Deep learning models excel in computer vision tasks with large labeled datasets but often struggle with performance degradation under domain shifts to unlabeled target domains. Unsupervised Domain Adaptation (UDA) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. In this paper, we propose Self-Contrastive Learning for Domain Adaptation (SCoDA), a novel framework that combines self-supervised contrastive learning with distributional alignment techniques to learn domain-invariant representations. SCoDA jointly optimizes domain alignment and classification by using contrastive loss to reduce feature gaps while preserving task performance through source supervision. Experiments on UDA benchmarks demonstrate that SCoDA outperforms traditional baselines, achieving accuracy improvements without relying on target labels. These results highlight SCoDA’s effectiveness in addressing complex domain shifts and its potential for real-world applications. Our code is available at https://github.com/viniavena/SCoDA.

Wi-Fi datasets play a crucial role in wireless networking research. They are often the result of extensive, demanding measurement campaigns. Unfortunately, researchers lack a clear assessment of where to place probes and when they have collected enough data and still obtain a representative view of the environment. Our goal is to make this process more efficient while preserving its rigor. We propose a framework that incorporates a calibration phase to evaluate the representativeness of a dataset. To this end, we use Earth Mover’s Distance (EMD) as a similarity metric to quantify data distribution differences and avoid redundant data captures. Through experimental campaigns in three distinct environments, we demonstrate that achieving a significant reduction in data collection effort is possible without compromising measurement reliability.

This work presents a proactive computer vision-based ADAS system designed to enhance VRU safety in risk zones at the rear of the vehicle and filter lanes. The system combines object detection, multi-object tracking, and risk assessment to generate real-time proximity alerts. Compared to a detection-only approach, the proposed method shows a 19% improvement in precision and a 10% increase in F1-score, while introducing a latency overhead of less than 1 ms. Evaluated on edge devices, the system maintains efficient performance across resource-constrained platforms. To support research in this domain, we introduce FilterLane-VRU, a new dataset of rearview urban traffic scenarios with temporal proximity annotations. Results validate the feasibility of deploying the system in real-world ADAS applications, offering a reliable and cost-effective solution for VRU safety.

The emergence of software-defined vehicles has brought machine learning into the vehicular domain. To support these data-driven applications, techniques to incentivize users to share their vehicle data are crucial. Federated learning trains machine learning models in a distributed manner, leveraging client data without compromising its privacy. Nonetheless, in vehicular networks, the dynamic behavior of nodes affects client availability and the global model's performance. Accordingly, this paper evaluates federated learning (FL) in a realistic vehicular network topology, accounting for real vehicle traffic in two Brazilian urban areas. The network simulation covers 3.7 km2with road speeds and 1,290 vehicles per hour, based on real data. We observe a performance decay in urban areas with longer vehicle permanence. Interestingly, longer vehicle participation in FL training leads to a biased model, with reduced generalization. We then improve our investigation based on the Dice-Sorensen coefficient to enhance vehicle variability over time. With 47% fewer vehicles per round, we achieve faster learning, higher convergence in the first 15 rounds, and equivalent final accuracy of 93 %.

Payment Channel Networks (PCNs) have successfully replaced slow global consensus mechanisms with local cryptographic agreements between nodes. As PCN payments heavily depend on network topology for payment routing, strategic node positioning is critical to building cost-effective channels for users and enhancing network robustness against topological attacks. Nevertheless, existing node attachment strategies in the Lightning Network (LN), the most popular PCN, ignore crucial topology issues, such as network centralization and the scarcity of cycles for cheap off-chain rebalancing. In this paper, we first investigate the current state of the LN topology and show that the availability of topology cycles is highly unequal in the network, which exposes the network to several vulnerabilities. Then, we design ProfitPilot, a node positioning strategy that encourages cycle creation in PCNs to reverse the trend in centralization and enable cheap off-chain rebalancing. We compare our proposed algorithm with heuristics available in the Lightning Network and verify that even by focusing on creating cycles, ProfitPilot successfully increases the user’s probability of collecting fees by over 2× while reducing average paying fees. Furthermore, out of all the evaluated heuristics, ProfitPilot presents the fastest increase in network transitivity and mitigates the impact of targeted topological attacks by over 17% compared with the regular Lightning Network operation.

