Mission-critical applications depend on networks that consistently meet strict end-to-end (e2e) latency bounds. In these systems, a network becomes effectively unavailable whenever delay exceeds the required deadline, making availability a question of delay compliance rather than simple uptime. This paper proposes a methodology for assessing delay-based availability using Extreme Value Theory (EVT). The contribution lies in the systematic integration and automation of established EVT techniques to enable reproducible and diagnostically validated tail analysis of latency data. The approach includes algorithmic selection of thresholds and block sizes, formal validation of approximate independence through declustering, and stability diagnostics for reliable tail modeling. We demonstrate the methodology on a publicly available latency dataset from a commercial 5G non-standalone (NSA) network, used strictly as a case-study example. Results show consistent tail-index estimates across both EVT methods, enabling extrapolation of delay-violation probabilities under conditions where EVT assumptions are verified. The proposed framework provides a principled foundation for evaluating delay-based availability in communication systems where rare extreme delays dominate reliability and sufficient measurement data are available.

5G networks offer significant advancements over its predecessor, 4G Long-Term Evolution (LTE). Low latency network access, a key requirement enabling near real-time responsiveness as required by applications such as autonomous driving, factory automation and virtual reality, is one of 5G's key features. In this paper, we present the results of a long-term measurement campaign of the uplink end-to-end (e2e) latency experienced by a 5G-capable device using a commercial sub-6Ghz 5G non-standalone (NSA) network. Our results show an average uplink e2e latency of 12ms, with a 95th percentile of 21ms. This compares favorably with an average uplink e2e latency of 35ms and a 95th percentile of 53ms using 4G LTE to reach the same destination. We also characterize and define, through real-world network parameters in the uplink data transmission process, an unexpected latency pattern that impacts the performance of latency-sensitive applications, even in 5G standalone (SA) networks, such as edge computing or ultra-reliable low-latency communication (URLLC)-a new class of applications targeted in 5G networks.

The mobile network operation in the unlicensed spectrum offers opportunities for non-public connectivity services without the financial burden of spectrum licensing. Previous studies regarding unlicensed spectrum operation have mainly relied on analysis or simulation without addressing practical constraints. In this work, we designed, implemented, and evaluated the listen-before-talk protocol as a co-existence medium access mechanism on top of the 5G-NR physical layer. We propose different TDD configurations to enable different sensing frequencies. We enabled OpenAirInterface operation in the n46/n47 bands, a European band dedicated to connected intelligent transportation services. We evaluate the performance of unlicensed 5G-NR under bursty cross-traffic from a different device. The results show that the frequency of cross-traffic bursts affects BLER (Block Error Rate) and throughput more than their duration. The design of the TDD frame to offer more frequent sensing opportunities allows for reduced BLER but negatively impacts achievable throughput in the current setup.

Multipath transport can be used in heterogeneous wireless environments to provide seamless connectivity between a mobile device and edge nodes. Two challenges of different nature can be addressed by multipath transport: 1) seamless handovers; and 2) bandwidth aggregation across multiple access networks to obtain a more reliable end-to-end service. Multipath transport has been widely explored for wired networks and for web access. However, existing solutions target reliable throughput, while emerging use cases often favour delivery timeliness, eventually at the cost of some errors. This work discusses multi-access Real-time Transport Protocol (MA-RTP), a multipath protocol for sensor stream transport. The motivation behind developing MA-RTP stems from the need for a multipath transport protocol specifically designed to cater to the requirements of real-time, loss-tolerant data streams. It is associated to a context-based scheduler to decide which access network to use. Both the protocol and the scheduler are implemented and tested in a video offloading scenario with 5G and 802.11ac access technologies to show the potential of the proposal.

WiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs’ limited coverage, offloading performance is heavily impacted by the users’ mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter’s mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed methodology. The ability to predict the next ORs would benefit offloading orchestration. Offloading mobility predictability, although crucial, proves to be challenging, expressed by the poor predictive performance of well-known models ($\approx $37% acc. for the best predictor). We show that mobility regularity properties improve predictive performance up to$\approx $35%. Finally, we look into the impact of further OR extraction and prediction parameters. We show that the exploration phase length does not impact the discovery of low relevance ORs, and that both filtering low relevance OR and predicting multiple ORs increase predictability. By characterising the trade-off between mobility predictability and offloading opportunities in transit, we highlighting the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets.

We implement a quantum binary classifier where given a dataset of pairs of training inputs and target outputs our goal is to predict the output of a new input. The script is based in a hybrid scheme inspired in an existing PennyLane's variational classifier and to encode the classical data we resort to PennyLane's amplitude encoding embedding template. We use the quantum binary classifier applied to the well known Iris dataset and to a car traffic dataset. Our results show that the quantum approach is capable of performing the task using as few as 2 qubits. Accuracies are similar to other quantum machine learning research studies, and as good as the ones produced by classical classifiers.

Covidmonitor is a crowdsensing tool to support epidemologists and public health authorities in monitoring the covid-19 pandemic. The tool collects data to support transdisciplinary studies aiming at improving the knowledge of the pandemic evolution as well as monitor the citizens' behaviour and mental health. Covidmonitor leverages a previously existing mobile crowdsensing platform, SenseMyCity, adapted in collaboration with epidemology, public health and psychology researchers. Our biggest challenge was to identify the relevant metrics for the target trans-disciplinary studies and map them to collectable data. Covidmonitor explores the concept of citizens as probes to sample collective behaviour. The mobile application launches questionnaires about hygiene practices, use of personal protection equipment, health and emotional state. The questionnaires are triggered by different logic, adequate to the multi-dimensional perspectives of the target studies. Covidmonitor also seamlessly collects relevant mobility data without significant battery consumption. Finally, it enables voluntary sharing of location and symptom history, to facilitate tracing in case of infection. The tool considers user privacy and data minimisation by design, and is currently under preliminary scrutiny of the data protection regulator in Portugal.

In-vehicle wireless networks (e.g., Wi-Fi, Bluetooth) are experiencing a faster market penetration than dedicated V2V technologies and are compatible with non-V2X devices. In this paper, we assess whether commodity in-vehicle networks can leverage opportunistic V2X communication to nodes outside of the vehicle, particularly with Vulnerable Road Users. We characterize the radiation pattern and performance of communication links in the 2.4 GHz band between in-car wireless networks and a wireless-enabled bicycle in two representative interaction scenarios (i.e. parallel and perpendicular vehicle-VRU trajectories) using both production hardware (i.e. built-in WiFi hotspot) and dedicated measurement equipment. Empirical results show that (i) the signal propagation to the outside of the vehicle is strongly affected (up to 20 dB) by the vehicle elements (e.g. pillars) and by the placement of the wireless system inside the car, and (ii) the communication performance (in terms of RSSI, IRT, Throughput) is also impaired by the spatial arrangement of vehicle and VRU, and other time-varying phenomena (e.g., human body and bicycle shadowing). We conclude that the in-car system performance allows supporting a wide range of safety and infotainment applications (e.g., IRT under 300 ms) even at large TX-RX distances, and that the placement of an in-car wireless system should be tailored according to the target application.

3D maps of urban environments are useful in various fields ranging from cellular network planning to urban planning and climatology. These models are typically constructed using expensive techniques such as manual annotation with 3D modeling tools, extrapolated from satellite or aerial photography, or using specialized hardware with depth sensing devices. In this work, we show that 3D urban maps can be extracted from standard GNSS data, by analyzing the received satellite signals that are attenuated by obstacles, such as buildings. Furthermore, we show that these models can be extracted from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips, unleashing the potential of applying the principle to wide areas. Our proposal incorporates position inaccuracies in the calculations, and accommodates different sources of variability of the satellite signals' SNR. The diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios using data crowdsourced from over 900 users. Our results show that the generalization accuracy for a Random Forest classifier in typical urban environments lies between 79% and 91% on 4 m wide voxels, demonstrating the potential of the proposed method for building 3D maps for wide urban areas.

