## 4 Conclusions  
This study treats the reverse logistics focused on the collection signal algorithm development to automatically determine the proper collection time for disposed S&M e-wastes from collection boxes. Currently, disposed S&M e-wastes are regularly collected once a week and collection boxes are usually filled by midweek. As a result, newly disposed e-wastes are found lying outside and around the collection box and become an easy target for illegal recycl to collect and dismantle them, resulting in several toxic effects on Earth's environment. A simple two-week visit may solve this problem; however, it can lead to an increase in the company's logistics and labor costs. In contrast, IoT and sensor technology bring a chance to increase the efficiency of collection and to save the environment. An algorithm which uses the sensor data from the collection box provides quite accurate information for the fulfillment rate of the boxes. The results from the algorithm could be utilized to make a real-time collection schedule or plans. Therefore, a collection signal algorithm is an essential part of using the concept of Industry 4.0. This study introduces a collection box with sensory technology to help solve the current problems with the way in which S&M e-wastes are being disposed. Sensors are embedded inside the collection box and generate signals whenever a predetermined level of the collection box has been reached with the disposed e-wastes. An experimental approach was applied to develop the collection signal algorithm, which provides analysis results to make a collection schedule. The proposed algorithm would be embedded in the collection boxes with IoT technology. The present study may be extended in the following direction: It is required to develop the collection signal algorithm based on the different data types of sensors. In this paper, the light-emitting sensors were applied due to the total experiment budget. However, there are many different types of sensors such as weight sensors and image sensors. Combining these different types of sensor data could increase the accuracy and the efficiency of the collection activity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Trial** & **Result** & **Group** & **Trial** & **Result** \\ \hline \multirow{5}{*}{21} & 1 & 3 (48\%), 6 (58\%), 12 (68\%), 12 (72\%) & & 1 & 6 (51\%), 12 (61\%), 12 (67\%), 16 (71\%) \\  & 2 & 8 (58\%), 16 (68\%), 16 (72\%) & & 2 & 8 (51\%), 12 (61\%), 16 (67\%), 16 (71\%) \\  & 3 & 6 (48\%), 9 (58\%), 12 (68\%), 16 (72\%) & & 24 & 3 & 6 (51\%), 12 (61\%), 16 (67\%), 16 (71\%, uncovered) \\  & 4 & 4 (48\%), 6 (58\%), 12 (68\%), 12 (72\%) & & 4 & 6 (51\%), 10 (61\%), 12 (67\%), 16 (71\%, uncovered) \\  & 5 & 5 & 5 (51\%), 12 (67\%), 12 (72\%) & & 5 & 5 (51\%), 12 (67\%), 12 (67\%), 16 (71\%, uncovered) \\ \hline \multirow{5}{*}{22} & 1 & 6 (55\%), 6 (56\%), 12 (63\%), 12 (67\%), 16 (71\%) & & 1 & 2 (52\%), 6 (61\%), 12 (67\%), 12 (70\%), 12 (70\%), 16 (71\%) \\  & 2 & 3 (55\%), 6 (56\%), 13 (62\%), 16 (71\%) & & 2 & 4 (61\%), 8 (67\%), 12 (70\%), 12 (70\%), 12 (70\%), 12 (71\%) \\ \cline{1-1}  & 3 & 6 (55\%), 8 (59\%), 3 (12\%), 16 (75\%), 13 (7\%) & 25 & 3 & 6 (61\%), 8 (67\%), 12 (70\%), 12 (70\%), 16 (71\%) \\ \cline{1-1}  & 4 & 4 (56\%), 12 (63\%), 12 (67\%), 16 (71\%) & & 4 & 3 (52\%), 6 (61\%), 8 (67\%), 12 (70\%), 16 (70\%), 16 (71\%) \\ \cline{1-1}  & 5 & 6 (56\%), 9 (3\%), 12 (67\%), 16 (71\%) & & 5 & 2 (52\%), 8 (61\%), 10 (67\%), 12 (70\%), 12 (70\%), 12 (71\%) \\ \hline \multirow{5}{*}{23} & 1 & 3 (55\%), 6 (59\%), 11 (68\%), 12 (65\%), 16 (76\%) & & & \\  & 2 & 2 (55\%), 5 (59\%), 9 (65\%), 9 (65\%), 12 (76\%) & & & \\ \cline{1-1}  & 3 & 8 (59\%), 12 (65\%), 12 (65\%), 16 (76\%) & & & \\ \cline{1-1}  & 4 & 2 (55\%), 6 (59\%), 12 (65\%), 12 (65\%), 16 (76\%) & & & \\ \cline{1-1}  & 5 & 4 (59\%), 6 (65\%), 8 (65\%), 12 (67\%) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results: number of activated sensors (accumulated volume).