Flow classification employs machine learning techniques to identify attacks on computer networks. This classification relies on quantitative features that synthesize the information of packets from the same flow. Conventional features, however, such as packet size and the number of bytes, generate redundancies and do not capture the temporal correlations between the packets in a flow. Automated network attacks generate periodic patterns observable through spectral decomposition, which facilitates classification. This paper proposes FENED (Feature Extraction by Network spEctrum Decomposition), a method to extract features from network data. We consider the packet-arrived order within the same flow using the fast Fourier transform for binary classification. The proposed feature vector contains the module of the spectral components of the flow. Experimental results show that FENED outperforms conventional proposals because it extracts features that consider intra-flow packet-arrival order.

Service provisioning in next-generation networks, such as 5G and 6G, relies on virtualization to carry out multi-domain and multi-tenant connections. In these scenarios, virtual network functions (VNF) orchestration becomes susceptible to security threats once trust between peers cannot be assumed. This paper1proposes a blockchain-based system for an agile, secure, and distributed provisioning of virtual network functions in scenarios with multiple administrative domains. Our proposal employs smart contracts to deliver all stages of a service-level-agreement management life cycle automatically. We develop, implement, and evaluate a prototype of the proposed system using smart contracts running on Hyperledger Fabric. The performance evaluation results show that the system guarantees high-rate VNF provisioning, reaching hundreds of slice requests per second in a trustful way.

The optimization of urban traffic lights is a relevant problem. With the increasing occupation of urban pathways comes mobility deterioration: increasing delays, traffic jams and other consequential losses. Its relevance led to several proposals on traffic light optimization; the majority of them only consider vehicular traffic, to the detriment of pedestrians. Nonetheless, the longer pedestrians wait to cross, the riskier their behavior becomes, since they become more impatient. We tackle this problem through the optimization of traffic lights considering the average delays of both pedestrians and vehicles, by using microscopic traffic simulations. The problem is modeled on the basis of reference works of the area, and solved by an Evolution Strategy (ES). Several constraint handling methods are compared, including one proposed in this work, Two-Level Ranking (TLR), that aims to quickly find feasible solutions, which is important for real-time execution. The ES was able to find solutions that keep the pedestrian delays within the limits given by related work. Furthermore, in the evaluated scenario, a solution that satisfies the constraints is found, in average, at approximately 18.6 seconds with TLR, which is shorter than what other methods yield, allowing real-time operation.

Vehicular communications based on IEEE802.11p promise to improve traffic efficiency and safety in the near future. Embedded with 802.11p-compliant devices, the cooperation among connected vehicles will provide a shift in the way transportation systems operate. Nevertheless, before widespread adoption, extensive performance evaluation of the standard is in need. Due to the high cost and reasonably low availability of commercial devices, most work on VANETs is still performed using simulations. To investigate the equivalence between real experiments and simulations involving 802.11p, this article evaluates the behavior obtained by periodic beacon messages, the basis of safety applications in VANETs. Using commercial OBUs and RSUs, the results are compared with those obtained with NS-3/PhySim and Veins/MiXiM simulators. In V2I and V2V communication scenarios, three key metrics are evaluated: the maximum communication range, packet delivery rate, and packet inter-reception time. The influence of different modulations supported by 802.11p and varying vehicle speeds are also analyzed.

The multi-resource allocation problem arises in different scenarios. Different mechanisms have been proposed to fairly divide multiple resources, most notably, Dominant Resource Fairness (DRF). Even though DRF satisfies several desirable properties, it considers fairness only in the static setting. We propose Stateful DRF (SDRF), an extension of DRF that looks at past allocations and enforces fairness in the long run while keeping the fundamental properties of DRF. We prove that SDRF is strategyproof, since users cannot manipulate the system by misreporting their demands; incentivizes sharing, because no user is better off if resources are equally partitioned; and is efficient, as no allocation can be improved without decreasing another. In SDRF, users' priorities change over time. To avoid recalculating priorities at every task scheduling decision, we also propose Live Tree, a data structure that keeps elements with predictable time-varying priorities ordered. We implement SDRF on Mesos and run it in a real cluster. Moreover, we conduct large-scale simulations based on Google cluster traces of 30 million tasks over one month. Results show that SDRF reduces users' waiting time on average. This improves fairness, by increasing the number of completed tasks for users with lower demands, with negligible impact on high-demand users.

