Industry 4.0 is a paradigm that enhances industrial automation systems with the recent advances in the domain of the Internet of Things (IoT), gaining new possibilities and providing new services. Traditional industrial machines do not have IoT capabilities, and in order to integrate such a machine into Industry 4.0, there is a need for an intermediary device or system that communicates with the machine through its supported communication interfaces and protocols and forwards the communication to the global network. This paper presents the development and experimental validation of a low-cost hardware module that can easily integrate the machine’s existing control unit into the IoT and enable synchronization of the measurements and states of the variables of the machine and its environment with a cloud server. The developed module is universal, can connect to any control unit that is able to communicate through basic RS232 serial communication, and does not require the control unit to have any higher level communication protocol implemented. On the other end, the presented solution uses a dedicated smartphone application to provide remote monitoring and control of the machine through the cloud by using the synchronized variable states, as well as further possibilities for storing, processing, and analyzing the historical data from the system. The developed solution was experimentally validated on an experimental setup consisting of a conveyor belt driven by a three-phase asynchronous electromotor controlled by a programmable logic controller through a variable-frequency drive.

In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached.