Collective Perception Messages (CPMs), defined by European Telecommunications Standards Institute (ETSI), enable vehicles and roadside infrastructure to exchange information about detected objects, enhancing situational awareness in cooperative environments. However, as the size of CPMs increases — particularly in dense traffic scenarios — the wireless channel can become saturated, leading to delays in transmission and reduced packet delivery ratios. This paper starts by assessing how the number of objects included per CPM impacts communication performance, highlighting the necessity for effective object selection strategies during periods of congestion. To address this issue, we propose a lightweight, real-time object prioritization algorithm based on deviations from the predicted path. Our method estimates each object’s expected state based on its last transmission, and prioritizes those whose current state deviates most from this prediction, as these are likely to be more informative. The evaluation uses a real-world dataset and demonstrates that our strategy significantly improves predictive accuracy by at least 7%. Moreover, the algorithm does not increase CPU or memory usage, demonstrating similar resource consumption compared to the method described in the Collective Perception Service (CPS) standard, making it well-suited for embedded platforms. These results confirm that Prediction–Deviation selection can enhance the efficiency and informativeness of CPMs, especially when the message size must be constrained due to network congestion.

The rapid expansion of vehicular networks is expected to cause significant congestion in communication channels, potentially compromising the reliability of safety and traffic management systems. To mitigate congestion, the European Telecommunications Standards Institute (ETSI) developed the Decentralized Congestion Control (DCC) mechanism, which dynamically adjusts transmission parameters based on real-time channel conditions. However, real-time DCC data may not always be available due to hardware limitations. This paper proposes a new approach to estimate the Channel Busy Ratio (CBR) in ITS-G5 vehicular networks through passive channel observation. Using real hardware, we compare the proposed estimator with measurements from the wireless card, showing a maximum error below 5.6%. We also analyze the impact of standard vehicular messages and IP traffic on network congestion. The estimator offers accurate, non-intrusive CBR measurements, supporting congestion control where direct measurement is impractical.

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.

Bandwidth estimation plays a crucial role in maintaining Quality of Service (QoS) in wireless networks. While active estimators can be more accurate, passive methods minimize the network overhead and avoid potential interference. To the best of our knowledge, there is no passive estimator for heterogeneous networks that preserves the network performance when adding a new communication flow, while calculating the estimated available bandwidth without synchronization, additional hardware, or non-local information.This paper introduces two passive bandwidth estimation approaches: one leverages only local interface measurements; the other also incorporates historical transmission rates from the network. The results demonstrate that the former enables a conservative estimation of available bandwidth, effectively preserving the performance of existing flows on all types of networks. More accurate estimations can be achieved with the latter, but only on homogeneous networks. We show that our approaches can match or outperform widely accepted available bandwidth estimation methods that rely on active probing.

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.

Abstract Spontaneous networks may need to establish connections with already-in-place devices and their resources, irrespectively of their capabilities, for example, in emergency scenarios. Interoperability and integration are a key issue when combining multiple wireless networks and heterogeneous devices. To address this issue, this article extends the SDN concept, and presents a new hybrid network architecture that combines legacy devices in a wireless data plane, therefore increasing the range, and making the network more efficient, dynamic and programmable. In this article, we propose an optimized adaptive routing approach in a network architecture that joins SDN-capable devices with legacy devices for emergency scenarios. This solution takes into consideration the impact that a flow can have in the network, therefore minimizing its channel interference, and to a certain degree, balancing the load in the network’s devices. In this work, the importance of the network and service parameters is optimized through machine learning for each flow’s data rate, and then feed into the routing approach to determine the best hybrid legacy and SDN-based path. Results show that the new architecture can successfully integrate the legacy devices in the wireless software defined data plane, and utilize them to increase the performance. The combination of delivery ratio and delay are always relevant to every data rate. The link stability is more relevant for lower data rates, while the break probability is more relevant for higher data rates. Moreover, the number of hops and possible interference combined have a high importance in every data rates, and the nodes’ speed knowledge can have a good impact on the network performance.

