The emergence of 6G networks introduces new challenges, including the planning of ultra-dense networks that can be optimized through Network Digital Twins (NDTs). In smart cities, NDTs are especially valuable, as they integrate network and mobility data to enable adaptive and intelligent management. This work proposes a NDT designed to analyze the impact of urban mobility on 5G networks. The NDT environment integrates real and simulated urban mobility with emulated 5G base stations, emulating real vehicles in the road accessing 5G network services while on the move. The proposed system leverages metric collection for beyond-5G networks with real-time and historical capabilities, to support the envisioned simulations. The system combines real-world data collection with large-scale network emulation using open-source projects, such as SUMO framework, UERANSIM, and Kubernetes to simulate multiple V2X connections between vehicles and 5G base stations. The proposed NDT bridges urban mobility and network behavior, providing a foundation for planning and emulating beyond-5G network management solutions in mobile and dynamic environments. Several scenarios have been tested, such as random, a marathon with road blocks, and lane closure. The results show a high aggregation of vehicles and data traffic in one of the 5G antennas when blocking roundabouts on fast roads in comparison to the random scenario; moreover, the closure of a double entry lane to the roundabout at the location of the 5G gNodeB translates to much lower peak utilization and a better balance of antenna resources during the running scenarios.

The accelerated deployment of smart cities has led to a proliferation of diverse services operating simultaneously within urban environments. However, this growth presents the challenge of scalability issues, due to the varied resources used per service and the difficulty in analyzing wide-ranging network flows, since most traffic is not classified by their service. We present a path toward a smart city sensitive to criticality by: (1) showcasing contributions to evolve an infrastructure with the internet-of-things (IoT) and edge computing to accommodate traffic differentiation and service criticality; and (2) proposing a distributed network protocol, Rank, that is capable of dynamically allocating various types of resources along paths between nodes in the network infrastructure, so that new services could be added on demand. Our results show that it is possible to transform a smart city into a criticality-sensitive one by adopting a traffic classification with little to no impact on packet forwarding and time synchronization through hierarchical elements with respect to a GNSS source, where Rank conveniently solves the dynamics of variable traffic with low overhead.

In recent years, the adoption of containerization solutions has grown significantly. Simultaneously, there has been a rise in applications with stricter latency and jitter requirements, such as tactile internet, autonomous vehicles, and immersive AR/VR. Time-Sensitive Networking (TSN) emerges as a promising solution for these latency-critical applications, offering tools specifically designed to manage such conditions and support mixed-criticality scenarios involving both hard and soft deadlines. This paper explores techniques and solutions for running latencysensitive applications in containerized environments. It focuses on how high-priority traffic can coexist with best-effort traffic using TSN mechanisms, and how containerized applications can leverage hardware-based techniques to achieve precise transmission timing. The results demonstrate that, with TSN mechanisms alone on the network side, high-priority traffic can coexist with interfering traffic while maintaining accurate transmission scheduling and the target bandwidth. However, hardware-based techniques such as Launchtime proved crucial in achieving the most precise transmission schedule-evident in a $\mathbf{4 2}$-fold reduction in standard deviation. This improvement is also reflected in the P99 metric, where 99% of transmission delays remained below 0.501 ms. Finally, the paper presents mitigation strategies to address challenges introduced by container isolation, which can cause unforeseen conflicts between transmission schedules and add latency to communications.

The growing need for healthier and more ecofriendly city transport has empowered the enforcement of electric and smart bikes, which integrate sensors and communication technologies to improve monitoring and safety. This paper presents a data management architecture designed to enhance the efficiency and adaptability of 2-wheeler monitoring and safety systems. The architecture integrates a data stream service and a persistence system, using fuzzy logic to dynamically prioritize and manage data transmission. The data stream service ensures the delivery of critical messages, such as diagnostics, malfunction and safety alerts, while the persistence system stores data batches and schedules their transmission based on factors such as priority, deadlines, and expiration status. Experimental evaluations show that fuzzy logic-based prioritization significantly reduces message waiting times, outperforming traditional approaches. These results demonstrate the effectiveness of the proposed solution in ensuring reliable and timely data management under varying conditions.