Global Navigation Satellite Systems (GNSS) are used today in various contexts as a source of data for several applications. They provide real-time positioning based on the transmission of electromagnetic waves from a satellite to a receiver, being subject to several factors. Some scenarios, such as canyons (urban or geographic), forests and tunnels, are challenging, since the coverage in them is unavailable or unreliable, producing rogue positioning information or no information at all. Thus, applications that demand high availability usually employ other sensors. Nevertheless, reducing the amount of such devices results in lower costs and energy consumption. Aiming to improve the reliability and availability of GNSS-based systems retaining cost-effectiveness, this work proposes a dead reckoning system, using the last known location and sensor data to infer the current position. The sensors employed here are largely available in commercial vehicles. We calculate the estimates using machine learning models and improving the results through a map matching procedure. The results, based on simulations with real GNSS and sensor data, indicate that the system is able to closely reproduce trajectories for over a minute. The obtained mean error is of approximately 19 meters, suitable for obtaining approximate locations in scenarios with unreliable satellite coverage.

The standard approach adopted by software middleboxes to use multiple cores has long been to direct packets to cores at flow granularity. This, however, has significant shortcomings. First, it is inefficient, since it cannot use all cores when there is a small number of concurrent flows---which happens frequently. Second, asymmetry in flow distribution causes unfairness even with a larger number of flows. Yet, the current trend of higher-speed links and core-richer CPUs only aggravates these problems. In this paper, we propose a natural alternative: that middleboxes should direct packets to cores at a finer granularity. Our system, Sprayer, solves the fundamental problems of per-flow solutions and addresses the new challenges of handling shared flow state that come with packet spraying. Sprayer builds on the observation that most middleboxes only update flow state when connections start or finish; ensuring that all control packets from the same TCP connection are processed in the same core. We show that, when compared to the per-flow alternative, Sprayer significantly improves fairness and seamlessly uses the entire capacity, even when there is a single flow.

In order to match the energy demand of electric vehicles to the capacity of the power grid, it is fundamental to understand the occupancy of charging stations and to react accordingly. A Markov model of a fast charging station for lithium-ion (Li-ion) batteries, i.e., the most prevalent type today, is proposed. Li-Ion batteries present a two-step charging profile, making energy management particularly challenging. A wide range of situations is covered by considering three types of scenarios with and without waiting lines. The analytical results obtained from the steady-state solution of the Markov model reveal the behavior of multiple variables of interest: availability of the charging station to accept new customers (in terms of space and energy), number of customers, energy consumption, and power utilization. From the results, indicators for assessing the quality of service of the charging station are derived. Based on these indicators, customers may decide either to wait or to head toward another station. The owners of the stations, in turn, can predict the impact of investments in space and energy provisioning, when devising capacity planning strategies.

Achieving efficient content dissemination in mobile opportunistic networks becomes a big challenge when content sizes are large and require more capacity than what contact opportunities between nodes may offer. Content fragmentation solves only part of the problem, as nodes still need to decide which fragment to send when a contact happens. To address this problem, we propose EPICS, a protocol designed to quickly exchange large contents in opportunistic networks. Using grey relational analysis, EPICS is able to balance the distribution of contents that have different sizes and creation times, providing fairer delay distribution and faster dissemination. We implemented and evaluated EPICS through real experimentation using Android devices. Results show that EPICS significantly reduces content dissemination delays when compared to classic approaches.

A hot topic in data center design is to envision geo-distributed architectures spanning a few sites across wide area networks, allowing more proximity to the end users and higher survivability, defined as the capacity of a system to operate after failures. As a shortcoming, this approach is subject to an increase of latency between servers, caused by their geographic distances. In this paper, we address the trade-off between latency and survivability in geo-distributed data centers, through the formulation of an optimization problem. Simulations considering realistic scenarios show that the latency increase is significant only in the case of very strong survivability requirements, whereas it is negligible for moderate survivability requirements. For instance, the worst-case latency is less than 4 ms when guaranteeing that 80% of the servers are available after a failure, in a network where the latency could be up to 33 ms.