3D maps of urban environments are useful in various fields, from cellular network planning to urban planning and climatology. We show that 3D urban maps can be extracted from received satellite signals that are attenuated by obstacles, such as buildings, from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips. Our proposal incorporates position inaccuracies in the calculations, and the diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios. Our results show that the generalization accuracy for a Random Forest classifier lies between 79% and 91%, demonstrating the potential of the proposed method for building 3D maps for wide urban areas. In the demo, we show multiple 3D visualizations of the various processing stages, which can be viewed interactively using Google Earth, allowing hands-on exploration of the work.

Bicycle-to-bicycle (Bi2Bi) communication can be implemented by well-established technologies in the 2.4GHz ISM band: IEEE 802.11, Bluetooth or IEEE 802.15.4. These technologies have distinct performance due to different physical and data link layers. In this paper, we characterize the mentioned 2.4 GHz-operating technologies over opportunistic links established between bicycles using commodity hardware. We find that, in Bi2Bi links, Blue-tooth, IEEE 802.11 at 24 Mbit/s, and IEEE 802.11 with automatic rate adaptation can communicate only in the immediate surroundings (under 15m of range), to maxima of 1.5 Mbit/s, 17 Mbit/s and 25 Mbit/s, respectively. IEEE 802.15.4 and IEEE 802.11 at 1 Mbit/s sustain connectivity up to 30 and 40 meters and peak transfer rates of 50 kbit/s and 800 kbit/s respectively. In addition, we observed that, in all measurement scenarios, link performance depended strongly on whether bicycles were approaching or moving away, rather than on whether one was at the front or back of the other.

With the rise in popularity of the Internet of Things in all kinds of different application scenarios, various middleware solutions have appeared with different use-cases and optimizations in mind. The design space for any specific deployment is thus increasingly large, but little objective support exists to help choose the best middleware for each use-case. From this stems the need to evaluate how different IoT middleware solutions perform in different use-cases. Measuring the performance of IoT middleware in a way that 1) provides common ground among experiments, and 2) makes it easier to integrate new IoT middleware in the benchmark is not straightforward. In this paper, we propose a generic architecture for comparing the performance of publish/subscribe middleware, develop a tool that implements this architecture, and show the benefits in time and effort that can be reaped from our approach. We further validate our approach by using the architecture and tool to benchmark different middleware solutions, taking lessons from the changes necessary to support new middleware, and attempting to quantify the effort through lines of code and to qualitatively assess code structure similarity.

Smart cities aim to improve the citizens' quality of life by leveraging information about urban scale processes extracted from heterogeneous data sources collected on citywide deployments. The Internet-of-Things (IoT) is, thus, the enabler of smart city technologies at urban scale. In this paper, we present PortoLivingLab, a multisource sensing infrastructure that leverages IoT technology to achieve city-scale sensing of four phenomena: weather, environment, public transport, and people flows. To sense these processes on a city scale, we deployed a vehicular network with over 600 vehicles and 19 static environmental sensors. We also developed an easily reconfigurable crowdsensing platform and carried out several crowdsensing campaigns with more than 600 participants. The data is collected in a common backend and stored using similar spatio-temporal data models to simplify sharing and joint analysis for the characterization of urban dynamics. We describe the architecture and composing elements of PortoLivingLab, highlighting the IoT technologies, and challenges faced. We present several proof-of-concept use cases (e.g., passenger flows from WiFi connections) that provide new insights into different components of an evolving and moving city. Finally, we lay out the future lines of work that will strive for finding hidden phenomena by leveraging data from the three complementary platforms.

Traffic congestion adversely impacts our lives. Traffic estimation resorting to mobile (crowdsensing) probes is a challenging task. We present key challenges for accurate and real-time traffic estimation resorting to crowdsensing data, namely data sparsity, user trip diversity, population bias, data quality, among others. We propose solutions to address some of these issues and demonstrate the relevance of others through an exploratory data analysis.

