## 7 Experimental Results  
The use of the Dark Net in IoT contexts, particularly for applications like wearable health devices, presents a complex trade-off between enhanced privacy and potential performance limitations. As discussed by [PERSON] [53], while the Dark Net offers significant privacy benefits, it also introduces challenges in terms of standardization and performance. Our study aims to quantify these trade-offs, providing insights that can inform decision-making for IoT deployments in privacy-sensitive contexts. It is crucial to emphasize that the use of Dark Net technologies in IoT should be considered on a case-by-case basis, weighing the privacy benefits against potential drawbacks in performance and ease of use. Traditional secure networking protocols may be more appropriate for many IoT applications, particularly in smart home or industrial contexts. However, for scenarios where anonymity is critical, such as in certain medical or human rights applications, the benefits of Dark Net technologies may outweigh the drawbacks. While the Dark Net offers enhanced privacy and anonymity, it is important to acknowledge the potential trade-offs in terms of performance and usability. As [PERSON] et al. [7] noted, using TOR can introduce additional latency and potentially reduce throughput due to the multiple relay hops involved in routing traffic. These performance impacts must be carefully considered in the context of IoT devices, which often have limited computational resources and power constraints. Our study quantifies these trade-offs, providing empirical data on the performance implications of using Dark Net technologies for MQTT communications in IoT contexts. We observed that while there is a measurable impact on latency and throughput, the degree of this impact varies depending on the specific cipher suite and QoS level used. These trade-offs may be acceptable for applications where the privacy benefits outweigh the performance costs, such as in sensitive health monitoring scenarios. However, alternative security measures may be more appropriate for time-critical or high-bandwidth IoT applications. It is crucial for IoT system designers to carefully evaluate these trade-offs based on their specific use case, considering factors such as data sensitivity, required response times, and the potential consequences of privacy breaches. Our findings provide a foundation for making informed decisions in this complex landscape of security, privacy, and performance in IoT communications. Our focus on MQTT in this study is justified by its widespread adoption and suitability for a wide range of IoT applications. However, we acknowledge that for certain large-scale enterprise deployments, especially those requiring advanced device management features, alternatives like LWM2M might be more appropriate. The choice between MQTT and other  
Figure 5: Physical Deploy for 3 GPP-4G, IEEE 802.3 ab, and IEEE 802.11n/ac testbeds. protocols depends on the specific requirements of the IoT deployment, including scale, management needs, and long-term infrastructure plans. To assess the efficacy of our suggested measuring technique in an operational context, we created a specific test configuration, which is illustrated in Figures 3 and 4. The experimental configuration utilizes a pair of Raspberry Pi 4 units, each equipped with 8 GB of memory. In the MQTT Benchmarking framework illustrated in Figure 3, one unit (the white-cased model) functions as a performance-testing MQTT client, while the other (the exposed black board) operates as the MQTT server. Figure 5 outlines the algorithm steps executed by these Raspberry Pis, using functions like \"mqtt_pub_base-qosX-vY-tsZ\" to invoke tools such as mosquito_pub/mosquitto_sub, mqtt-benchmark, mqttx, and mqtt-cli for testing.