Unmanned Aerial Vehicles enable a wide range of applications, including search and rescue, environmental monitoring, and disaster response. These aerial platforms can form dynamic flying ad hoc networks to support both sensing and data communication. In particular, UAVs can establish a resilient communication backbone that adapts to varying propagation conditions and fluctuating traffic demands. However, maintaining reliable performance in such highly dynamic and unpredictable environments remains a critical challenge. This work investigates online link quality estimation by autonomous agents, with a focus on real-time detection of link model changes (e.g., due to mobility or interference) through model drift. Building on this, we propose and evaluate adaptive formation control strategies that adjust UAV placement to optimize the network’s Packet Delivery Ratio. Simulation results demonstrate that the proposed optimal placement strategy significantly outperforms baseline approaches for line-based UAV networks.

Accurate channel modelling is essential for enhancing overwater communications in dynamic propagation environments, but it is hindered by variable and noisy measurement data that cause uncertainty in large-scale propagation models. This work examines data uncertainty in overwater shore-to-shore WiFi communications and explores strategies to reduce it. We show that local principal curves allow representing empirical data with lower error than conventional methods. Uncertainty and sensitivity analysis provided insights into regions (water vs. mud) and periods (rising tide) with high variability in model predictions, and the most influential parameters (antenna heights). Parameter calibration effectively mitigates the impact of input parameter uncertainty.

Wireless communications increasingly enable ubiquitous connectivity for a large number of nodes, applications and scenarios. One of the less explored scenarios is aquatic communications, specially when considering near-shore and short-range communications. Overwater communications are impaired by a number of distinguishing dynamic factors, such as tides, waves or node mobility, that lead to a widely fluctuating and unpredictable channel. In this work, we empirically characterize near-shore, overwater channels at 2.4 GHz under realistic conditions, including tidal variations, and relatively short TX-RX separations. To this end, we conducted experiments in a coastal estuarine region and on a harbor to characterize Shore-to-Shore (S2S) and Shore-to-Vessel (S2V) communication channels, respectively, and to identify major factors impairing communication in such scenarios. The empirical results show that constructive/destructive interference patterns, varying reflecting surface, and node mobility (i.e. travel direction and particular maneuvers) have a relevant and noticeable impact on the received signal strength. Thus, a set of parameters should be simultaneously considered for improving the performance of communication systems supporting S2S and S2V links, namely tidal variations, reflection surface changes, antenna height, TX-RX alignment and TX-RX separation. The results useful provide insights into realistic S2S and S2V link design and operation.

This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design . To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering . Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector , closeness , betweenness , and degree . Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods.

Electric mobility with all of its advantages has gained momentum during the last decade with increasing utilization by many sectors of the society. However, professional fleets’ operators (e.g. taxis) are still conservative in switching to this new mobility paradigm in many parts of the world. In this paper, we empirically evaluate whether electric vehicles together with conventional charging stations could replace current internal combustion engine vehicles for taxi mobility and study the implications for the taxi business. To perform this study, we resort to a detailed mobility dataset of a taxi fleet collected in a mid-sized European city. The results provide a first indication that such transition towards electric mobility is feasible for the vast majority of the vehicles of the fleet and that simple AC chargers at taxi stands could suffice to provide the necessary range autonomy.

The overtaking maneuver can be often dangerous and risky, especially when driving in rural roads, with a significant probability of leading to fatalities and serious injuries. Vehicle communication emerges as one of the leading technologies to improve the driving experience and road safety. This article presents a system designed to augment road safety in an Over-taking Maneuver, providing the ahead vehicle's Line-of-Sight by real-time video-streaming. It uses the C-V2X Transmission Mode 4 that, building upon cellular technology, has evolved as one of the main standards for vehicle communication. Results show an effective application of the system in real overtaking scenarios, enhancing the safety and proving its usefulness. Whilst having some impact on the latency, the solution incurred in low packet losses and provided good video quality.

This paper proposes the application of market-based mechanisms to establish cooperative behavior within traffic scenarios involving autonomous vehicles. The aim is to understand the suitability of commonly used auction rules as a potential mechanism for tactical-level collective decision-making in platoon applications considering near-reliable communications. As a proof of feasibility, two auction clearing rules are compared, namely the first-price and the second-price sealed bid auctions. Both rules are tested according to their impact on coalition welfare and communication. To realistically assess the system performance, we designed an integrated simulation platform composed of an agent-based platform, a microscopic traffic model, and a vehicular network simulator. Results show the viability of two rules to maintain high satisfaction among platoon members, that can lead to stable formations and consequently better traffic conditions. From a communication perspective, a non-negligible delay is present, and should be taken into account when implementing an auctioning mechanism for real-world deployment.

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.

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.

Despite impressive developments in automated driving technology, several technical, economic and social challenges hinder the large-scale deployment of highly or full automated vehicles. We present teleoperated driving - where in-car drivers are replaced by tele-drivers located at a control center- as a transient technology to enable a driverless, door-to-door taxi service. In this novel service, the transmission of video and audio streams of the vehicle surroundings via wireless networks to the taxi dispatch center allows a human operator to remotely sense the environment through a virtual windshield and to remotely operate the vehicle controls through an emulated cockpit. This safe and cost-effective transport service merges together aspects of taxi transport with car sharing services if the passenger drives part of the route. A large-scale empirical evaluation study proves the feasibility of this novel taxi operation mode and shows that the implementation of the system can reduce, on average, the number of drivers to between 15% and 27% when considering teleoperation during pickup/dropoff and service, respectively. A premium service where passengers are remotely also driven from their origin to the destination also presents considerable gains for taxi operators. Teleoperation of taxi fleets could revolutionize urban mobility by offering a cost-effective and safe door-to-door transportation service.

Despite impressive developments in autonomous driving technology, little work has been devoted to improving the mobility intelligence of self-driving vehicles. Intelligent transport mobility, where cars autonomously or collaborative take mobility decisions, will be of major importance for future transportation systems. Through a large-scale data analysis, we empirically evaluate the patterns of high performing taxi drivers and taxi stands. We also empirically show that advanced vehicle positioning can be beneficial for taxi operators by quantifying the impact of a taxi stand selection system that uses knowledge of passenger demand. Results show that a judicious selection of taxi stands allows decreasing the idle time and travelled distance, and improving the hourly revenue. The implementation of the system allows decreasing the operators' operational expenditures and improving profitability.

Vehicular Networks enable a vast number of innovative applications, which rely on the efficient exchange of information between vehicles. However, efficient and reliable data dissemination is a particularly challenging task in the context of vehicular networks due to the underlying properties of these networks, limited availability of network infrastructure and variable penetration rates for distinct communication technologies. This paper presents a novel system and mechanism for information collection and dissemination based on virtual infrastructure selection in combination with multiple communication technologies. The system has been evaluated using a simulation framework, involving network simulation in conjugation with realistic vehicular mobility traces. Simulation results show the feasibility of the proposed mechanism to achieve maximum message penetration in a geographical area with reduced overhead. The judicious vehicle selection also enables scalable data collection and leads to improved network utilization through the offload of traffic to the short-range communication network.

Parking is a major problem of car transportation, with important implications in traffic congestion and urban landscape. Reducing the space needed to park cars has led to the development of fully automated and mechanical parking systems. These systems are, however, limitedly deployed because of their construction and maintenance costs. Leveraging on semi and fully-autonomous vehicular technology, as well as on the electric propulsion paradigm and in vehicular ad hoc networking, we propose a new parking concept where the mobility of parked vehicles is managed by a parking lot controller to create space for cars entering or exiting the parking lot, in a collaborative manner. We show that the space needed to park such vehicles can be reduced to half the space needed with conventional parking lot designs. We also show that the total travelled distance of vehicles in this new parking lot paradigm can be 30% less than in conventional parking lots. Our proposal can have important consequences in parking costs and in urban landscape.