In the last years there has been a strong effort in the development of Intelligent Transportation System (ITS)-based solutions, leading to an important change in the way that drivers and other road users become aware of the surroundings. The development of Cooperative-ITS, which utilises direct wireless short-range connections, is integrating cellular networks as well (4G and 5G), allowing the growing use of the road users smartphones to provide real-time information about Vulnerable Road Users (VRUs), like pedestrians and cyclists. Such increase is becoming a serious concern, since every VRU is likely to have one smartphone, which may lead to scalability and latency issues. This work presents an approach for a microservices-based application, targeting the always critical VRU safety use-case, in a multi-site scenario, using real road infrastructure Multi-Access Edge Computing (MEC) and mobile network provider cloud computing. The main novelty of this work is the multiple approaches on the deployment of the required microservices, and several scenarios that have been tested, and the investigation of the best approach to minimize the service-level latency of the safety application. The results show the potential of microservices distribution through the edge and cloud, with a strong impact on improving the efficiency of ITS. Depending on the services’ location, the latency of the VRU and vehicle’s notification is deeply affected, but using a federated scenario we are able to keep the VRU’s notification delay around the 200 ms, with better results being achieved if a closer mobile network provider cloud platform is used. The results present a noticeable advancement in the development of more scalable and operational solutions that work on improving ITS, with a focus on microservices and edge computing to minimize the delay of critical applications.

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.

Abstract Considering that one of the goals of the future network generations is to provide ubiquitous communication in the most diverse scenarios to achieve high connection coverage, it is foreseen that the use of unmanned aerial vehicles as flying base stations (UAV-BSs) can potentially extend the network and communication range. UAVs as flying base station can bring the potential to assist user devices and vehicles by carrying communication resources that can accommodate clients that were not previously planned by the ground infrastructure design due to flash crowd events, sudden natural disasters, or any other event that let to an overloaded environment. Allocating UAVs as flying base station still poses significant challenges in their deployment and the effectiveness of information transmission through UAVs as flying base station in the context of wireless communication since it is necessary to deal with both wireless communication capability and interference in the presence of terrestrial infrastructures already present. Besides, it is essential to understand how communication resources affect network performance. This paper studies the feasibility of using UAVs as flying base station in the assistance of wireless communication in a scenario where there is a sudden demand for data transmission due to possible congestion of local infrastructure. We show how the number of communication resources provided by the UAV-BS, the interference caused by the presence of multiple next generation node Bs (gNBs), and the UAV as flying base station positioning affect the network performance. We also highlight the need for a better next generation node B (gNB) and UAVs placement criteria since the received signal power prevents the user equipments (UEs) from using most of the available resources.

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.

This work presents a LoRa channel access strategy that allows a single-channel high-speed moving LoRa gate-way to discover and receive sensing data from end devices installed in the railroad. This medium access is based on control messages to coordinate the communication between the mobile gateway and static sensors. The proposed approach is tested in a real mobile environment and compared with the well established LoRaWAN protocol. The results show that our solution outperforms LoRaWAN both in packet error rate and throughput, revealing the differences between a non-awareness solution such as LoRaWAN, and a solution where the end node only transmits when receiving a control message alerting that a LoRa gateway is nearby.

With the evolution of communication and sensing devices, in addition to connected and autonomous vehicles, real-time information about the status of the roads can become a reality. This paper presents a proposal of a Collective Perception Service (CPS), a system defined by the European Telecommunications Standards Institute (ETSI), which aims at enabling Intelligent Transport Systems (ITSs) to cooperate and exchange information about road users, obstacles on the road, and sensing information, through Collective Perception Messages (CPMs). The proposed system is able to join the information of the communication messages and the data from the different sensors, such as LiDAR, radars, and video cameras. The CPS is evaluated and tested in real-world scenarios in a connected city infrastructure. Results show that CPS allows Intelligent Transport System Stations (ITS-Ss) to cooperate and exchange information regarding perceived objects in a timely manner. A temporal analysis outlined the influence of the message size on the CPM dissemination process and the need for a mechanism that decides if specific information shall be sent in the CPM. Finally, the scalability tests showed the effect of network congestion, testing the system’s limits in the selected hardware.

Mobile networks have proved to be an attractive solution to support emergency services, due to their inherent mobility, which requires flexible and yet efficient communication. However, the management of resources in slices in a 5G network is still made static and with no mobility information and prediction. This paper proposes the integration of a 5G network platform and a city mobility network, which allows 5G network resources to be allocated dynamically and in advance according to the users mobility and their needs. This mobility and network integration is important for different types of services, from infotainment to emergency. The prediction of the mobility and location through the mobility network provides the knowledge for a 5G cellular network to dynamically reserve the required network slicing resources. This approach has been tested in real road scenarios and vehicles covered by 5G. The results show that the solution guarantees the required resources, reserved in a proactive and predictive approach, both to optimize the resources of one base station, and to optimize resources between base stations, offering the required quality to the services.

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.

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.