The growing need for healthier and more ecofriendly city transport has made electric and smart bikes more popular, but this also brings safety concerns to cyclists. This paper proposes an Advanced Driver Assistance System (ADAS) solution for smart bikes to improve cyclist safety, throughout a set of cooperative perception services that communicate using Vehicle to Everything (V2X) to a city network. The services presented collect physical data from the $\mathbf{2}$-wheelers and surrounding environment: detect rear obstacles using Light Detection and Ranging (LiDAR); use computer vision on the front to identify pedestrians and vehicles; and use inertial sensors to detect the road conditions. These services are designed with constraints on power consumption while meeting real-time detection requirements. Experiments in real scenarios show that: (1) the LiDARbased rear detection system achieved an F1-score of 0.98; (2) the road quality assessment system distinguished smooth roads from potholes with an F1-score of 0.75; and (3) the pedestrian detection service achieved $85 \%$ accuracy. This work provides innovative and practical solutions to improve road safety and collaborative perception in smart bicycles.

Heterogeneous networks integrate diverse devices and technologies, creating significant challenges for efficient traffic forwarding and network management. Smart cities are an example of such networks, relying on applications ranging from transportation to public safety, and demanding adaptive communication infrastructures, terrestrial and non-terrestrial, to ensure seamless connectivity and performance. Simultaneously, Software Defined Networking (SDN) has emerged and proved itself as a valid new approach to network management, enabling intelligent and centralized control. This paper presents an approach, denominated Service-Aware Multi-path BAlancing (SAMBA), to service-aware traffic forwarding to manage heterogeneous, multi-technology, multi-path environments, overcoming the limitations of previous, more technology-specific methods. This approach is evaluated against Shortest Path Forwarding (SPF) across various network technologies, including Ethernet, wireless networks (IEEE 802.11p and private 5G), and Non-Terrestrial Networks. The results demonstrate significant improvements in service throughput and latency while enhancing load distribution and preventing network link saturation.

The operation of hybrid networks (wireless and wired-based) is subject to a number of significant challenges, particularly in relation to the frequent changes in topology and high mobility that are inherent to them. This is especially the case in Vehicle to Everything (V2X) networks. This paper proposes a Software Defined Network (SDN)-based monitoring platform, designed for improving network visibility and responsiveness in dynamic environments. The proposed approach leverages Cooperative Intelligent Transportation System (C-ITS) messages to enable real-time tracking of vehicular mobility and wireless links quality, which enhances the SDN controller's ability to adapt to rapidly changing network conditions, thereby providing crucial data for informed decision-making. This broader view of the network provides the foundation for future seamless communication devices. The system's efficiency in maintaining up-to-date network awareness, optimizing handover processes, and ensuring communication stability across hybrid networks has been demonstrated in outdoor testing. The results show that incorporating the monitoring and control approaches within SDN environments markedly enhances the management and reliability of dynamic wireless networks.

This paper proposes an urban and mobility-based Digital Twin that provides the representation of an urban scenario with both real and simulated mobility data of vehicles, 2-wheelers and people. The platform integrates real-time data from the Aveiro Tech City Living Lab (ATCLL) with 2D and 3D visualizations using SUMO and CARLA, respectively, enabling detailed traffic analysis and management. Key functionalities include the synchronization of real-world sensor data with simulated environments, providing accurate and dynamic traffic visualizations. The platform also supports the change and blocking of intersections, roundabouts and lanes, being able to test scenarios when the road conditions change, anticipating the impact of those changes in the urban mobility. The platform results show that they can help decision makers to optimize the traffic flow and anticipate changes in the roads, providing information on the travel times, CO2 emissions and congestion in the roads.

Modern smart homes are becoming increasingly complex due to the growing number of connected wireless devices and the need for a fast, stable Internet connection. Tri-band Wi-Fi 6E addresses these demands by reducing network congestion and enhancing performance across the 2.4, 5 and 6 GHz bands. However, improper channel management can lead to frequent interference and network instability. This work introduces a Wi-Fi management solution that uses reinforcement learning to automatically optimize network configurations based on the observed interference levels. The Wi-Fi network is mapped to a virtual environment to simulate different scenarios with real data acquired from the network, allowing the system to learn and improve without disrupting the actual network. Practical tests were conducted in four residential network layouts, including star and daisy chain topologies. The solution effectively reduces the number of hops, improves the throughput in longer paths from the gateway to clients, adapts to unforeseen network dynamics in the presence of Ethernet clients and adjusts the configuration as clients move within the home. The results demonstrate improved network capacity and responsiveness compared to default configurations. In the best cases, clients achieved rates of up to 1 Gbps, round-trip times as low as 10 ms, and efficient airtime distribution across the three frequency bands. The proposed approach provides a robust and automated solution to address the challenges of domestic wireless networking and paves the way to more sophisticated virtualization solutions for end users at home.

