Abstract
The Internet of Things (IoT) has revolutionized how objects and devices interact, creating new possibilities for seamless connectivity and data exchange. This paper presents a unique and effective method for transferring images via the Message Queuing Telemetry Transport (MQTT) protocol in an encrypted manner. The image is split into multiple messages, with each carrying a segment of the image, and employ top-notch encryption techniques to ensure secure communication. Applying this process, the message payload is split into smaller segments, and consequently, it minimizes the network bandwidth impact while mitigating potential of packet loss or latency issues. Furthermore, by applying encryption techniques, we guarantee the confidentiality and integrity of the image data during transmission, safeguarding against unauthorized access or tampering. Our experiments in a real-world scenario involving remote indicator panels with LEDs verify the effectiveness of our approach. By using our proposed method, we successfully transmit images over MQTT, achieving secure and reliable data transfer while ensuring the integrity of the image content. Our results demonstrate the feasibility and effectiveness of the proposed approach for image transfer in IoT applications. The combination of message segmentation, MQTT protocol, and encryption techniques offers a practical solution for transmitting images in resource-constrained IoT networks while maintaining data security. This approach can be applied in different applications.

Abstract
Monitoring and controlling the energy consumption of electrical appliances brings significant benefits to both consumers and the energy utility. This work presents a system for monitoring and controlling energy consumption by residence loads connected to smart plugs. The user will have a tool to view consumption information and remotely turn loads on and off, as well as control the power level at which certain appliances will operate. In addition, it is intended to give the system the ability to make decisions regarding the operation of electrical devices based on the electrical energy available. This decision-making can occur either through priorities established by the user or, possibly, through Machine Learning applied to the system, based on the consumption pattern. Solutions like these can even be applied in situations where the user produces his own energy and would like to use the surplus produced to meet certain loads.

Abstract

Abstract
It is known that electrical energy consumption is higher during the day than at night.This is a challenge to balance the consumption levels because when the consumption is high at night, it does not have energy production to supply and the tariff usage is cheaper. Aspiring to avoid the users consuming too much electrical energy and work on this usage control during the night, the present work aims to develop smart plug modules that could self-manage power in residence utilizing the minimum of grid energy. In this sense, the modules may use the overproduction of energy coming from generator systems (such as photovoltaic panels), eliminating the necessity of battery usage. Sometimes, the power supply could provide different values of current, consequently, the use of this electric energy needs to adapt according to the production. Therefore, the final objective is to build an intelligent electrical management system that works on energy efficiency.

Abstract
In the current energy context, alternatives are sought that provide a more conscious use of energy and the development of technology aimed at efficiently meeting the needs of energy consumers and the utility company. In this scenario, smart systems for monitoring and controlling the energy consumption of residential loads stand out. In [1], the authors worked on a system from which the user could monitor their energy consumption in real time. Through a website, the consumer accessed their information using visualizations in graphics, for example. Consumption data was obtained by a smart plug. Furthermore, the option to remotely turn devices on and off has been included in the system so that the user has the ease of controlling their devices.

Abstract
In urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. This study compares the effectiveness of delivery using trucks versus drones. Two customer datasets, one clustered and one random, were used for testing. Route optimization involved four deterministic and four non-deterministic algorithms. The performance of these algorithms, considering the total distance traveled, was evaluated across different datasets and vehicle types. The top two algorithms were further assessed for environmental impact and cost efficiency. Battery consumption along the routes was also analyzed to gauge operational feasibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.