Middleware is being extensively used in Internet of Things (IoT) deployments and is available in a variety of flavors - from general-purpose community-driven middleware and telco-developed Machine-to-Machine (M2M) middleware to middleware targeting specific deployments. Despite this extensive use and diversity, little is known about the benefits, disadvantages, and performance of each middleware platform and how the different platforms compare with each other. This comparison is especially relevant to help the design and dimensioning of IoT infrastructure. In this paper, we propose a set of qualitative dimensions and quantitative metrics that can be used for bench-marking IoT middleware. We use the publication-subscription of a large dataset as use case inspired by a smart city scenario to compare two middleware platforms. The methodology enables us to systematically compare the two middleware platforms. Further, we are able to use our approach to identify inefficiencies in implementations and to characterize performance variations throughout the day, showing that the metrics may also be used for monitoring.

The Internet of Things (IoT) emerges as a myriad of devices and services that interact to build complex distributed applications. Interoperability and standardization are imperative for the realization of this vision. Machine-to-machine (M2M) communications standards can be the middleware that glues together the IoT. However, standards are highly complex and require a large amount of interpretation, deployments are currently scarce, and performance evaluations simplistic or speculative. In this paper, we focus on the experimental evaluation of latency in IoT service composition with mobile gateways (GWs). We measure latency between system components and quantify application protocol overheads to assess the capabilities and limitations of a standard M2M middleware. We designed and implemented a mobile e-health use case on top of ETSI M2M and openEHR standards. We ran a pilot remote monitoring ten people for three weeks, collecting nearly 480 h of data. Our results show that while the latency added by a broker lies around 25 ms, the cellular network often exceeds 1 s, becoming a problem for interactive applications. Moreover, we observe that latencies between a smartphone GW and cloud hosted services vary largely depending on the user mobility, and on the promotion delay of the used wireless network.

Smartphones, with vast connectivity and sensing capabilities, are the natural choice to serve as gateways for physiologic sensors and body area networks. Machine-to-Machine (M2M) middleware standards are driving the emergence of Internet of Things (IoT) applications by providing autonomic interoperability. However, standards remain opaque and difficult to interpret, throttling implementation. In this work, we report the design and development of an ETSI M2M Gateway (GW) on a mobile device, instantiated in a smartphone. We describe our reasoning in interpreting the standard for the varied implementation challenges faced. Further, we develop libraries to ease the deployment of IoT applications using the ETSI M2M ecosystem with reduced development costs. Finally, we validated the implementation with a mobile e-health pilot with 10 participants during 3 weeks.

A large-scale urban sensing platform, composed of multiple Data Collection Units (DCUs) equipped with sensor hardware scattered accross the city, allows pervasive monitoring of environmental parameters. Gathering sensor data from a number of disparate locations at a backend server can be supported by delay-tolerant services provided by existing vehicular networks. Our real-world sensing platform takes advantage of an existing vehicular network with more than 400 vehicles equipped with On-Board Units (OBUs). A purposely-developed implementation of a delay tolerant service is installed in all elements involved in the communication flow, from DCUs to the backend server. In this demo, we showcase the full end-to-end data flow with the actual equipment being used in our real-world deployment. Data produced at a DCU is collected by an OBU installed in a vehicle and delivered to a Road-Side Unit (RSU), which then forwards the data to the backend server.

The experience of daily stress among bus drivers has shown to affect physical and psychological health, and can impact driving behavior and overall road safety. Although previous research consistently supports these findings, little attention has been dedicated to the design of a stress detection method able to synchronize physiological and psychological stress responses of public bus drivers in their day-to-day routine work. To overcome this limitation, we propose a mobile sensing approach to detect georeferenced stress responses and facilitate memory recall of the stressful situations. Data were collected among public bus drivers in the city of Porto, Portugal (145 h, 36 bus drivers, +2300 km), and results supported the validation of our approach among this population and allowed us to determine specific stressor categories within certain areas of the city. Furthermore, data collected throughout the city allowed us to produce a citywide “stress map” that can be used for spotting areas in need of local authority intervention. The enriching findings suggest that our system can be a promising tool to support applied occupational health interventions for public bus drivers and guide authorities' interventions to improve these aspects in “future” cities.

E-health has raised a great deal of expectations on improving the quality of health services while simultaneously enabling health services cost reductions. To advance towards those visions, it is imperative to gain the trust of the involved stakeholders, doctors and other medical personnel, patients, families, health care providers and regulators. Even though one critical requirement is interoperability among the various systems involved, currently existing solutions are still vertical silos to a large extent. In this paper, we present an E-health solution that results from the integration of components that comply with rising standards at the various levels of the ICT infrastructure: Machine-to-Machine (M2M) communications for interconnecting devices and services, Health Level 7 (HL7) for communicating with health platforms and openEHR for data semantics, storing and making data available. Concretely, we provide an interoperable and extensible e-health service following these three uprising standards and present the architecture design. We map the service to the various components of the infrastructure building blocks, thus demonstrating how the integration can be successfully accomplished. We are currently developing a prototype solution to be used in a pilot project with 15 elders.

Many wireless applications require a propagation model that describes the attenuation of the transmitted signal as a function of the distance between devices. Such channel models are derived commonly from signal strength measurements, and assume that the true distances between wireless terminals are known. In practice, however, the true distances may be unavailable or difficult to obtain, for instance in mobile scenarios or in the absence of line-of-sight. These conditions typically occur in forested environments, urban areas, etc. This paper addresses the problem of path loss model parameter estimation in presence of erroneous distance measurements, such as the ones derived from the GPS positions. We provide a model for the uncertainties, and study the impact of distance errors on the estimation of a log-distance channel model. Our main conclusion is that the path loss model can be estimated with a reasonable accuracy from unreliable distances, provided that the measurements are taken at distances beyond a few standard deviations of the GPS positioning error. In case the maximum communication range does not allow such large distances, we provide a method to correct the erroneous channel model. Real-world measurements are used in order to validate our approach.

Vehicular Ad-hoc Networks (VANETs) are a promising approach to increase road safety, turning VANETs into critical infrastructures in the near future. Recently, it was shown that Radio Frequency (RF) jamming has a disruptive effect in VANET communications in ranges of several hundred meters in open space areas and up to 50 m around intersections in constructed areas. Hence, it is critical to study the potential disruptive impact of RF jamming in VANET applications and to research how to detect and mitigate it to ensure security at the lower layers of the protocol stack. In this paper, we recreate outdoor measurements of VANET communications under the influence of RF jamming in NS-3 simulations in an attempt to generalize the use of previous results to study the impact of RF jamming in large-scale VANET applications. Extending a previously existing simulation model for wireless jamming, we are able to reproduce in simulations the results obtained in outdoor measurements. Thus, the simulation model that we validate in this article provides a useful tool to study the impact of RF jamming in VANET applications, as well as cooperative detection and mitigation techniques.

In our previous work, we proposed to use a vehicle network for data gathering, i.e. as an urban sensor. In this paper, we aim at understanding the theoretical limits of data gathering in a time slotted wireless network in terms of maximum service rate per node and end to end packet delivery ratio. The capacity of wireless networks has been widely studied and boundaries for that capacity expressed in Bachmann-Landau notation [1]. But these asymptotic limits do not clarify the numeric limits on data packets that can be carried by a wireless network. In this paper, we calculate the maximum data that each node can generate before saturating the network. The expected number of collision and its effect of the PDR% and service rate are investigated. The results quantify the trade off between packet delivery rate and service rate. Finally, we verify our analytical results by simulating the same scenario.