Traffic monitoring and control are becoming more and more important as the number of vehicles and traffic jams grow. Nevertheless, these tasks are still predominantly performed by visual means using strategically placed video cameras. For more effectiveness, proposals to improve traffic monitoring and control should consider automated systems. In this paper, we propose the Collaborative and Opportunistic Traffic Monitoring System (COTraMS), which is a system that monitors traffic using available IEEE 802.11 networks. COTraMS is collaborative because user participation is essential in defining the vehicle movement and opportunistic because it uses existing information. To evaluate the performance of COTraMS, a prototype is implemented using an IEEE 802.11 b/g network. Measurements from a real public wireless network in Rio de Janeiro, Brazil, demonstrate the possibility of obtaining traffic conditions with our proposed monitoring system. In addition, we analyze COTraMS via simulation to evaluate its performance in scenarios with a larger number of vehicles. The comparison of the obtained results with data obtained from Global Positioning System shows high accuracy in detecting both the position of the vehicle and the estimation of the road condition, using a simple architecture and a small amount of network bandwidth.

The network infrastructure plays an important role for datacenter applications. Therefore, datacenter network architectures are designed with three main goals: bandwidth, latency and reliability. This work focuses on the last goal and provides a comparative analysis of the topologies of prevalent datacenter architectures. Those architectures use a network based only on switches or a hybrid scheme of servers and switches to perform packet forwarding. We analyze failures of the main networking elements (link, server, and switch) to evaluate the tradeoffs of the different datacenter topologies. Considering only the network topology, our analysis provides a baseline study to the choice or design of a datacenter network with regard to reliability. Our results show that, as the number of failures increases, the considered hybrid topologies can substantially increase the path length, whereas servers on the switch-only topology tend to disconnect more quickly from the main network.

Traffic monitoring and control is getting more and more important as the number of vehicles and traffic jams steadily grow. Nevertheless, traffic control is still predominantly done by visual means using strategically placed video cameras. To be more effective, proposals to improve the traffic conditions should consider automated monitoring systems. This work proposes an opportunistic system for collaborative traffic monitoring using available IEEE 802.11 networks. Based on the information received by 802.11 beacon frames, vehicles provide the data needed by a central entity to handle and disseminate information about traffic conditions on urban roads, exploiting readily available network resources. Experiments performed with data from a real public wireless network, in Rio de Janeiro, demonstrate the possibility of obtaining traffic conditions with our proposed traffic monitoring method. The system results are close to GPS traces. Moreover, the proposed system facilitates large-scale adoption since it does not require specific hardware.

Xen is a tool for hardware virtualization often used to build virtual routers. Xen, however, does not assure the fundamental requirement of network isolation among these routers. This work proposes XTC (Xen Throughput Control) to fill this gap, and therefore, to guarantee multiple network coexistence without interference. XTC sets the amount of CPU allocated to each virtual router according to the maximum throughput allowed. Xen behavior is modeled by using experimental data, and based on these data, XTC is designed using feedback control. Results obtained in a testbed demonstrate the XTC ability to isolate virtual network capacities and to adapt to system changes.

As of today, many routing protocols for wireless mesh networks have been proposed. Nevertheless, quite a few take the high loss rate of control packets into account. This work analyzes the problem of consistent routing information among wireless network nodes. To accomplish this, we propose a metric to evaluate the level of inconsistency among routing tables. Our experimental analysis demonstrates that the high loss rates seen in indoor environments negatively influence route computation. In addition, we demonstrate that the high network dynamics leads to severe instability in next hop selection. Results show that the effect of loss is significant and that the simple manipulation of routing protocol configuration parameters may be not enough to cope with the problem.

This work proposes the wireless-mesh-network proactive routing (WPR) protocol for wireless mesh networks. Unlike current routing protocols, such as the optimized link- state routing (OLSR), WPR uses a controlled-flooding algorithm tailored to the typical wireless-mesh-network traffic matrix, which concentrates traffic on links close to the gateway. The goal is to improve efficiency by saving network resources and avoiding network bottlenecks. WPR also avoids redundant messages using the AMPR (adapted multipoint relay) set. In this paper, we provide a complexity analysis of the algorithms used by WPR and OLSR. Besides, simulation results show that WPR outperforms OLSR in throughput and packet delivery rate.