This work characterizes the influence of different spatial sampling resolutions on the prediction of vehicular mobility. By assuming different spatial quantization areas over a region the vehicles move through, we characterize the distribution of trajectories for a fixed number of samples using the data available in a dataset of GPS positions sampled in an urban scenario. Both spatial resolution and the number of GPS samples per trajectory are analyzed, concluding that similar distributions of the trajectories can be obtained when the unitary dimension of the spatial area is approximately 0.05 km2and the trajectories last approximately 5 minutes. This is of particular importance to adequate the spatio-temporal sampling variables to the dynamics of the vehicular motion, thus avoiding over-sampling or sub-sampling in the spatial and temporal domains. The paper also proposes a deep-learning approach based on recurrent neural networks to predict future positions of a vehicular trajectory, showing the influence of spatial sampling to predict single and multiple future positions of the trajectory. The accuracy and the computation time of the prediction process are evaluated, showing how the magnitude of the prediction error is influenced by the adopted spatial sampling resolutions.

Wireless networking is expected to sustain the direct interaction between personal users’ devices, and to provide connectivity on large-scale resource-constrained devices. However, conventional networking protocols fail in large scale mobile wireless environments, due to node mobility, dynamic topologies, and intermittent connectivity. Information-Centric Networking (ICN) has been considered the most promising candidate to overcome the drawbacks of host-centric architectures where Named Data Networking (NDN) is one of the well-known and studied architectures within the ICN paradigm. The main objective of this work is to improve both content availability and network performance in mobile environments regarding the ICN paradigm. This is provided through a context-based approach for the caching admission policy providing in-network caching and content replication, facilitating the efficient and timely delivery of information. Content popularity, freshness, proximity, source mobility type and network density are some of the metrics considered in the caching decision. We conducted a comparative study between our proposal and the NDN caching strategy by using two different datasets with real mobility and connectivity traces, addressing intermittent communication. According to our results, we observed that using a multi-criteria context-based cache admission policy improves cache hits, cache evictions, and request satisfaction ratios in mobile environments, thus improving content delivery and network efficiency.

In recent years, the Internet of Things (IoT) has become a standard facet of modern communications, and information-centric networks have been pointed as an alternative to bypass the restrictions imposed by the traditional Internet protocol networks regarding the mobility of its network elements. However, the improvements imposed by this new paradigm fall short in large scale mobile wireless distributed environments inherent to IoT, due to high node mobility, dynamic topologies and intermittent connectivity. To tackle these issues, we present a named data network (NDN)-based publish–subscribe mechanism with support for both Consumer and Producer mobility. This approach handles the Producer mobility by combining the Data packets with infrastructure specific information, fixing the broken paths between the Producer and the Consumer; and the Consumer mobility by monitoring and anticipating mobile node trajectories while compelling the infrastructure to adjust to new paths. Simulation results, assuming a smart city use case and using real traces of vehicular mobility, have shown that the proposed solution far surpasses the native NDN workflow and traditional publish–subscribe solutions. With respect to the Producer mobility, the proposed solution delivers 79% of Data packets against 14% with the Native implementation, when using 25 mobile Producers; regarding the Consumer mobility, results have shown that our solution achieves almost the same Consumer satisfaction ratio as previous implementations but reducing substantially the network overhead related with the transmission of Interest packets.

Low-power wide-area networks (LPWANs) are probably the most promising radio access technologies for Internet-of-Things (IoT) applications. Amongst these, one of the most auspicious solutions is LoRa, a versatile technology highly compatible with urban environments, enabling long-range communications. Most of the LoRa-based medium access protocols operate under the ALOHA rationale, whose performance is known to be fairly poor. This work targets the medium access in single-channel large-scale LoRa networks, proposing a new protocol, denoted as the LoRa mode adaptive protocol (LoRa-MAP), which manages to maintain the best possible connection between end nodes and the gateway, by adapting the LoRa's physical layer parameters and making use of control packets for its coordination without violating the duty-cycle constraints of both end nodes and gateway. An analysis on different medium access schemes is conducted, aiming to perceive how different parameters and network layouts influence the coordination process. An energy expenditure analysis is conducted comparing LoRa-MAP to simpler solutions to study the impact of additional transmission/listening periods. The simulation results have shown that the proposed solution increases the LoRa network scalability, deeming it a great candidate for IoT environments.

Vehicular Networks (VANETs) are a critical component of a Smart City environment. They extended the connectivity plane with support for a wide range of applications, from safety to entertainment. Such services, when deployed outside the vehicular network, may imply an additional delay, which can be critical. In addition, these services become inaccessible whenever the vehicles lose contact with the infrastructure.This paper proposes a practical solution that aims to minimize the impact of the services’ location and its inaccessibility in a VANET. The solution focuses on using Network Function Virtualization technologies to support the deployment of the services at the edge of a mobility-enabled multihomed VANET, thus allowing the services to be accessible in intermittent connectivity situations, as well as enabling lower delays for critical services. The results obtained show that the solution is capable of deploying services at the edge of the VANET with low delay and with a fast recovery when in handover and mobility scenarios.

LoRa technology has been attesting itself as one of the most prominent and widely adopted low power wide area technologies. Highly compatible with Internet of Things (IoT) applications and urban environments, this technology enables large range communications although with small bandwidths and duty cycle restrictions. In this work, we propose a distinct way of dealing with the Medium Access Control (MAC) in LoRa, through the use of control packets to enhance the technology performance in urban city scenarios, where a large number of nodes is expected. The proposed protocol is asynchronous and takes into account the energy expenditure. We compare this scheme with the standard (LoRaWAN) by considering different network densities and packet sizes, and through different LoRa collision models. Performance results, such as network throughput and fairness index, show that, depending on the ratio between data and control packet lengths, it is possible to greatly improve the bit rate and overall network performance, even if increasing the duty-cycle restriction time due to the addition of overhead.

The growing interest in vehicular networks (VANETs) is encouraging its deployment in new environments, for both safety applications and to provide Internet to its users. Vehicles can connect to others, or to the infrastructure, providing an Internet connection. The communication equipment placed inside the vehicles may have multiple network interfaces of diverse technologies, such as IEEE 802.11p/WAVE, IEEE 802.11a/g/n (Wi-Fi) and cellular. For this purpose, multihoming and network coding can be used to provide high bandwidth and reliability to these services, reducing packet losses due to poor wireless signal quality, therefore improving the final Quality-of-Service (QoS). This work evaluates the impact of a Systematic Network Coding (SNC) approach in multihoming when the coding is applied over traffic incoming from multiple technologies, or from a single technology, depending on the entity responsible for the encoding process. The multi-technology architecture proposed in this work will be evaluated using different coding configurations (generation and buffer sizes) and by analyzing its impact on the packet loss recovery ratio. The comparative results show that the multi-technology approach has a better delivery ratio when compared to the single-technology, despite the small impact on the packet delay.

A common concern among Smart Cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of Smart Cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed in- formation for future treatment. With these challenges in mind, we are proposing self-organizing opportunistic approaches that allow vehicles to disseminate information to each other even while on the move, over a multi-technology opportunistic communication platform for data gathering and data exchange with both static and moving elements. To demonstrate their characteristics, we perform real experiments to evaluate four distinguished routing strategies, Epidemic, Direct Contact, Controlled Replication with Neighborhood Classification - Contacts based and Controlled Replication with Neighborhood Classification - Mobility based over an opportunistic vehicular network.

This work presents a V2V video transmission system to help on a car-overtaking decision using the IEEE 802.11p/WAVE communication technology. The system consists on a ffmpeg based video encoder that encapsulates the data received from a camera placed in a front vehicle into HTTP Post packets, and forwards the packet to the rear vehicle through the On Board Units (OBUs) installed on both vehicles. The rear vehicle presents the images to the driver using a visualization device. The proposed system was evaluated through real vehicular experiments in two distinct scenarios: urban and highway. Performance studies were focused on delay measurements, re- transmission rate, signal strength and bandwidth consumption related with the traveling speeds and video quality. Results show that the communication delay is higher in the highway scenario, mainly due to the distance between vehicles and the different speeds. However, promising results regarding the maximum delay and the average number of retransmissions foresee important inputs for future services of assisted-driving, in general, and car- overtaking assistance, in particular.

The advances in wireless technologies and the automotive industry lead to the appearance of Vehicular Ad-hoc NETwork (VANET). Consequently, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g. videos, ads, touristic information) dissemination for vehicular network environments. The nodes of a VANET are equipped with On-Board Units (OBUs) to enable the communication with other vehicles and to the infrastructure through Road Side Units (RSUs). OBUs enable content dissemination by the mobile nodes while RSUs are static boards deployed along the road or/and strategic locations (e.g. intersections and high buildings). This paper studies and designs how data can be efficiently disseminated using real vehicular boards (RSUs and OBUs) equipped with Dedicated Short Range Communications (DSRC) interfaces in vehicular networks. It evaluates a content distribution strategy (Local Rarest Bundle First (LRBF)) through a laboratory testbed composed by real RSUs and OBUs equipped with IEEE 802.11p (WAVE) communication interfaces. The experimentation results, performed using real vehicular boards, show that the LRBF strategy provides a high delivery of content through the vehicles, with a limited amount of resources.