This paper seeks to understand and characterize policies that exploit multiple available technologies and/or heterogeneous communication routes simultaneously with the goal of improving throughput and energy performance or economical costs in a converged networks scenarion. We present an optimization framework and a set of allocation policies to provide efficient, channel-aware load allocation for multi-homed devices under different cost criteria. Network coding is used as a key enabler of these techniques as coding across packets requires less coordination, simpler and less frequent feedback mechanisms, and higher resiliency to changes in the transmission channel. Our formulation incorporates bursty erasure channels, multiple simultaneous communication routes, and different channel coding and modulations available to each technology as part of the resource allocation optimization. Numerical results are provided showing that dynamic allocation policies are instrumental to improving resource efficiency by reducing energy consumption and/or channel utilization. The energy gains of our proposed policies outperform the best fixed network coding multi-homing policies by a factor of 2 in some scenarios.

Public transport networks (PTNs) are difficult to use when the user is unfamiliar with the area she is traveling to, as shown by a user survey that we present in this paper. This is true for both infrequent users (including visitors) and regular users who need to travel to areas with which they are not acquainted. In these situations, adequate on-trip navigation information can substantially ease the use of public transportation and be the driving factor in motivating travelers to prefer it over other modes of transportation. However, estimating the localization of a user is not trivial, although it is critical for providing relevant information. In this paper, we propose the use of an electronic ticketing infrastructure of a PTN operator for positioning within the context of the PTN to give on-trip personalized navigation cues. To our knowledge, this is an innovative contribution that has not been described or deployed, to date, elsewhere. We assess relevant design issues for a modular cost-efficient user-friendly on-trip navigation service that uses position sensors and present the details of a proof-of-concept prototype running in our laboratory. We also present and analyze the results of a user survey on the usefulness of the service and its acceptance by users.

Channel-adaptive techniques increase the efficiency of wireless communications, as they are efficient in coping with the quality variation of the wireless channels. However, channel-adaptive mechanisms require a prediction of the future behaviour of the channel. Heuristics are an important alternative to more complex predictors in WLAN scenarios. In this paper we use WLAN measurement traces for the simulative evaluation of the influence of channel prediction errors in the performance of a threshold-based adaptive modulation scheme, considering also the case of delayed channel feedback. The results show that, as long as the necessary prediction horizon does not exceed 2 ms, assuming the channel to stay constant leads to less than 15% capacity loss compared to the case when perfect channel prediction is used. When farther prediction horizons are needed, the moving average of the received signal should be used for channel prediction.

Impairments in wireless data communication due to time and location dependent errors can be overcome by using channel-adaptive techniques, like channel-aware scheduling or adaptive modulation. These techniques require information about channel behaviour obtained from channel predictors. Unfortunately enough the accuracy of predictors usually suggested in the literature has not been confirmed and comparative studies do not exist (with one exception). In addition, existing predictors are frequently difficult to compute and sensitive to numerous parameters, e.g. noise and channel variability. In this paper, we investigate and compare the performance accuracy of three simple heuristics for channel prediction. Our investigation includes a comparison with a verified reference predictor using Rayleigh channels and measured WLAN traces. Our study indicates the usefulness of the investigated heuristics for fixed systems and pedestrian speed mobility.

Following widespread availability of wireless Internet, a wide range of applications with differing QoS requirements must share the wireless access. The objective of this work is to provide QoS support to different applications delivered over a wireless link shared by different users. We propose a link-layer packet scheduler following the cross layer concept: the channel-aware scheduling approach is extended by information about the importance of individual packets for the application. For this purpose we use utility curves, which map network service delivered to a given data flow to application-perceived quality. We define a single scheduling metric which expresses, for each packet, the balance between the quality increase of the flow served and the quality loss of the flows that are not. We evaluated our concept through extensive simulations with mixes of VoIP and file download flows. The results show that our maxsum utility-based scheduler clearly improves the quality seen by the VoIP flows when compared to a channel-aware round robin (caRR)/proportional fair scheduler (PFS), respectively. Further, this is achieved while delivering similar amount of data to file download users. We also show how different forms of the utility curves can be used to achieve differentiated QoS provisioning to users/applications with different priorities/ requirements