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.

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura–Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura–Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

The flying ad hoc network (FANET) has emerged as an alternative access technology for regions that have no fixed infrastructure or are hard to reach. This new type of network is composed of devices called unmanned aerial vehicles (UAVs) that communicate with each other, but there is no specific routing protocol to FANET applications that allows efficient communication between these devices. This paper proposes a FANET adaptive routing protocol based on the fuzzy system. The validation of the FANET adaptive protocol was performed through simulation using Network Simulator version 2 (NS-2) and, it was assessed by quality of service (QoS) and quality of experience (QoE) metrics.

The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322–1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714–1.3891 dB, with an error SD less than 1.1706 dB.

In the new context of Next Generation Networks, Aerial Ad Hoc Networks, also known as FANET (Flying Ad Hoc Network), are being used to monitor areas of difficult access. Owing to the dynamism and autoconfiguration of this type of network, a strategy is needed to position its devices (drones) to ensure it can achieve good performance. In light of this, this paper proposes a flight path planning model, which involves a metaheuristic optimization-based approach. The proposal relied on the artificial neural networks to optimize the positioning of the relay device, so that the throughput between the other devices could be increased; the benefits of the proposal were demonstrated through simulations.

The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Pará (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with −108 dBm, the optimal solution determined the use of three gateways to cover the smart campus area.