive set of experiments exploiting one European Fed4Fire+ testbed, i.e., the Grid'5000 testbed. The aim of those experiments is to assess whether a distributed edge/cloud computing approach is feasible for the implementation of future flood or environmental monitoring systems.

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery" problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a flexible Scheduling Function that can be easily adapted to meet the requirements of various IIoT applications. We analyze the considered Scheduling Function in different scenarios with varying traffic patterns, and define an algorithm for sizing the system in such a way to guarantee the application requirements. Finally, we investigate the impact of mobility on the performance of the system. Our results show that the proposed solutions allow to manage node mobility very effectively, and without significant impact on the performance.

The adoption of real-time object detection systems via video streaming analysis is currently exploited in several contexts, from security monitoring to safety prevention. In industrial environments, proper usage of Personal Protective Equipment (PPE) is paramount to ensure workers’ safety. However, the use of some types of PPE, such as helmets, is often neglected by workers, especially in indoor areas. Thus, in order to reduce the risks of accidents, real-time video streaming-based monitoring systems may be used to monitor areas in which workers operate and alert them not to wear PPEs via acoustic alarms or visual signals. In case of a remote analysis, there are potential issues related to the high rate of data streams to be transported and analyzed and workers’ privacy. In this work, we propose an embedded smart system for real-time PPE detection based on video streaming analysis and deep learning models. We discuss the deployment of different versions of the YOLOv4 network fine-tuned using a public PPE dataset. In the end, we assess the performance of the proposed system in terms of accuracy and latency and of the overall PPE detection procedure.

Recent Information Technologies are progressively dramatically changing how industrial processes are carried out. This is leading to a new industrial revolution, Industry 4.0, in which productivity, efficiency and safety are improved significantly. Safety of workers and operators, in particular, is expected to improve thanks to pervasive technologies like wearable devices and sensors. In this paper we present a demo of a sensorized glove for industrial safety based on Near-Field Communication (NFC). The demonstration is a proof-of-concept implementation that shows how an NFC-enabled sensor installed in the glove of a worker can be exploited to monitor worker's action and report dangerous situations in advance in order to prevent accidents and injuries.

Railways monitoring and control are currently performed by different heterogeneous vertical systems working in isolation without or with limited cooperation among them. Such configuration, widely adopted in practical deployments today, is in contrast with the integrated vision of systems that are at the foundation of the smart-city concept. In order to overcome the current fractured ecosystem that monitors and controls railways functionalities, the adoption of a novel integrated approach is mandatory to create an all-in-one railway system. To this aim, new IoT-based communication technologies, like wireless or Power Line Communication technologies, are considered the main enablers to integrate in a very rapid and easy manner existing vertical systems. In this work, we analyse the architecture of future railways systems based on a mix of wireless and Power Line Communication technologies. In our analysis, we aim at studying possible failure management strategies on rail-road switches to improve the level of reliability, crucial requirement for systems that demand maximum resiliency as they manage a critical function of the infrastructure. In particular, we propose a set of solutions aimed at detecting and handling network and sensor failures to ensure continuity in the execution of the basic control functions. The proposed approach is evaluated by means of simulations and demonstrated to be effective in ensuring a good level of performance even when failures occur.

The 6TiSCH architecture has been gaining attraction as a promising solution to ensure reliability and security for communication in applications for the Industrial Internet of Things (IIoT). While many different aspects of the architecture have been investigated in literature, an in-depth analysis of the security features included in its design is still missing. In this paper, we assess the security vulnerabilities of the 6top protocol, a core component of the 6TiSCH architecture for enabling network nodes to negotiate communication resources. Our analysis highlights two possible attacks against the 6top protocol that can impair network performance and reliability in a significant manner. To prove the feasibility of the attacks in practice, we implemented both of them on the Contiki-NG Operating System and tested their effectiveness on a simple deployment with three Zolertia RE-Mote sensor nodes. Also, we carried out a set of simulations using Cooja in order to assess their impact on larger networks. Our results show that both attacks reduce reliability in the overall network and increase energy consumption of the network nodes.

The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to ensure reliable and timely communication. 6TiSCH relies on the IEEE TSCH MAC protocol and defines different scheduling approaches for managing TSCH cells, including a distributed ( neighbor-to-neighbor ) scheduling scheme, where cells are allocated by nodes in a cooperative way. Each node leverages a Scheduling Function (SF) to compute the required number of cells, and the 6top (6P) protocol to negotiate them with neighbors. Currently, the Minimal Scheduling Function (MSF) is under consideration for standardization. However, multiple SFs are expected to be used in real deployments, in order to accommodate the requirements of different use cases. In this article, we carry out a comprehensive analysis of 6TiSCH distributed scheduling to assess its performance under realistic conditions. Firstly, we derive an analytical model to assess the 6P protocol, and we show that 6P transactions take a long time to complete and may also fail. Then, we evaluate the performance of MSF and other distributed SFs through simulations and real experiments. The results show that their performance is affected by the failure of 6P transactions and the instability of the routing protocol, which may lead to congestion from which the network is unable to recover. Finally, we propose a new SF (E-OTF) and show, through simulations and real experiments, that it can effectively improve the overall performance, by allowing nodes to quickly recover from congestion.

The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to provide reliable and timely communication. 6TiSCH includes a distributed management mode, in which network resources are computed autonomously by nodes and allocated in a cooperative way. Specifically, nodes use the 6top protocol to negotiate network resources with their neighbors. In this paper, we investigate the performance of 6top protocol through a set of experimental measurements on a testbed. We show that the time required to complete a 6top transaction in a real environment is not negligible, as often assumed in many studies. In addition, a significant percentage of 6top transactions fails, with a negative impact the performance of the application. We investigate the reasons for such failures and propose some guidelines to reduce their number.

Urban population is expected to continuously grow in size. The smart city concepts allows to handle the new challenges and issues created by this growth by applying a wide range of technologies that can provide citizens with a better living environment. Smart agriculture will play an important part of smart cities, as a sustainable and high quality food supply chain is crucial to facilitate the grow of human agglomerates. In this context, European laws imposes very strict requirements in the food industry, in order to ensure that food provenance is always guaranteed. Such fine-grained traceability can be only achieved by applying state-of-the-art technologies. In this paper, we present BRUSCHETTA, a blockchain-based application for the traceability and the certification of the Extra Virgin Olive Oil (EVOO) supply chain. EVOO is an emblematic food product for Italy, but it is also one of the most falsified ones. BRUSCHETTA provides a blockchain-based system to enforce the certification of this product by tracing its entire supply chain: from the plantation to the shops. The goal is to enable the final customer to access a tamper-proof history of the product, including the farming, harvesting, production, packaging, conservation, and transportation processes. BRUSCHETTA leverages Internet of Things (IoT) technologies in order to interconnect sensors dedicated to EVOO quality control, and to let them operate on the blockchain. We also provide a support for the correct tailoring of the BRUSCHETTA blockchain system, and we propose a mechanism for its dynamic auto-tuning to optimize it in case of high loads.

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.

The IEEE 802.15.4g standard is a wireless technology that can potentially enhance the capabilities of the Internet of Things. Originally conceived for Wireless Smart Utility Networks, it can be used in a number of potential application domains, both in urban and rural environments, as it provides low-power and low-cost communication over long distances, thus enabling Low-Power Wide Area Networks. In this paper, we show the results of a set of experiments carried out in real environments to measure the communication range in outdoor deployments. In our experiments, we used Zolertia remote sensor nodes and considered different scenarios, namely rural, semi-rural, and urban environments. The results obtained show that in the urban context, due to the presence of buildings, trees and other obstacles that prevent the signal propagation, the communication range is under 200m, while in a rural (open) environment it is possible to achieve longer distances, in the order of 800m.

The industrial Internet of Things (IIoT) is expected to revolutionize the current industry. The capillary introduction of sensors and actuators for real-time monitoring and remote control and their seamless integration into existing information systems will represent a technological breakthrough that will help to reshape the industrial processes. To this aim, the definition of wireless communication standards will play a crucial role in reducing deployment costs and minimizing the time for installation. In this context, the new IPv6 over the TSCH mode of IEEE 802.15.4e communication stack, 6TiSCH, represents the current leading standardization effort that aims at achieving both reliable and timed wireless communication and integration within IPv6 communication networks for industrial systems. In this paper, the network formation dynamics of 6TiSCH networks are assessed, considering the current guidelines for the so-called minimal configuration, a static initial configuration pre-configured to guarantee control communication during network bootstrap. It is shown that the minimal configuration might lead to long network formation and suboptimal performance of the routing algorithm which may result into a disconnected network. In order to overcome this issue, a dynamic resource management algorithm to be executed during network bootstrap is proposed. Simulation and experimental results show that the proposed solution allows to minimize the network formation time and also helps in optimizing routing operations leading to the discovery of better routes.

Due to their intrinsic resource constraints, the mobile Internet of Things (IoT) devices are not able to provide intensive services by just relying on their own facilities. Fog Computing effectively helps overcome this hurdle. Indeed, it extends the Cloud toward the network edge, distributing resources and services of computing, storage, and networking close to the end devices. This topological proximity is the key enabler of several advantages that are essential in many emerging ICT domains. Nonetheless, the mobility of an IoT device compromises such benefits as it increases the topological distance to the serving Fog node. Therefore, the Fog service has to be migrated in order to be always close enough to the served IoT device. We name thisCompanion Fog Computing(CFC), since the Fog service behaves as a "companion" of the correspondent application on the mobile device. In this paper, we present a Fog Computing Platform that performs stateful container (i.e., Fog service) migrations in order to enable CFC. Specifically, we introduce a CFC model from which we derive a reference architecture comprising all the functionalities required in a platform to make migration decisions and carry them out. Moreover, we demonstrate the soundness of the proposed reference architecture by discussing a proof-of-concept implementation based on the Stack4Things (S4T) platform, and we report a set of conducted experiments to show the feasibility of stateful container migrations.

As for the Cloud, essential features of Fog Computing are both virtualization and the capability to migrate virtual environments among nodes. In this paper, we thoroughly report both the state-of-the-art virtualization and migration techniques and their available implementations. In particular, we investigate the aptness of such technologies for a specific layer of the Fog hierarchy, namely the network edge. Indeed, this layer presents some characteristics that distinguish it from the Cloud so that a virtualization or migration technique that represents a good compromise for the Cloud might not be likewise suitable for the edge.

The possibility of interconnecting any kind of device to the Internet is driving the adoption of the Internet of Things (IoT) paradigm also in a city environment. Many IoT applications are making the Smart City concept real, offering advanced services to citizens and city administrators. This evolution relies upon a seamless exchange of information among different systems. Exchanged data includes personal and/or critical information, thus requiring proper handling in order to avoid security and privacy issues. At the same time, recent developments in robotics are fostering the realization of autonomous agents (e.g., cars, buses, drones) that do not require human intervention. In the city of the future, many services will rely on autonomous agents. Also, autonomous agents (e.g., robots) will become more human alike, and will be able to show emotions. Therefore, they will replace humans in many city activities. In this paper, we first overview the expected evolution of IoT applications and, then, we briefly analyze the security, social, and ethical issues that are foreseen in a Smart City context.

Future Smart Cities will be enabled by a pervasive fabric of sensors and actuators, seamlessly integrated within information systems. This large-scale Internet of Things (IoT) will bring a new level of challenges to existing communication networks and wireless standards. In order to analyze these issues and study possible solutions, a quick and rapid experimentation is of paramount importance to ensure that IoT protocols and network architectures can handle such challenges. In this paper, we present the ECOAP project that aims at evaluating the performance of the Constrained Application Protocol (CoAP), the application protocol defined to interconnect IoT devices with information systems. The project will carry out an evaluation of CoAP through the WiSHFUL platform, a platform for large-scale experimentation of network systems. The project will focus on assessing different congestion control strategies in order to evaluate the scalability of the protocol in future large-scale scenarios.

CoAP is an application protocol that provides standardised RESTful services for IoT devices. Since COAP messages are encapsulated into UDP datagrams, COAP specification provides: i) optional reliability mechanisms through retransmissions, and ii) simple congestion control mechanisms based on retransmission timeouts. Recent studies have demonstrated that these congestion control schemes may significantly underperform when operating with bursty traffic. To address these limitations, in this paper we propose COAP-R, an alternative solution for regulating the sending rate of CoAP sources, which adopts a rate-based approach for traffic control. Key features of COPA-R are: i) to leverage the tree-based routing structure of IoT networks to estimate the maximum throughput that can be obtained on the bottleneck link of every upward route, and ii) to perform in a distributed manner a max-min fair allocation of available network capacity on the basis of estimated bottleneck bandwidths. The proposed approach is evaluated by means of simulations considering a scenario in which traffic is generated in bursts, for instance as consequence of events detected by sensors. Our simulations demonstrate that the proposed approach ensures a fair allocation of network resources, and leads to a 40% decrease of the data collection delays when compared to COAP.

The IETF 6TiSCH Working Group (WG) is currently standardizing a novel architecture to integrate IEEE 802.15.4e TSCH Wireless Networks into the Internet of Things. Within the proposed 6TiSCH architecture, a Scheduling Function (SF) is used to manage the allocation of communication resources (i.e., TSCH timeslots). Although many SFs have been proposed in literature, the 6 TiSCH WG is considering the On-The-Fly (OTF) Bandwidth Reservation Algorithm as the reference SF. In this paper, we carry out an extensive simulation analysis of OTF to evaluate if, in its current definition, it can ensure the low latency and high reliability requirements of critical applications targeted by TSCH. Specifically, we investigate how its performance is affected by the RPL protocol and the 6top protocol used for routing and distributed resource negotiation, respectively. Our results show that the OTF performance is significantly affected by (i) frequent changes in the preferred parent selected by RPL, and (ii) failures in resource negotiation. These events typically lead to congestion, from which the network can hardly recover. To overcome such limitations, we propose a modified version of the Bandwidth Reservation Algorithm, named Enhanced-OTF. We show that proposed modifications can effectively improve the overall network performance by allowing network nodes to recover from congestion in a short time.

Massive diffusion of constrained devices communicating through low-power wireless technologies in the future Internet of Things (IoT) will require in many scenarios the deployment of IoT gateways to allow applications to discover and access IoT resources. In this context, Fog/Edge computing will represent an opportunity to deploy IoT gateways in proximity of IoT domains, meeting the requirements of applications needing low-latency interactions with devices. In this paper, we present an edge-centric distributed architecture to provide resource discovery and access services to IoT applications. The proposed approach leverages the CoRE Resource Directory interface and the Constrained Application Protocol (CoAP) to expose a standard interface for global discovery and access. Multiple IoT gateways are federated through a peer-to-peer overlay implemented by a distributed hash table, which is exploited to share the information on IoT resources available across multiple domains. The proposed solution is validated by means of a small-scale prototype, and extensively evaluated through emulation in large scale deployment in comparison to a centralized Cloud-based approach. Experimental results demonstrated that the proposed approach guarantees lower latencies than a centralized solution and can ensure scalability for small to medium sized deployments.

Wireless Sensor Networks (WSNs) will represent a crucial enabling technology to interconnect sensors and actuators in future smart cities. Since many applications will demand for reliable and low-latency communication, current standardization efforts are focusing in the definition of novel wireless standard architectures, e.g., the 6TiSCH architecture, to improve reliability and introduce support for quality of service. In this paper, we evaluate the performance of the 6TiSCH architecture during the initial network formation. Specifically, the performance of the 6top protocol, defined in 6TiSCH for distributed negotiation of resources, is evaluated. Simulations highlighted how the initial negotiation of resources is influenced by the routing protocol and the link-quality estimation mechanism adopted. The performance evaluation allowed to draw a set of guidelines for network configuration to guarantee the reliability of the initial allocation of resources.

Future intelligent transportation systems and applications are expected to greatly benefit from the integration with a cloud computing infrastructure for service reliability and efficiency. More recently, fog computing has been proposed as a new computing paradigm to support low-latency and location-aware services by moving the execution of application logic on devices at the edge of the network in proximity of the physical systems, e.g. in the roadside infrastructure or directly in the connected vehicles. Such distributed runtime environment can support low-latency communication with sensors and actuators thus allowing functions such as monitoring and remote control, e.g. for remote telemetry of public transport vehicles or remote control under emergency situations, respectively. These applications will require support for some basic functionalities from the runtime. Among them, discovery of sensors and actuators will be a significant challenge considering the large variety of sensors and actuators and their mobility. In this paper, a discovery service specifically tailored for fog computing platforms with mobile nodes is proposed. Instead of adopting a centralized approach, we propose an approach based on a distributed hash table to be implemented by fog nodes, exploiting their storage and computation capabilities. The proposed approach supports by design multiple attributes and range queries. A prototype of the proposed service has been implemented and evaluated experimentally.

The Internet of Things (IoT) envisions billions of devices seamlessly connected to information systems, thus providing a sensing platform for applications. The availability of such a huge number of smart things will entail a multiplicity of devices collecting overlapping data and/or providing similar functionalities. In this scenario, efficient discovery and appropriate selection of things through proper context acquisition and management will represent a critical requirement and a challenge for future IoT platforms. In this work we present a practical approach to model and manage context, and how this information can be exploited to implement QoS-aware thing service selection. In particular, it is shown how context can be used to infer knowledge on the equivalence of thing services through semantic reasoning, and how such information can be exploited to allocate thing services to applications while meeting QoS requirements even in case of failures. The proposed approach is demonstrated through a simple yet illustrative experiment in a smart home scenario.

Abstract The continuously increasing interest in developing efficient vehicle‐roadside communication networks to provide on‐board connectivity has recently brought to the definition of several solutions based on different wireless technologies. Among them, wireless local area network‐based solutions emerged as an attracting alternative to guarantee high‐quality connectivity enabling services, like video streaming, that require stringent quality of service guarantees. In this work, we propose a handoff procedure based on a forecasting model of the link quality for mobile routers operating in vehicle‐roadside wireless local area network‐based networks. First, a preliminary set of experiments is performed in a realistic environment to study the behavior of the wireless channel when mobility in urban environment is considered. Then, considering the hands‐on experience gained from the initial set of experiments, a novel handoff procedure is designed, which exploits a forecasting technique to predict link channel quality. The proposed procedure is then exploited in a cross‐layer manner to proactively reduce the number of transmitted layers during handoff in the case of real‐time video traffic based on H.264/SVC encoding. The proposal is assessed by means of simulation and compared with existing solutions. Results demonstrate that our proposal guarantees performance comparable with other algorithms. The advantage of predicting the handoff point is demonstrated by means of simulations employing a realistic video streaming traffic model, showing how the quality of experience perceived by end‐users can be improved through the adaptation of the traffic load. Copyright © 2016 John Wiley & Sons, Ltd.

The future Web of Things (WoT) foresees a web in which applications can seamlessly access physical objects through the same REST interface used today to access web services. A key enabler of this shift is the Constrained Application Protocol (CoAP), a redesign of the popular HTTP protocol that aims at supporting resource-constrained devices for Machine-to-Machine applications. In this work we propose a proxy virtualization framework to support scalability and easy implementation of custom functionalities in large WoT deployments. The functionalities offered by a CoAP proxy are exploited to transparently decouple applications from servers and to guarantee the implementation of custom policies and functionalities through virtualization techniques. A solution based on Linux Containers is implemented in a real test bed made of off-the-shelf hardware and open-source software, demonstrating the feasibility of the proposed design. Experimental results have shown that the response time is highly improved by our solution thanks to central coordination of concurrent requests from different virtual proxy instances. Moreover, to highlight the potential of the proposed frame-work, a priority-based Quality of Service policy is also implemented and evaluated.

Cloud computing is a key enabler for the development and deployment of large-scale IoT service platforms. The integration into such platforms of different sensing and actuating systems will imply the availability of many similar IoT services with common functionalities though with different QoS and cost. A cloud-based IoT platform can then benefit from implementing QoS-aware service selection algorithms to match application demand to IoT services, whilst guaranteeing to meet the respective QoS requirements. Such algorithms must however take into account that IoT service providers are usually constrained devices with limited computation, storage and energy capabilities. In this work we formulate the QoS-aware service selection problem for IoT cloud platforms as an integer optimization problem, whose solution minimizes the energy consumption so as to maximize the lifetime of battery-powered devices, whilst guaranteeing the fulfillment of real-time QoS requirements. We then propose a computationally efficient heuristic algorithm to solve the problem, and show through extensive numerical analysis that it is able to find solutions very close to the optimal one in all considered scenarios.

Lightweight link quality estimation is crucial in wireless sensor networks. Indeed, devices with limited capabilities shall trade off between consuming their resources to maintain a precise view of the neighbours' link quality and to build routes almost blindly. For instance, the Routing Protocol for Low-Power and Lossy Networks (RPL), which has been recently standardised by the IETF to enable IPv6-based sensor networks, only estimates the quality of the links used to deliver data packets. However, this solution has been demonstrated to cause periods of routing instability and reduced packet delivery rates since it estimates only the quality of utilised links. To address this issue in this work we propose a lightweight link estimation procedure that exploits Trickle-based topology maintenance techniques to simultaneously estimate link qualities and propagate routing information. Our proposed scheme has been integrated in the Contiki's RPL prototype implementation. Simulation results demonstrate that our proposal is capable of measuring the quality of the links to neighbours with small overhead, which results into better routing decisions and improved packet delivery rates.

In recent years, wireless mesh networks have become a popular solution for a wide range of scenarios because of their flexibility, reliability and fast deployment. In particular, in the context of providing network connectivity to mass transit vehicles, wireless mesh networks are often preferred to cellular networks because they allow custom network deployments. However, when high mobility is involved, an efficient handoff procedure is of paramount importance to ensure seamless connectivity. In this work, we propose a handoff procedure for wireless mesh networks which exploits a forecasting technique to predict link channel quality. The proposal is assessed by means of simulation and compared with existing solutions. Results demonstrate that our proposal outperforms existing algorithms and provides an overall link quality which is close to the optimal.

It is a great pleasure to welcome you to the second edition of the IEEE Workshop on the Internet of Things: Smart Objects and Services.

In wireless mesh networks two different media access control mechanisms have been defined to allow several mesh node to access the shared wireless means: CSMA and TDMA. In CSMA, concurrent transmissions from different nodes are allowed. To mitigate the problem of interference several orthogonal channels are assigned to break the network in several smaller collision domain. In TDMA instead, time is divided in collision free transmission opportunities (slots) which are allocated to the nodes. A static allocation of these resources, channels and slots, has been demonstrated to result in poor performances due to variation in channel quality and in traffic demand over the time. In this work we provide an overview of our ongoing research on how to manage these resources efficiently. In particular, we propose two dynamic allocation policies which are designed to mitigate the problems of collisions, in CSMA, and local congestion, in TDMA, caused by an initial static allocation.

The Mesh Coordinated Channel Access (MCCA) defined by the IEEE 802.11s draft standard implements a TDMA-based Medium Access Control (MAC) protocol by allowing mesh routers to negotiate collision-free transmission opportunities, called MCCAOPs, in a hop-by-hop manner. The procedure to determine the duration, in time slots, and the periodic schedule of MCCAOPs is however left unspecified by the standard. In this paper, we propose a Dynamic Delay-balancing Slot Allocation (D2SA) algorithm to deal with dynamic traffic conditions in the context of IEEE 802.11s MCCA. D2SA is fully distributed and aims at exploiting locally at each node the statistical multiplexing of different traffic flows in order to mitigate temporary congestion or under-utilization of the available capacity due to variable traffic demand. This is accomplished by dynamically balancing over the smallest possible time scale the queuing delays experienced by packets relayed to different neighbors. By means of extensive packet-level simulations under realistic network assumptions, we evaluate the effectiveness of D2SA and show that, by improving both the average and the percentiles of the delay per link at each node, it is able to yield better end-to-end performance than in the static case at basically no additional overhead cost.

Power saving is an important feature of mobile Broadband Wire-less Access (BWA) systems, since it allows user terminals to switch off the radio transceiver when there is no network activity, thus increasing the battery lifetime. In this paper, it is shown that even greater energy savings can be obtained if user terminals are put to sleep while there is network activity, by trading off performance for battery duration in a controllable way. To this aim, a framework is proposed, in which existing wireless scheduling algorithms can be fit to pursue their original goals, e.g., maximizing throughput or fairness, while improving the energy efficiency of user terminals. The framework is specifically tailored to the IEEE 802.16/WiMAX technology. Its effectiveness is assessed in this context through an extensive packet-level simulation campaign with realistic FTP data traffic.

Mobile Broadband Wireless Access (BWA) networks will offer in the forthcoming years multiple and differentiated services to users with high mobility requirements, connecting via portable or wearable devices which rely on the use of batteries by necessity. Since a relatively large fraction of energy is consumed by such devices for transmitting/receiving data over-the-air, mechanisms are needed to reduce power consumption, in order to increase the lifetime of devices, and hence, improve user's satisfaction. The IEEE 802.16, which supports mobile BWA since its "e" amendment in 2005, defined power saving functions at the Medium Access Control (MAC) layer, which are designed to be operated during open traffic sessions for the greatest energy consumption reduction. However, enabling power saving usually increases the transmission latency, which can negatively affect the Quality of Service (QoS) experienced by users. On the other hand, imposing stringent QoS requirements may limit the amount of energy that can be saved. In this paper, an extensive study of the mutual interaction between power saving mechanisms and QoS support is carried out in the context of the IEEE 802.16e. In particular, two types of delay-constrained applications with different requirements are considered, i.e., Web and Voice over IP (VoIP) for which the IEEE 802.16e standard specifies two different power saving classes. The performance is assessed via detailed packet-level simulation, with respect to several system parameters. To capture the relative contribution of all the factors on the energy- and QoS-related metrics, part of the evaluation is carried out by means of 2k· ¿! analysis.