This paper presents an adaptive architecture that centralizes the control of public lighting and intelligent management to economize lighting and maintain maximum visual comfort in illuminated areas. To carry out this management, the architecture merges various techniques of artificial intelligence (AI) and statistics such as artificial neural networks (ANN), multi-agent systems (MAS), EM algorithm, methods based on ANOVA, and a Service Oriented Approach (SOA). It achieves optimization in terms of both energy consumption and cost by using a modular architecture, and is fully adaptable to current lighting systems. The architecture was successfully tested and validated and continues to be in development.

In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community.

This paper presents a novel automated road damage detection approach using Unmanned Aerial Vehicle (UAV) images and deep learning techniques. Maintaining road infrastructure is critical for ensuring a safe and sustainable transportation system. However, the manual collection of road damage data can be labor-intensive and unsafe for humans. Therefore, we propose using UAVs and Artificial Intelligence (AI) technologies to improve road damage detection’s efficiency and accuracy significantly. Our proposed approach utilizes three algorithms, YOLOv4, YOLOv5, and YOLOv7, for object detection and localization in UAV images. We trained and tested these algorithms using a combination of the RDD2022 dataset from China and a Spanish road dataset. The experimental results demonstrate that our approach is efficient and achieves 59.9% mean average precision mAP@.5 for the YOLOv5 version, 73.20% mAP@.5 for the YOLOv7 version, and 65.70% mAP@.5 for a YOLOv5 model with a Transformer Prediction Head. These results demonstrate the potential of using UAVs and deep learning for automated road damage detection and pave the way for future research in this field.

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.

With the growing number of heterogeneous resource-constrained devices connected to the Internet, it becomes increasingly challenging to secure the privacy and protection of data. Strong but efficient cryptography solutions must be employed to deal with this problem, along with methods to standardize secure communications between these devices. The PRISEC module of the UbiPri middleware has this goal. In this work, we present the performance of the AES (Advanced Encryption Standard), RC6 (Rivest Cipher 6), Twofish, SPECK128, LEA, and ChaCha20-Poly1305 algorithms in Internet of Things (IoT) devices, measuring their execution times, throughput, and power consumption, with the main goal of determining which symmetric key ciphers are best to be applied in PRISEC. We verify that ChaCha20-Poly1305 is a very good option for resource constrained devices, along with the lightweight block ciphers SPECK128 and LEA.