A home network is a communication system that aims to interconnect household appliances and share the access to the Internet. This work proposes a novel mechanism which is able to improve the multiple access method of home networks. The Contention window Proactive Increase (CPI) mechanism avoids collisions by increasing the number of times the backoff procedure is called. We applied the CPI mechanism to the IEEE 802.11 and HomePlug standards given their similar access methods. We show the efficiency of the proposed mechanism evaluating through simulations the network throughput gains compared to the original standards. 1.

Powerline communications (PLC) are receiving special attention since they use an already available and ubiquitous infrastructure. The main standard for PLC home networks is HomePlug. This work improves the throughput of HomePlug by modifying the medium access control sub-layer. The key idea is to define a fast collision avoidance mechanism where every station that wants to access the medium increments its contention window after sensing another ongoing transmission. The proposal reduces the number of collisions in the network improving the achievable throughput. We compared our mechanism to the original HomePlug standard through simulation and mathematical analysis. We verified that the improvement is independent from the packet size, the transmission rate and the number of nodes when the network is high loaded

The paper proposes a new mechanism to increase the HomePNA 2.0 MAC protocol throughput. First, we review the HomePNA (Home Phone-line Network Alliance) MAC protocol and its collision resolution mechanism. Then, we use simulations to evaluate the throughput of HomePNA using a module that we implemented in the ns-2 simulator We propose a priority aggregation mechanism that uses the eight HomePNA priority levels in a more efficient way. The simulation results show that the proposed mechanism is able to increase the throughput up to 44%. Moreover, our mechanism does not require modifying the HomePNA specification, and can be easily implemented as a new sublayer above the MAC sublayer.

Quality of service (QoS) based routing provides QoS guarantees to multimedia applications and an efficient utilization of the network resources. Nevertheless, QoS routing is likely to be a costly process that does not scale when the number of nodes increases. Thus, the routing algorithm must be simple. This paper proposes and analyses the performance of a link-state QoS routing algorithm that takes into account three metrics: propagation delay, available bandwidth, and loss probability. A heuristic based on the residual loss probability and metric-combination is used to turn the algorithm scalable and solvable in polynomial time. The simulation results show that our algorithm is a promising solution to construct paths constrained on three metrics.

The IP multicast architecture was not widely deployed because multicast address allocation is difficult and there is no scalable solution to inter-domain multicast routing. Hence, there is an interest in developing protocols that allow the progressive deployment of the multicast service by supporting unicast clouds. This paper proposes HBH (hop-by-hop multicast routing protocol). HBH adopts the source-specific channel abstraction to simplify address allocation and implements multicast distribution using recursive unicast trees. In this model, data packets have unicast destination addresses. Therefore, HBH supports pure unicast routers transparently. The branching-nodes recursively create packet copies to implement the distribution. HBH constructs a shortest-path tree even in the presence of asymmetric unicast routing. Consequently, HBH provides best routes in asymmetric networks, and is suitable for an eventual implementation of QoS-based routing. Additionally HBH reduces tree bandwidth consumption in asymmetric networks when compared to other approaches. The results obtained from simulation support our statements.

IP Multicast is facing a slow take-off although it is a hotly debated topic since more than a decade. Many reasons are responsible for this status. Hence, the Internet is likely to be organized with both unicast and multicast enabled networks. Thus, it is of utmost importance to design protocols that allow the progressive deployment of the multicast service by supporting unicast clouds. This paper proposes HBH (Hop-By-Hop multicast routing protocol). HBH adopts the source-specific channel abstraction to simplify address allocation and implements data distribution using recursive unicast trees, which allow the transparent support of unicast-only routers. Additionally, HBH is original because its tree construction algorithm takes into account the unicast routing asymmetries. As most multicast routing protocols rely on the unicast infrastructure, these asymmetries impact the structure of the multicast trees. We show through simulation that HBH outperforms other multicast routing protocols in terms of the delay experienced by the receivers and the bandwidth consumption of the multicast trees.