Internet of things

The rapid growth of the Internet of Things (IoT) has revolutionized the logistics industry by enabling real-time tracking and monitoring of goods throughout the supply chain. However, the immense volume of telemetric data generated by IoT devices presents significant challenges in terms of scalability, data integrity, and security for traditional database solutions. This paper explores the applicability of blockchain technology as an alternative for storing and managing IoT telemetric data in logistics.

The article investigates the potential of serverless architectures for efficiently processing large-scale metadata generated by Internet of Things (IoT) sensors. As IoT systems grow increasingly complex, the challenges associated with processing vast amounts of data in distributed environments become more pronounced. Key issues include ensuring data accuracy, maintaining scalability, and reducing the operational costs of data processing infrastructure.

The design of modern Cyber-Physical Systems (CPS) connects physical and digital realms from cloud systems to edge devices. Microservice architecture has been widely used for IT solutions and emerges as a promising approach for supporting CPS that are more efficient, adaptable, and interconnected. However, there is an increasing need to improve the availability, reliability, and resilience of microservice systems according to the needs. This paper summarizes the challenges and drawbacks of microservice architecture used for CPS.

A multi-level structure of safe intellectualization of society’s infrastructure “objects – cyber-physical systems” in the functional space “selection – exchange of information – processing – management” is proposed according to the profiles – confidentiality, integrity, availability for “smart environmental monitoring”, “smart education”, “smart energy”, “intelligent transport system” and other subject areas.

The application of intelligent sensors, network technologies, and machine learning in IoT and industry is increas- ingly widespread as a part of the development and implementation of Industry 4.0, Industry 5.0, and Smart City. It is necessary to review the fundamental principles of metrological support for production. This includes calibration, estimation of measurement uncertainty, traceability, and processing of large data sets to reproduce and compare the results of measurements of physical quan- tities remotely.

A justified hybrid multilevel approach to IoT infrastructure implementation is set to facilitate the achievement of a scalable IoT (Internet of Things) infrastructure, incorporating integration into cloud technologies and services. The localization of hardware-software traffic management means at the lower level of the IoT infrastructure, close to data collection devices, ensures the generation of control influences in real-time, and relieves communication channels at the higher levels of the IoT infrastructure architecture.

The increasing demand for precision agriculture has prompted the integration of advanced technologies to optimize agricultural practices. This article presents an approach to agricultural field data processing using a cloud-based data pipeline. The system leverages data from various sensors deployed in the fields to collect real-time information on key parameters such as soil moisture, temperature, humidity, etc. The collected data is transmitted to the cloud where it undergoes a series of data processing and analysis stages.

Water management is crucial for agriculture, as it is the primary source of irrigation for crops.  Effective water management can help farmers to improve crop yields, reduce water waste, and increase resilience to drought.  This can include practices such as precision irrigation, using sensors and technology to deliver water only where and when it is needed, and conservation tillage, which helps to reduce evaporation and retain moisture in the soil.  Additionally, farmers can implement water-saving techniques such as crop selection, crop rotation, and soil conservation to reduce their water

Predictive maintenance is a widely applied maintenance program that requires extensive support of computer aided systems. The program uses specific procedures that are to be addressed when developing predictive maintenance software solutions. Despite the fact that software solutions for predictive maintenance were introduced almost at the same time as the program emerged, it still remains a very actual field for the application of computer aided systems.