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.

Smart-city services are typically developed as closed systems within each city's vertical, communicating and interacting with cloud services while remaining isolated within each provider's domain. With the emergence of 5G private domains and the introduction of new M2M services focusing on autonomous systems, there is a shift from the cloud-based approach to a distributed edge computing paradigm, in acontinuumorchestration. However, an essential component is missing. Current orchestration tools, designed for cloud-based deployments, lack robust workload isolation, fail to meet timing constraints, and are not tailored to the resource-constrained nature of edge devices. Therefore, new orchestration methods are needed to support MEC environments. This paper addresses this gap. We propose an orchestration platform and its algorithms to facilitate the seamless orchestration of both cloud and edge-based services, encompassing both critical and non-critical services. This work extends the current Kubernetes orchestration platform to include a novel location-specific resource definition, a custom scheduler to accommodate real-time and legacy services, continuous service monitoring to detect sub-optimal states, and a refined load balancing mechanism that prioritizes the fastest response times.

The process of software updates for future autonomous vehicles on the road can be a critical process since they are constantly moving. Therefore, V2X services are susceptible to occasional connectivity interruptions, which can render traditional Over-the-Air (OTA) updates on the Software Defined Vehicle (SDV) concept impractical. By leveraging Mobile Edge Computing, using its Edge Nodes, with the assistance of the distributed protocol InterPlanetary File System (IPFS) and tools like Ansible, an OTA update system was designed to enable agile updates of V2X services under the Fifth Generation (5G) network and current Software Defined Network (SDN). The system enhances the network’s adaptability and minimizes service disruptions, reducing operational costs, and ensuring timely deployment of software patches and feature upgrades. This paper shows the proposed Vehicle to Everything (V2X) OTA updates solution and the tests performed in a real-world environment, the Aveiro Tech City Living Lab City (ATCLL) in Aveiro, Portugal, and shows how the vehicles gather the content blocks through the distributed edge road side units while the vehicles travel around in the different roads. We believe that such an approach can be set as an exemplary standard for modern Smart cities globally, in regard to the V2X service deployment.

The growing use of Internet of Things devices and the increasing demand for high-speed, reliable, and secure wireless connectivity pose significant challenges for existing wireless networking solutions in modern smart homes. As such, there is an increasing urgency for the development of advanced and effective wireless technologies that can fulfill the new requirements for interconnected devices and services deployed in households. Tri-band Wi-Fi 6E equipment addresses these needs by reducing the network congestion and enhancing the performance across the 2.4 GHz, 5 GHz, and 6 GHz bands. However, improper band management can lead to frequent interference and network issues. As such, this work introduces a dynamic Wi-Fi link orchestration solution that follows a heuristic model. This approach aims to optimize the network layout and channel allocation based on device metrics, utilizing the EasyMesh specification for simplified network setup and management. The model was implemented and tested in a residential environment network, using EasyMesh and tri-band Wi-Fi 6E devices. Results show the effectiveness of the model in improving network capacity and adapting the links to the current traffic, outperforming the initial and base network configuration.

Over the last years, there has been an enormous growth in internet and mobile users. In urban regions, the combination of these events has resulted in a massive flow of network traffic to the city infrastructure and cellular operators. Content Delivery Networks (CDNs) appear as a promising solution: by distributing the content through multiple nodes, they are capable to deliver content with low latency, over intermittent connections, leading to a reduction in the use of the 5G backhaul. This paper focuses on the proposal of a CDN for a smart city, deployed in a private cellular network, with CUPS and MEC, using the IPFS protocol. A comparison between the CDN and direct stream is performed, in controlled and real-world environments, to services already deployed on the Aveiro Tech City Living Lab (ATCLL). Lastly, the impact of IPFS distribution on the edge and core, in CUPS and MEC scenarios is assessed.

Over the years, WiFi became an essential technology. The success of the introduction of wireless devices with WiFi connectivity increased the demand for better WiFi networks. Such networks need better service and better coverage, either in mobile or residential environments. To solve this challenge, WiFi Alliance developed WiFi EasyMesh, a standard for WiFi networks that uses multiple access points that allow an easy setup and compatibility with WiFi certified devices. This work studies the performance of a mesh wireless network with different frequencies in use by the backhaul links (2.4 GHz and 5 GHz). The results can then be used to derive better backhaul steering algorithms to a better Quality-of-Service.

