###### Abstract  
It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the two conflicting demands is to continuously monitor the animals. In this article, rumen bolus sensor techniques are reviewed, as they can provide lifelong monitoring due to their implementation. The applied sensory modalities are reviewed also using data transmission and data-processing techniques. During the processing of the literature, we have given priority to artificial intelligence methods, the application of which can represent a significant development in this field. Recommendations are also given regarding the applicable hardware and data analysis technologies. Data processing is executed on at least four levels from measurement to integrated analysis. We concluded that significant results can be achieved in this field only if the modern tools of computer science and intelligent data analysis are used at all levels. rumen bolus; data processing; artificial intelligence; machine learning; dairy cattle 202  
In the case of dairy cattle, the application of sensor technology is a rapidly evolving field. A study published in 2010 on trends in dairy farming technologies does not even mention cattle-monitoring IoT techniques [2]. Similar studies in 2019 [3] and 2020 [4] clearly put their vote in favor of continuous monitoring of cows as one of the most important technologies proposed in cattle farming. Animal-based sensors can be divided into two groups; there are devices for research purposes and commercially available on-farm decision support systems. Separation is not necessarily differentiated according to the level of development (according to TRL levels), but the purpose of the two sensor groups is different, and this is particularly pronounced depending on the IoT technologies used. One helps research tasks with objective measurements, and only a small proportion of them are currently dedicated to the day-to-day practice of dairy farms. Research-aimed sensors provide primary measured quantities, which will be evaluated and interpreted by the researchers according to the aspects of the actual research question and with statistical methodology [5; 6; 7; 8; 9; 10; 11; 12]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensor Type by Placement** &
\begin{tabular}{c} **Placement** \\ **without Hurt** \\ \end{tabular} & **Usage Length** & **Aim of Data Processing** \\ \hline
1 & Body surface ECG equipment & + & 1 to 2 days & processed measurement result \\ \hline
2 & Neck rumination sensor & + & few weeks–3 years & processed measurement result \\ \hline
3 & Leg activity sensor & + & 1–3 weeks & processed measurement result \\ \hline
4 &
\begin{tabular}{c} Rumen sensors, \\ originally pH measurements \\ \end{tabular} & + & 3 months–5 years & processed measurement result, complex expert opinion/alert \\ \hline
5 & Tail, ear estrus detector & + & few days & processed measurement result complex expert opinion/alert \\ \hline
6 & Vaginal calving alarm thermometer & + & up to 1 week & processed measurement result complex expert opinion/alert \\ \hline
7 & Subcutaneous sensor & - & 5 years & processed measurement result complex expert opinion/alert \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensor groups according to the placement location and methodology, usage length, aim of data processing. Figure 1: Dairy cattle “at cow” sensors according to Table 1. 1. Body surface ECG equipment, 2. Neck rumination sensor, 3. Leg activity sensor, 4. Rumen sensors, 5. Tail, ear estrus detector, 6. Vaginal partition detector, 7. Subcutaneous sensor. At the same time, there are more expectations in dairy farms today. Tools and methods need to be developed to provide a complex expert support by assessing behavioral and physiological characteristics in a complex way, thus helping farmers to make decisions [1; 4; 13; 14; 15].