Generative Adversarial Networks (GANs) have emerged as tools for creating synthetic data that mimics real datasets. Time series GANs extend GANs concept by attempting to replicate the temporal dependencies and patterns of time series, while preserving the privacy of the real data. However, measuring the fidelity and utility of a synthetic time series remains a challenge. Thus, this paper discusses the pros and cons of quantitative metrics to assess synthetic time series generated by GANs based on sensitive data of two application domains. We first review how metrics based on probability distributions, such as Kullback-Leibler Divergence, Jensen-Shannon Distance, Wasserstein Distance, and Maximum Mean Discrepancy, have been used to evaluate synthetic data. Meanwhile, to assess the utility of synthetic data the Testing on Synthetic, Training on Real (TSTR) score is used. To assess these metrics, we compare three time series GANs (RGAN, TimeGAN, Doppelganger) to generate synthetic datasets of two network domain applications: i) user content requests for a Brazilian streaming provider; and ii) devices connected to mobile base stations in a large Brazilian city. We compare the fidelity of synthetic datasets and assess their utility in predicting content requests and the number of users connected to a given mobile base station.

Smart grids integrate intelligence, automation, and communication into the electrical grid infrastructure, primarily through the use of smart meters. These meters play a crucial role in collecting and transmitting data, either to the cloud, which may cause delays, or to the edge, where meters are closer to the data source. In this paper, we propose Q-Balance, a neural network-based solution for optimizing computational resources at the edge, thus minimizing service processing time. Q-Balance utilizes the Multi-Layer Perceptron (MLP) technique to estimate response times for requests processed by computational resources. Evaluation results demonstrate that Q-Balance can significantly reduce the average response time, achieving up to a 65% reduction compared to the Min-Load approach at the edge and up to 79% in the cloud.

Having the proper incentive mechanism is paramount for the success of location-based social networks (LBSNs). With that in mind, we performed a cross-cultural study on the mechanism of incentive Mayorship, which Foursquare-Swarm employs to engage its users. The user who has the mayorship is the one who performed the highest number of check-ins in the last thirty days in a particular venue. We study how alternations and disputes for mayorship occur at the venues through automatic temporal monitoring. We collected data in two cities in different countries: Curitiba (Brazil) and Chicago (United States). We found, for instance, that renowned American food chains may have a more significant influence on the mayorship dispute in Curitiba. Also, hidden prestige associated with more affluent areas could be an extra motivator factor in Curitiba, but the same cannot be said for Chicago. This study shed some light on the mechanism of incentive mayorship in different venues, showing that local factors could play an important role. Our results can assist in improving user engagement on social web systems in different cultures.

Due to the increasing location-aware devices, mobility traces datasets have become an essential source for smart cities planning. Given this scenario, we propose MobVis, a framework to characterize mobility traces through different metrics, allowing comparisons between different mobility traces in a simplified way. Furthermore, MobVis can extract and visualize spatial, temporal, and social aspects of mobility data through a Web interface. MobVis architecture has five main components: input data; data preparation; data processing and analysis to extract mobility metrics; visualization; and a web interface. To demonstrate the framework's process, we created a use case analyzing the characteristics of two distinct traces (Taxi and IoT-Objects). Then, through different metrics, we evaluated the data in two aspects: i) descriptive, through a set of graphics and quantitative data that enables characterizing each trace; and ii) comparative, presenting the main differences between the traces.

Situational awareness platforms support the continuous observation of interest situations. In the eHealth context, SA can improve health services by assessing and interpreting patients' current situations. For instance, it allows the healthcare providers to recognize changes and abnormalities in the patient's condition and take the necessary actions. However, SA platforms still suffer from centralized management issues, such as availability, security, and privacy. This paper presents a blockchain-based approach for SA data processing to overcome these issues. Blockchain is a decentralized and distributed technology that also brings the advantage of flexible data custody. We evaluate our system through a case study and discuss the benefits and challenges of adopting Blockchain as a solution for eHealth.

Human mobility datasets have been used to characterize mobility and social aspects. These datasets range from cellular operator logs to tracking apps in scenarios such as university campuses, vehicles, and conferences. In this paper, we present and characterize the mobility dataset of participants of an academic conference, gathered through a gamification system. To achieve this goal, we mapped the social network formed by attendees in each technical session of the conference into a temporal graph. Furthermore, we discuss a community detection scheme based on topics of interest and analyze the performance of device-to-device (D2D) opportunistic forwarding algorithms. Results show that, although each participant has a high number of contacts, contact time is low.

Device-to-Device (D2D) communication has gained attention due to its potential to reduce data traffic in mobile operator networks. Several models and algorithms for efficient D2D communication have been proposed recently. However, despite the availability of D2D communication technologies in most current devices, there are few real-world performance evaluations using such communication model. Aiming to fill this gap, this paper presents an experimental evaluation of D2D communication using off-the-shelf devices. To achieve this goal, we propose ShareFile, a tool to measure the performance of content sharing through Wi-Fi Direct in Android devices. We deployed a testbed to compare the performance of D2D communication against centralized or non direct solutions. The results demonstrate that even small distances degrade D2D communication performance. The time to find nearby devices ranges from 400ms to 1.5s, while time to establish a connection can reach 6s and the flow rate reaches up to 6.5MBps, which is close to the 7.9MBps achieved by cloud solutions.

The number of smart devices in home networks is rapidly increasing, making it more complex to manage their faults. In addition, the lack of customer knowledge and tools to automatically diagnose and fix faults aggravate the problem. In this paper, we propose HomeNetRescue, a Software-Defined Network (SDN) service for autonomous management of wireless and wired home networks focused on fault and configuration management. We evaluate HomeNetRescue in a real world prototype, considering throughput, delay, and jitter. Our results show that HomeNetRescue can increase the throughput of the network by up to 131%, reducing wireless transmission delay and jitter by 46% and 24%, respectively.

In the recent years, the research community proposed several protocols and applications for opportunistic networking. A common assumption is that all nodes have pro social behavior and are willing to cooperate with the network. However, in opportunistic networking applications, this assumption can lead to degradations in the network performance. People can be selfish and this behavior affects the operation of the network. In this work, we propose an incentive mechanism to improve routing, called MINEIRO, which aims to detect and avoid selfish nodes based on the source of the messages. We demonstrate under which constraints our algorithm leads to Bayesian equilibrium. Moreover, we show that without an incentive mechanism the network supports up to 60% of nodes with selfish behavior without performance degradation in a random mobility scenario. Meanwhile, in a scenario with social-based mobility, the performance decreases linearly for more than 20% of selfish nodes. Our proposal, on the other hand, improves the performance with any amount of selfish nodes by encouraging users to relay messages from third-parties.

With the increasing number of users subscribing to mobile Internet such as 3G and 4G networks, Wireless Internet Services providers (WISP) aim to provide a good service for customers while elevating the number of clients. Several proposals to offload the traffic of 3G networks were made in the last few years, including the use of femtocells, WiFi offloading and more recently mobile-to-mobile opportunistic offloading. In this paper, we propose a multi-criteria decision-making framework to manage the offload of data from 3G networks using mobile-to-mobile opportunistic communications. Primarily, we focus on building a decision framework that employs only user knowledge to select which users should handover from infrastructure to mobile-to-mobile network, avoiding changes in the infrastructure. Next, we evaluate our proposal and demonstrate its feasibility through trace-driven simulations, achieving 6% of data offload when there is no delay tolerance in the application and up to 36% when application can tolerate 20 minutes of delay.