# Improved Performance on Wireless Sensors Network Using Multi-Channel Clustering Hierarchy
## 2 Multi-Channel Clustering Hierarchy (MCCH)
### The Process of Forming a WSN Channel
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channel 3** & **Node** \\ \hline  & 2, 10, 16, 18, 20, 21, 22, 23, 27, 28, 30, 32, 35, 36, \\  & 39, 41, 42, 44, 45, 46, 48, 51, 54, 55, 56, 58, 59, 61, \\  & 62, 63, 64, 69, 72, 75, 77, 79, 80, 81, 84, 86, 88, 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Node channel 3. Table 11 is the number of delay on each channel. Figure 14 shows the distance parameters, number 1 indicates channel 1, number 2 indicates channel 2, number 3 indicates channel 3, and number 4 indicates channel 4. Table 12 is the distance number for each channel. Figure 15 describes the throughput parameters of the MCCH algorithm, number 1 indicates channel 1, number 2 indicates channel 2, number 3 indicates channel 3, and number 4 indicates channel 4. Table 13 is the result of the Throughput of the MCCH algorithm. \begin{table}
\begin{tabular}{c c} \hline \hline
**Channels** & **Parameters** \\ \hline Channel 1 & 508.5165 \\ Channel 2 & 255.5661 \\ Channel 3 & 479.8289 \\ Channel 4 & 646.5618 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Throughput parameters MCCH. \begin{table}
\begin{tabular}{c c} \hline \hline
**Channels** & **Parameters** \\ \hline Channel 1 & 0.0020 \\ Channel 2 & 0.0039 \\ Channel 3 & 0.0021 \\ Channel 4 & 0.0015 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Delay parameter. Figure 14: Distance parameters. Figure 16 describes the throughput value of a previous study entitled Adaptive multi-channel in IEEE.802.11s Wireless mesh networks [26]. Table 14 is the result of conventional multi-channel throughput. ## 4 Discussion

This study presents a methodological approach to the development of wireless sensor networks. The WSN protocol in this study is developed to form a cluster head, where the cluster head is used as a reference channel that will be followed by cluster members. The distances between the cluster head and cluster members are designed to be small in order to save energy when exchanging data. The next step is the process of grouping between \begin{table}
\begin{tabular}{c c} \hline \hline
**Channels** & **Parameters** \\ \hline Channel 1 & 190 \\ Channel 2 & 165 \\ Channel 3 & 125 \\ Channel 4 & 158 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Throughput parameters multi channel. Figure 16: Multi- channel. Figure 15: Throughput parameters MCCH. cluster heads and cluster members by looking for similarities or proximity that will enter the channel group. The development of a protocol on WSN by applying the multi-channel clustering hierarchy (MCCH) method is believed to be able to solve problems such as of delay and data loss. Compared to the previous WSN protocol development method, the main contribution of the proposed method is the formation of 4 channels on the WSN by grouping them between cluster heads and cluster members by looking for the closest similarity between node members and cluster heads. The divide of the WSN channel into 4 channels increases the throughput value of each channel and can reduce delay and data loss. Next, an analysis of the performance of the multi-channel clustering hierarchy method by using QoS parameters such as delay, data loss and throughput is required. The results of the MCCH research are 4 channels in WSN which can improve performance of WSN and optimize the performance. The MCCH method can overcome traffic congestion on the WSN protocol path. This describes the path that was originally 100 nodes with 100% traffic density on one lane broken down to 25% per lane. Thus, the existing traffic density on the WSN path can be overcome by the proposed MCCH method. ## 5 Conclusions

From a series of research activities, the following can be concluded:

The development of the MCCH protocol is done by modifying the routing protocol. Modifications are made by dividing the network into several clusters by selecting the cluster head as a channel reference. The selection of the cluster head is done by spreading 100 nodes and randomly selecting it with the probability formula.