IoT Off-Grid, Data Collection from a Machine Learning Classification Using UAV.

IoT encompasses various objects, technologies, communication standards, sensors, actuators in powered environments, and networked communication. The concept adopted here, IoT off-grid, considers an environment without commercial electricity and commercial internet. Managing various utilities with IoT and collecting the relevant information from this environment is the purpose of this project. It uses machine learning to select relevant data. These data are collected safely using a drone that travels through the off-grid stations. A systematic literature mapping is presented, identifying the state of the art. The result is a software architecture proposal with configurations in the drone and off-grid stations that contemplate data collection from the IoT off-grid environment. The results are also presented with different selection algorithms used in machine learning and final execution in the prototype.

Development of an Efficiency Platform Based on MQTT for UAV Controlling and DoS Attack Detection.

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature.

The goal of this work is to present a systematic literature mapping (SLM) identifying algorithms for the search for data, determining the best path and types of communication between the local server and the drone, as well as possible simulators to validate proposed solutions. The concept, here considered as IoT Off-Grid, is characterized by being an environment without commercial electrical infrastructure and without communication connected to the internet. IoT equipment generates data to be stored on a local server. It collects these data through a drone that searches each local server for later integration with the commercial internet environment. As a result, we have algorithms to determine the best path based on the TSP-travelling salesman problem. Different types of communication between the drone and the server contain the data, predominantly WiFi 802.11. As a simulator, OMNeT++ stands out.