## 8 Discussion
For AES256-GCM-SHA384, the high computational overhead associated with 256-bit key encryption and SHA384 hashing increases the processing load, particularly on devices with limited hardware optimization. This \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Communication Media** & **Cypher Suite** & **MQTT Version** & **TLS Version** & **WORST** & **BEST** \\ \hline IEEE 802.3 ab & AES128-GCM-SHA256 & 3.11 & 1.2 & & 21,124 \\ \hline IEEE 802.3 ab & AES128-SHA & 3.11 & 1.2 & 16,830 & \\ \hline IEEE 802.3 ab & DHE-RSA-AES128-SHA & 5.0 & 1.2 & & 21,018 \\ \hline IEEE 802.3 ab & AES128-SHA & 5.0 & 1.2 & 16,888 & \\ \hline IEEE 802.3 ab & TLS-CHACHA20-POLY1305-SHA256 & 3.11 & 1.3 & & 21,145 \\ \hline IEEE 802.3 ab & TLS-AES-128-GCM-SHA256 & 3.11 & 1.3 & 20,881 & \\ \hline IEEE 802.3 ab & TLS-AES-128-GCM-SHA256 & 5.0 & 1.3 & & 20,896 \\ \hline IEEE 802.3 ab & TLS-CHACHA20-POLY1305-SHA256 & 5.0 & 1.3 & 20,632 & \\ \hline IEEE 802.11n/ac & ECDHE-RSA-AES128-SHA256 & 5.0 & 1.2 & & 20,831 \\ \hline IEEE 802.11n/ac & DHE-RSA-CHACHA20-POLY1305 & 5.0 & 1.2 & 17,487 & \\ \hline IEEE 802.11n/ac & ECDHE-RCDSA-AES128-GCM-SHA256 & 3.11 & 1.2 & & 21,169 \\ \hline IEEE 802.11n/ac & DHE-RSA-CHACHA20-POLY1305 & 3.11 & 1.2 & 15,137 & \\ \hline IEEE 802.11n/ac & TLS-CHACHA20-POLY1305-SHA256 & 5.0 & 1.3 & & 20,619 \\ \hline IEEE 802.11n/ac & TLS-AES-128-GCM-SHA256 & 5.0 & 1.3 & 19,548 & \\ \hline IEEE 802.11n/ac & TLS-AES-256-GCM-SHA384 & 3.11 & 1.3 & & 20,803 \\ \hline IEEE 802.11n/ac & TLS-CHACHA20-POLY1305-SHA256 & 3.11 & 1.3 & & 20,608 \\ \hline
3 GPP+G & DHE-RSA-AES256-SHA & 3.11 & 1.2 & & 21,118 \\ \hline
3 GPP+G & ECDHE-RSA-AES256-SHA384 & 3.11 & 1.2 & 12,751 & \\ \hline
3 GPP+G & ECDHE-ECDSA-AES128-SHA256 & 5.0 & 1.2 & & 20,786 \\ \hline
3 GPP+G & AES128-SHA & 5.0 & 1.2 & 17,491 & \\ \hline
3 GPP+G & TLS-AES-128-GCM-SHA256 & 3.11 & 1.3 & & 20,918 \\ \hline
3 GPP+G & TLS-AES-256-GCM-SHA384 & 3.11 & 1.3 & 20,321 & \\ \hline
3 GPP+G & TLS-AES-128-GCM-SHA256 & 5.0 & 1.3 & & 20,682 \\ \hline
3 GPP+G & TLS-AES-256-GCM-SHA384 & 5.0 & 1.3 & 20,425 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results: The table presents the bandwidth extremes (in messages per second) observed during testing, with QoS0 measurements serving as the reference point. suite also requires significant computational resources, which can result in increased latency, especially under conditions of high data load or multiple concurrent connections. Furthermore, strict TLS configurations aimed at enhancing security may also impact performance, compounded by potential inefficiencies in software or firmware implementations. Similarly, DHE-RSA-CHACHA20-POLY1305 suffers from significant overhead due to the Diffie-Hellman key exchange process, which adds complexity to the encryption procedure. Additionally, the CHACHA20 cipher, while effective, may not be fully optimized on all hardware platforms, contributing to slower performance. The integration of POLY1305 further complicates the encryption process, affecting speed. As with AES256-GCM-SHA384, network conditions and high workloads exacerbate the latency, indicating the need for optimization in both software implementation and configuration. Numerical tests have revealed significant findings. With an IEEE 802.3 ab connection, the use of the dark web increases communication delay by 11% for QoS0, 12% for QoS1, and 13% for QoS2. This increase is likely due to the additional negotiations required to establish hidden connections in the TOR network. These results highlight the trade-offs between enhanced privacy and dark web-based IoT communications performance. Our methodology proves crucial for evaluating potential network throughput under specific privacy and QoS requirements. It enables a more accurate determination of sampling frequencies and characteristics for DMS nodes. This approach allows for the extension of DMS services to contemporary applications that demand high levels of privacy.