Vehicle-to-everything represent a major step in the evolution of Intelligent Transportation Systems (ITS), by allowing vehicles and the infrastructure to share information seamlessly. The new sensors introduced by the next generation of vehicles, that can be autonomous or partially assisted, generate huge volumes of data that, in some scenarios, may not be handled by the available resource-constrained devices. Therefore, edge and fog concepts have been applied to vehicular networks under Vehicle Edge Computing (VEC), to perform computational offloading, content caching, data management, flexible network management, security, and others. Technologies like virtualization and containerization are the cornerstones when addressing service deployment on edge and cloud environments. This work assesses different schemes of VEC service deployments to understand their use with the deterministic and reliable nature of some critical VEC services, such as an open-source implementation of the ETSI C-ITS protocol suite. Service prioritization and preemption are explored in order to achieve an upper-bound latency in native and containerized services deployment. The results show that the preemption leads to a slight increase in the average and median processing delay, but it also results in positive effects for real-time services; the prioritization of services is able to provide lower upper-bound latency, providing a performance without guarantees but with an expected delay.

Modeling realistic millimeter-wave (mmWave) channels is crucial to the study of ultra-reliable communication in next-generation wireless networks. MmWave provides significant gains over sub-6GHz communication but has very stringent requirements on channel conditions, since slight variations in the channel may result in significant performance degradation of mmWave communication. In this work, we present an experimental mmWave testbed and the mathematical modeling of the channels using the measurements collected from an outdoor testbed that complies with IEEE 802.11ad. We show how the model fits the reality and demonstrate the impact of adaptive causal network coding in mmWave real and simulated networks.

Over time, mobile communication networks have seen significant evolution. In the 5G generation, the rollout of the network requires network densification, due to the new data-hungry applications and an exponential number of new devices. The idea of a smart city, a technologically advanced metropolitan area that gathers data to enhance the general quality of life of its residents, also contributes to the densification topic. To provide content for this increased number of end-users, Content Delivery Networks (CDNs) appear as a promising solution: by distributing the content through multiple end-nodes that are near the user, they are capable to deliver content with low latency, over intermittent connections, leading to a reduction in the use of the network backhaul. This work focuses on the architecture for 5G networks with Multi-Access Edge Computing (MEC) to be used in smart cities. By using User Plane Function (UPF) selection, the Control User Plane Separation (CUPS) concept has enabled the disaggregation of the Core Network (CN). Thus, a CDN was deployed in the private 5G network using the InterPlanetary File System (IPFS) protocol, to evaluate the proposed approach. The demo presented in this paper shows the integration of IPFS intelligence with the CN, more specifically in the desirable UPF, and it also approaches the idea of an Application Function (AF).

Mobile communication networks evolved staggeringly throughout the years. Current evolution (5G) needs a denser network, due to new vertical-based data-hungry applications and the increase of UEs, leading to additional costs in cellular deployment. To solve this issue and optimize the deployment of 5G, one unified network could be devised and shared by multiple operators to deploy their own networks. With a city network planned, ways to take full advantage of a neutral hosting architecture begin to be executed, which serve various services, like Vehicle-to-Everything (V2X) and Internet-of-Things (IoT). This is where topics such as Multi-Access Edge Computing (MEC) arise, by maximizing the exploitation of the city infrastructure to reduce latency and remove the computational effort from cloud servers to the edge. This thesis focuses on developing a neutral architecture for 5G networks, with Multi-acess Edge Computing (MEC), which can be deployed in a city. In the concrete case of this thesis, this architecture will be deployed in the city of Aveiro, taking advantage of its multiple edge nodes. The disaggregation of the Core Network (CN) is a focal point of this thesis, creating a way to have a gateway closer to the edge, thus enabling MEC technologies to improve end-user and service experience. A disaggregated architecture was proposed, and extended to the city infrastructure, to accurately simulate the city environment and to attest to the city services. The test scenarios involved the comparison of a service instantiated in the cloud and in the edge, mobility scenarios with a handover through the same network to attest user plane relocation, different user plane selection, and lastly, different flow level priority assessment, to higher priority services in a full bandwidth occupation scenario. The obtained results show that the deployment of the user plane in the edge brings significant improvements, both in common user traffic and service-oriented traffic; moreover, this outlines the capabilities of this core solution as well as its limitations providing a foundation for future works.

