To satisfy requirements on future mobile network, a large number of small cells should be deployed. In such scenario, mobility management becomes a critical issue in order to ensure seamless connectivity with a reasonable overhead. In this paper, we propose a fuzzy logic-based scheme exploiting a user velocity and a radio channel quality to adapt a hysteresis margin for handover decision in a self-optimizing manner. The objective of the proposed algorithm is to reduce a number of redundant handovers and a handover failure ratio while allowing the users to exploit benefits of the dense small cell deployment. Simulation results show that our proposed algorithm efficiently suppresses ping pong effect and keeps it at a negligible level (below 1%) in all investigated scenarios. Moreover, the handover failure ratio and the total number of handovers are notably reduced with respect to existing algorithms, especially in scenario with high amount of small cells. In addition, the proposed scheme keeps the time spent by the users connected to the small cells at a similar level as the competitive algorithms. Thus, the benefits of the dense small cell deployment for the users are preserved.

Background: In Brazil, a substantial number of coronavirus disease (COVID-19) cases and deaths have been reported. It has become the second most affected country worldwide, as of June 9, 2020. Official Brazilian government sources present contradictory data on the impact of the disease; thus, it is possible that the actual number of infected individuals and deaths in Brazil is far larger than those officially reported. It is very likely that the actual spread of the disease has been underestimated. Objective: This study investigates the underreporting of cases and deaths related to COVID-19 in the most affected cities in Brazil, based on public data available from official Brazilian government internet portals, to identify the actual impact of the pandemic. Methods: We used data from historical deaths due to respiratory problems and other natural causes from two public portals: DATASUS (Department of Informatics of the Unified Healthcare System) (2010-2018) and the Brazilian Transparency Portal of Civil Registry (2019-2020). These data were used to build time-series models (modular regressions) to predict the expected mortality patterns for 2020. The forecasts were used to estimate the possible number of deaths that were incorrectly registered during the pandemic and posted on government internet portals in the most affected cities in the country. Results: Our model found a significant difference between the real and expected values. The number of deaths due to severe acute respiratory syndrome (SARS) was considerably higher in all cities, with increases between 493% and 5820%. This sudden increase may be associated with errors in reporting. An average underreporting of 40.68% (range 25.9%-62.7%) is estimated for COVID-19–related deaths. Conclusions: The significant rates of underreporting of deaths analyzed in our study demonstrate that officially released numbers are much lower than actual numbers, making it impossible for the authorities to implement a more effective pandemic response. Based on analyses carried out using different fatality rates, it can be inferred that Brazil’s epidemic is worsening, and the actual number of infectees could already be between 1 to 5.4 million.

The use of unmanned aerial vehicles (UAVS) has been suggested as a potential communications alternative due to their fast implantation, which makes this resource an ideal solution to provide support in scenarios such as natural disasters or intentional attacks that may cause partial or complete disruption of telecommunications services. However, one limitation of this solution is energy autonomy, which affects mission life. With this in mind, our group has developed a new method based on reinforcement learning that aims to reduce the power consumption of UAV missions in disaster scenarios to circumvent the negative effects of wind variations, thus optimizing the timing of the aerial mesh in locations affected by the disruption of fiber-optic-based telecommunications. The method considers the K-means to stagger the position of the resource stations—from which the UAVS launched—within the topology of Stockholm, Sweden. For the UAVS’ locomotion, the Q-learning approach was used to investigate possible actions that the UAVS could take due to urban obstacles randomly distributed in the scenario and due to wind speed. The latter is related to the way the UAVS are arranged during the mission. The numerical results of the simulations have shown that the solution based on reinforcement learning was able to reduce the power consumption by 15.93% compared to the naive solution, which can lead to an increase in the life of UAV missions.

Optical cloud networks allow for the integrated management of both optical and IT resources. In this paradigm, cloud services can be provisioned in an anycast fashion; i.e., only the source node asking for a service is specified, while it is up to the cloud control/management system to select the most suitable destination data center (DC) node. During the cloud service provisioning process, resiliency is crucial in order to guarantee continuous network operations also in the presence of failures. On the one hand, a survivability strategy needs to be able to meet the availability requirements of each specific cloud service, while on the other hand it must be efficient in using backup resources. This paper proposes a restoration-based survivability strategy, which combines the benefits of both cloud service relocation and service differentiation concepts. The former is used to enhance the restorability performance (i.e., the percentage of successfully restored cloud services) offered by restoration, while the latter ensures that critical services are given the proper consideration while backup resources are assigned. The paper proposes both an integer linear programming (ILP) formulation, which guarantees optimal results, and a heuristic, which trades the optimality of the solution achieved by the ILP for faster processing times. Simulation results show that the average service availability and restorability performance obtained by both the ILP and the heuristic are very close to that achievable using a protection-based strategy, but with the inherent benefit, in terms of efficient use of resources, offered by a restoration-based approach.

Smart Energy Control Systems (SECS) has been increasingly implemented in the Smart homes scenario, due to the possibility of conditioning and controlling residential energy consumption, thus contributing to reducing energy losses and unnecessary electrical consumption. With the evolution of embedded systems in conjunction with the Internet of Things (IoT), Smart Outlet (SO), and devices that promote the Users Indoor Identification (UII) environments, they have assumed fundamental roles in the acquisition of data from electrical devices and also in the mapping of the individualized consumption of each resident throughout the day, providing essential information for SECS Systems as a way to assist in energy balance with minimal impact on the daily usability of electrical equipment. However, in most of the works that propose these types of assistance to SECS based on SO and UII, they have a massive implementation of sensors throughout the residence, misinterpretation of the data generated by the residents, difficulty in the identification of multiple residents. Thus, the present work proposes an evolution of an SECS architecture called SmartCom, with the implementation of accurate identification of electrical equipment through Near Field Communication (NFC)-based SO (data transfer between the appliance and the SO) and of multiple inhabitants through Wi-Fi handover using smartphones, with the least possible impact on the user's comfort, as well as in the building structure, achieving a rebalanced residential energy consumption 87.3% of the time it was used.