In this paper we characterize the radio frequency (RF) spectrum opportunities available in a common GSM (Global System for Mobile Communications) channel to support the operation of a cognitive radio network (CRN). In a first step, we describe the technical details involved to sample the channel using a Software Defined Radio (SDR) device. Adopting a simple Energy-based detector, we identify the two energy regions where the GSM system is active or inactive. Based on the output of the detector, we show that the distribution of the durations of busy and idle periods are approximated by geometric distributions. Finally, we validate a theoretical model for the distribution of the service time. The validation results indicate that the service time can be successfully represented by a discrete Generalized Pareto (dGP) distribution, which is confirmed by the Kolmogorov-Smirnov test. Because the throughput of the CRN is represented by the inverse of the service time, the proposed analysis provides an upper bound for the networks' throughput, indicating the maximum throughput that can be attained when a single SU transmits over a GSM cellular channel. The results presented in the paper are validated with real data, confirming the accuracy of the proposed service time model.

Efficient and cost effective data collection from smart city sensors through vehicular networks is crucial for many applications, such as travel comfort, safety and urban sensing. Static and mobile sensors data can be gathered through the vehicles that will be used as data mules and, while moving, they will be able to access road side units (RSUs), and then, send the data to a server in the cloud. Therefore, it is important to research how to use opportunistic vehicular networks to forward data packets through each other in a multi-hop fashion until they reach the destination. This paper proposes a novel data forwarding algorithm for urban vehicular networks taking into consideration the rank of each vehicle, which is based on the probability to reach a road side unit. The proposed forwarding algorithm is evaluated in the mOVERS emulator considering different forwarding decisions, such as, no restriction on broadcasting packets to neighboring On-Board Units (OBUs), restriction on broadcasting by the average rank of neighboring OBUs, and the number of hops between source and destination. Results show that, by restricting the broadcast messages in the proposed algorithm, we are able to reduce the network's overhead, therefore increasing the packet delivery ratio between the sensors and the server.

Low-Power Wide Area Networks (LPWANs) are a set of technologies that are growing in the field of the Internet of Things (IoT). Due to their long range capabilities and low energy consumption, LPWAN are the ideal technologies to send small data occasionally. With their unique characteristics, LPWANs can be used in many applications and in different environments such as urban, rural and even indoor. The integration of one of the LPWAN technologies, Long Range (LoRa), in a multi-technology data gathering platform is the key objective of this work. This objective is addressed with the following proposed approaches: (1) an organization based in clusters of sensor nodes; (2) a Medium Access Control (MAC) protocol that provides reliable communications through LoRa technology; and finally, (3) a connection manager that selects which wireless technologies should be used simultaneously to forward the sensing data, according to its classification. The evaluation experiments aim to understand which type of parameters can influence the performance of the overall proposed solution, as well as the advantages of a multi-technology approach in a data gathering platform. The results in a real scenario show that the proposed approach is able to achieve simultaneous multi-technology communication while delivering different types of data with reliability and low latency.

Current applications and services for ad hoc net works heavily rely in advertisements, which are transmitted using broadcast. In the current Medium Access Control (MAC) schemes, namely in IEEE 802.11, broadcast co-exists with unicast frames. While the transmission scheme used for unicast frames is considered reliable, the IEEE 802.11 does not offer any possibility of retransmission for broadcast frames. This work presents a novel reliable MAC scheme, which is designed to maximize the network throughput. In our scheme all nodes adopt a common optimal contention window, which is based on a common view of the channel. Contrary to IEEE 802.11 we use the same contention window for both unicast and broadcast transmissions. Thus the conditional collision probability is independent of the traffic type. Departing from the optimal throughput for a saturated network, we estimate the number of nodes, which is a prime parameter to regulate the medium access control. We show that each node is suitable to estimate the number of competing nodes by using both its own medium access probability and the idle slot probability observed from the channel. Several simulation results evaluate the reliability and the throughput of our proposal, for different amounts of unicast/broadcast traffic. The results are compared to the well known IEEE 802.11 MAC, indicating that even without retransmitting the broadcast frames, the probability of successful transmission can be doubled. Moreover, the throughput of our approach outperforms IEEE 802.11 MAC and the results show it can be boosted up to 100%.