WiGig technologies, such as IEEE 802.11ad and later IEEE 802.11ay, provide multi-gigabit short-range communication at 60 GHz for bandwidth-intensive applications. However, this band suffers from high propagation losses that can only be compensated using highly directional antennas, making millimeter-wave (mmWave) links susceptible to blockage and errors. This high sensitivity to blockage leads to unstable and unreliable connections, since proprietary IEEE 802.11ad mechanisms, such as beamforming training, have high overhead, and can only be triggered when performance degradation is already detected, which compromises QoS and QoE even more. This article proposes a proactive machine learning framework that uses real-life data acquired in an outdoor setting to improve the reliability and resilience of a blockage-prone WiGig-based network. In particular, we propose a link quality classifier, which can differentiate between normal, long-term blockage and short-term operation with a test F1-score of 97%. Moreover, we introduce a novel deep learning forecasting model that can accurately capture the interactions between past multi-layer observations under different environments to produce accurate forecasts for 16 KPIs.

This article presents the deployment and experimentation architecture of the Aveiro Tech City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of Internet of Things (IoT) devices with communication, sensing, and computing capabilities. The communication infrastructure, built on fiber and millimeter-wave (mmWave) links, integrates a communication network with radio terminals [WiFi, ITS-G5, cellular vehicular-to-everything, 5G and LoRa(WAN)], multiprotocol, spread throughout 44 connected points of access in the city. Additionally, public transportation has also been equipped with communication and sensing units. All these points combine and interconnect a set of sensors, such as mobility (radars, light detection and rangings (LiDARs), and video cameras) and environmental sensors. Combining edge computing and cloud management to deploy the services and manage the platform, and a data platform to gather and process the data, the living lab supports a wide range of services and applications: IoT, intelligent transport systems (ITSs) and assisted driving, environmental monitoring, emergency and safety, and among others. This article describes the architecture, implementation, and deployment to make the overall platform to work and integrate researchers and citizens. Moreover, it showcases some examples of the performance metrics achieved in the city infrastructure, the data that can be collected, visualized, and used to build services and applications to the cities, and, finally, different use cases in the mobility and safety scenarios.

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.

Smartphones have spawned a new generation of people-centric sensing applications, where their sensors gather information from users to achieve a wide range of objectives, from fitness monitoring to the urban-wide management of traffic. The connections between devices are of utmost importance for these systems, in fact, switching between the multitude of network interfaces available to smartphones (3G/4G, WiFi) can be beneficial to improve connectivity, the distribution of network traffic and save battery power. Current solutions in the literature for the management of network interfaces are limited in the sense that they only consider either application requirements or the system's status. In this paper, we propose a new model that considers not only these aspects but also the human-context, that is, the user's position and activity to intelligently manage which interfaces should be used. To support the switching between different interfaces without interrupting existing connections, we also discuss several handoff techniques. Finally, we present an experimental evaluation of our model based on Multipath TCP.

Mobile sensing in Cyber-Physical Systems has been evolving proportionally with smartphones. In fact, we are witnessing a tremendous increase in systems that sense various facets of human beings and their surrounding environments. In particular, the detection of human emotions can lead to emotionally-aware applications that use this information to benefit people's daily lives. This work presents the implementation of a Human-inthe- loop emotionally-aware Cyber-Physical System that attempts to positively impact its user's mood through moderate walking exercise. Data from smartphone sensors, a smartshirt's electrocardiogram and weather information from a web API are processed through a machine learning algorithm to infer emotional states. When negative emotions are detected, the application timely suggests walking exercises, while providing real-time information regarding nearby points of interest. This information includes events, background music, attendance, agitation and general mood. In addition, the system also dynamically adapts privacy and networking configurations based on emotions. The sharing of the user's location on social networks and the device's networking interfaces are configured according to user-defined rules in order to reduce frustration and provide a better Quality of Experience.

In general, deployments of Wireless Sensors Networks in industrial rigs face several constrains, especially those concerning power supply of scattered devices: cabling is expensive and takes a long time, and, on the hand, batteries are sensitive to both current peaks and high temperatures, thus requiring periodic replacement. This work presents a low power router which, by gathering energy from a tiny solar panel and storing it in a supercapacitor, addresses the stringent regulations that have to be met to work in potentially explosive atmospheres and, still, keep working continuously so as to serve communication requirements of wireless networks in such harsh environments: evaluation of the energy balance under different harvesting and consumption conditions was carried out, whose results ensure the feasibility of this technology.