IoT

The article presents a method for optimizing the algorithm flow graph of a deep neural network to reduce the number of processor elements (PE) required for executing the algorithm on single-board computers. The proposed approach is based on the use of a structural matrix to optimize the neural network architecture without loss of performance. The research demonstrated that by reducing the width of the graph, the number of processor elements was reduced from 3 to 2, while maintaining network performance at 75% efficiency.

This study provides a comprehensive overview of the various means and methods employed in gathering data, emphasizing the need for advanced technologies in the face of increasing energy demands and evolving regulatory environments. A thorough comparative analysis focuses on several key aspects, including technology comparison, data accuracy and reliability, real-time data collection capabilities, cost effectiveness, scalability, and flexibility, consumer interaction, and feedback mecha- nisms.

Timely detection and prevention of agriculture vehicles malfunctions are key approaches to reducing maintenance costs, as well as updating and replacing equipment, and reducing the cost of growing agricultural crops. In this article an approach for Remaining Useful Life (RUL) prediction that utilizes a combination of telemetry, maintenance, and geospatial data (such as weather and terrain information) as input to a Long Short- Term Memory (LSTM) algorithm has been considered.

The object of the research is the security of the file system of a single-board platform. As part of the research reported in this paper, a method has been proposed to protect the file system using encryption. Implementing a Linux Unified Key Setup paired with a password or Universal Serial Bus key has been demonstrated. The advantages of Linux Unified Key Setup for this task and the possibilities for system configuration and encryption method depending on the use case and hardware configuration has been outlined.

The Internet of Things (IoT) enables the creation of networks between devices, people, applications, and the Internet, creating new ecosystems with higher productivity, better energy efficiency, and higher profitability. Nodes in these networks must have the ability to communicate and exchange data, which requires the use of data transfer protocols. However, choosing the right protocol for a specific use case is not always straightforward.

The proper detection and prevention of malfunctions are crucial in mitigating maintenance costs and equipment replacements for agricultural vehicles, ultimately reducing the expenses associated with crop cultivation. Predictive analytics for agriculture vehicles leverage machine learning and sensor data to anticipate equipment faults, optimize maintenance schedules, and enhance operational efficiency in the farming industry.

Smart greenhouses use Internet of Things (IoT) technology to monitor and control various factors that affect plant growth, such as soil humidity, indoor humidity, soil temperature, rain sensor, illumination, and indoor temperature.  Sensors and actuators connected to an IoT network can collect data on these factors and use it to automate processes such as watering, heating, and ventilation.  This can help optimize growing conditions and improve crop yield.  To enable their vegetative growth and development, plants need the right amount of water at the right time.  The objective of this work

Many food contamination incidents have occurred during the last decade which has proven the failure of the food supply chain management system to track the food, money, and information movement within the food supply chain.  Many models have been established. This paper presents the design and implementation of the new model providing real-time data acquisition, monitoring, and storing on a tamper-proof blockchain of the main food supply movement.