In this paper we present a performance analysis of XOR1-based flat routing protocols in high mobility conditions, considering a vehicular ad hoc network (VANET) formed in a highway scenario. First, we describe an XOR-based protocol that incorporates several adaptations of the existing XOR-based routing algorithms for wired networks, in order to cope with the network mobility. Then we propose an improved version of it, XORi, which modifies the protocol's information gathering process to accommodate the specific dynamic nature of VANETs topology. Finally, we evaluate the performance of XOR-based protocols with other topology-based routing protocols. Simulation results allow us to characterize the performance of this class of protocols through the comparison of the packet delivery ratio, end-to-end path delay and average number of path hops2. When a moderate density of nodes is considered, simulations show that XOR-based algorithms achieve almost the same packet delivery rate as link state algorithms, such as OLSR, while for high density of nodes XOR-based algorithms scale better in terms of delay when compared to source routing algorithms, such as DSR3.

In the last years, there has been an increasing interest in developing and testing Medium Access Control (MAC) protocols for ad hoc networks based on optimal methods. Several solutions do not take into account the throughput-fairness tradeoff, presenting high throughput performance but under-performing in terms of medium access fairness. This work presents a novel MAC scheme, which is designed to maximize the network throughput-fairness performance. In our scheme, all nodes adopt a common optimal contention window, which is based on a common view of the channel. Our proposal improves the access fairness because the contention window is similar to all nodes, and its behavior does not depend on previous MAC states. Departing from the optimal throughput for a saturated network, we devise a scheme to estimate the number of nodes, which is a prime parameter to regulate the medium access control. We show that each node is suitable to estimate the number of competing nodes by using both its own medium access probability and the idle slot probability observed from the channel. Several simulation results evaluate the throughput-fairness performance of our proposal with several state-of-the-art MAC protocols, such as AOB, Idle Sense, GDCF and the well known IEEE 802.11. The results indicate that our approach presents the highest value of medium access fairness among the simulated protocols, meaning that its throughput is closer to the optimal theoretical throughput obtained for a fair network.

In the last years, there has been an increasing interest in developing and testing Medium Access Control (MAC) protocols for ad hoc networks based on optimal methods. The solutions already proposed in several works are generally hard to implement because they require extensive message passing among the nodes. Moreover, a relaxation for the optimal solution is generally needed to achieve an implementable algorithm from the optimization problem solution. These disadvantages decrease the performance of the optimal-based algorithms, often translated into a less than expected throughput. This is the main reason why several MAC protocols, such as IEEE 802.11, use simple heuristics to control the access of the nodes to the medium. Generally, the adoption of heuristics in MAC protocols turns its formal performance analysis very difficult. The motivation of this work is to assess the validity of a formal method that analyzes the throughput of heuristic-based MAC protocols. This work starts to introduce the method that characterizes the node's medium access satisfaction. This method is used to compare the heuristics used to control the medium access. We describe and compare several heuristics by applying the formal analysis, which is later validated through simulations. The method here described can be applied at MAC design stage, since the designer can evaluate the performance of a given heuristic by comparison with others.

Several routing protocols for Mobile Ad Hoc Networks (MANETS), including the well known Ad hoc On-Demand Distance Vector Routing (AODV) and Optimized Link State Routing (OLSR), propose the use of periodic messages (Hello messages) to detect neighbor nodes. After receiving the first Hello message from one of its neighbors, a node starts the link sensing task by setting up a sensing timer. Each time a new Hello message is received from the same neighbor, the sensing timer is restarted and the link duration is prolonged. If the sensing timer expires, it indicates a long time interval without receiving an Hello message and, consequently, the link is considered broken. The transmission frequency of the Hello messages and the expiration value of the sensing timer truly depends on node's mobility: if the nodes are moving quickly and the Hello messages are rarely transmitted, the neighbor nodes can be in communication range but they are not detected; in the same scenario, if the expiration value of the sensing timer is too high, a link is sensed broken too late. In this paper, we consider a MANET under the Random Waypoint mobility model. We investigate the relationship between the transmission frequency of the Hello messages and the sensing timer expiration value with the network node's mobility. We formally deduce the probability of link existence after β periods of transmission of the Hello message. The probability is later used to define the sensing timer expiration value, considering a given probability that the Hello message transmission fails. Finally, we evaluate our study through both numerically analysis and simulations, which confirms the effectiveness and accuracy of our approach.