## 4 Proposed Methodologies  
### FL Employment  
As discussed previously, an excellent BER is more important than the data rate in eHealth applications. The network's capacity is almost negligible, because the monitoring system does not require a similar data rate to voice or video calling. However, the outage probability, which is a function of the achievable SINR, should be given significant attention. The link blockage probability for OCC is also important for reliable communication. Based on these issues, the major performance factors for OCC are regarded as the average SINR, instantaneous received power, and communication distance. These features should be inspected and analyzed before finalizing OCC as the data transmission system. However, setting a certain threshold value of the parameters that should be considered while selecting OCC is particularly inconvenient. Therefore, we envisage FL to assist in the selection mechanism. FL is a computing approach that utilizes degrees of truth values ranging from 0 to 1 [60; 61] rather than using only \"true or false\" when making decisions. We employed the Mamdani fuzzy inference system to assess the selection process of OCC. Three steps are considered in FL, namely fuzzification of performance parameters, assessment of different \"if/then\" rules, and defuzzification. Fuzzification is a process that transforms crisp inputs into fuzzy output quantities. The inputs are fuzzified by using numerous fuzzifiers, also referred to as membership functions (MFs) [62]. These functions are utilized to represent a fuzzy set graphically. MFs have different types. We used MFs with different numbers of breakpoints for various inputs. Four input parameters are considered in the fuzzification process. We chose these parameters based on their effects on the OCC performance. The fuzzification process of SINR is illustrated in Figure 4. Four membership grades are chosen, such as low, average, high, and excellent, ranging from \(-15\) to \(45\) dB. The selected values of the breakpoints \(a\), \(b\), \(c\), and \(d\) are \(-15\), \(0\), \(15\), and \(30\) dB, respectively. Among the parameters, the fourth parameter that we considered is the number of strips projected inside the image sensor. The significance of this parameter is indisputable when considering OCC for data transmission. As discussed in Section 3, below \(\Gamma_{\mathrm{min}}\), the data bits cannot be extracted, although the LED image is projected in the image sensor. We performed several experiments on the training data and eventually chose the MFs. The grade breakpoints of the MFs were selected based on the variations of the parameters with distance prior to outage. Eventually, we selected four triangular MFs for the fuzzification of the SINR and three each for the other input parameters. The fuzzification process is followed by the assignment of \"if/then\" rules. The rules are generated on the basis of the considered indoor scenario. The assumption of a patient's movement was apparent while establishing the rules, because the receiving camera or BLE receiver is a static object. However, mobile robots can be utilized for remote monitoring, which becomes the only case based on which the rules can be employed. We performed the evaluation of the rules by several \"anding\" operations. The output is denoted by five triangular MFs to obtain a precise result. Finally, the defuzzification step generates a score based on the evaluation rules, which is represented by a crisp value. The inputs can be defuzzified by various methods, such as centroid, bisector, smallest maximum, and largest maximum. We adapted the centroid method owing to its superiority among the other methods. The obtained score, also defined as the selection score (SS), is represented by the following equation.