Vehicular networks will enable a number of active safety and traffic efficiency applications. At the core of many of those applications is cooperative awareness: the ability to detect location, speed, and heading of surrounding vehicles. We empirically analyze three key metrics that shed light on the communication performance available to applications: Packet Delivery Ratio: link quality in terms of the proportion of received messages over distance; Neighborhood Awareness Ratio: the proportion of detected neighbors within a given distance, which serves as an indicator of the effectiveness of cooperative awareness message exchange; and Neighborhood Interference Ratio: the proportion of neighbors above the desired range of interest, which can provide insight into the interference levels of fully deployed systems. By analyzing the measurement data collected within the scope of the DRIVE-C2X project, we conclude that the link layer delivery and neighborhood awareness criterion can be fulfilled for safety applications: in the analyzed datasets, the cooperative awareness ratio is close to 100% up to 100 meters. Depending on the desired region of interest, the interference from far-away vehicles can be considerable, thus requiring effective congestion control to balance between neighborhood awareness and interference.

Road transportation is one of the main sources of greenhouse gas emissions, which lead to global warming and climate change. Promoting the decarbonization of this sector through more efficient and greener mobility is a challenging task that can be achieved by intelligent transportation systems (ITS) enabled by vehicular communications. In this paper, we briefly present how mobility players and enablers (driver, vehicle, and road network) influence energy consumption and pollutant emissions. Furthermore, this survey paper details how different ITS, ranging from eco-routing to intelligent intersection management, can lead to sustainable mobility by promoting a more efficient vehicle usage and enhanced efficiency on road network utilization. Results shown that ITS has the potential to considerably reduce fuel consumption and pollutant emissions, namely, by smoothing the traffic flow, reducing the number of start–stops, and reducing the total travel distance. In addition, we present and analyze two main methods for evaluating green transportation systems: 1) field operational tests; and 2) simulation-based evaluations. We give special emphasis to simulation-based assessment of Green ITS measures by detailing the necessary models and their interactions. Finally, we propose a number of recommendations and future research directions in the area of Green ITS.

Considering that the transport sector is responsible for an increasingly important share of current environmental problems, we look at Intelligent Transportation Systems (ITS) as a feasible means of helping in solving this issue. In particular, we evaluate the impact in terms of Carbon Dioxide (CO2)emissions of Virtual Traffic Light (VTL), which is a recently proposed infrastructureless traffic control system solely based on Vehicle-to-Vehicle (V2V) communication. Our evaluation uses a real-city scenario in a complex simulation framework, involving microscopic traffic, wireless communication, and emission models. Compared with an approximation of the physical traffic light system deployed in the city, our results show a significant reduction on CO2emissions when using VTLs, reaching nearly 20% under high-density traffic.

Simulation of vehicular networks has been extensively studied in the last few years. In order to have an holistic view of the network functioning, communications and vehicle mobility aspects should be modeled in detail and fully integrated. This article presents a tool for simulating heterogeneous vehicular networks. The existing microscopic traffic simulator, DIVERT, has been extended by adding NS-3 support resulting in a very tightly integrated simulator. The feasibility of the approach has been tested by means of an application example: in-vehicle traffic lights. The provided results demonstrate the superior performance and the scalability of this novel simulator.

Self-optimisation will enable future wireless networks to manage themselves in a continuous and independent way. By dynamically adjusting relevant radio parameters during network operation a system that adapts to environmental changes can be created. This paper presents a novel self-optimisation algorithm based on Fuzzy Logic Controlling. Taking into consideration measurements gathered from the network, the Fuzzy Logic Controller balances continuously the individual link gain and the network gain. Results have shown the feasibility of the proposed approach, superior improvements on network performance and a capacity increase of 23%. It is envisioned that self-optimisation will lead to significant reductions in capital and operational expenditures by maximizing the utilisation of the air interface.

Dynamic optimization of UMTS systems has been gaining a growing interest by the research community. In this context, the current paper concentrates on dynamic automated tuning of RRM parameters to detect whether the uplink or the downlink is the limiting link and force a reconfiguration to balance both and maximize capacity. Modifications of the soft handover algorithm are revealed as a feasible solution to achieve this objective. A three blocks based functional architecture is described to adapt parameters to service mix dynamics and overcome capacity problems. Conducted tests show the feasibility of the approach. Effective adaptation to changes in traffic patterns and significant capacity gains are obtained.