## 5 UbiPriSEQ: System Evaluation  
We now evaluate the performance of UbiPriSEQ using SpMV computations (see Section 1). We have implemented the UbiPriSEQ framework in Python over the TensorFlow platform. The UbiPriSEQ system is evaluated using a real-life application of prime significance, Sparse Matrix-Vector product (SpMV). SpMV computations are fundamental to many scientific, engineering, business, and social applications that provide timely intelligence for the design, operations, and management of ubiquitous environments [60; 61; 62; 63; 64; 65; 66; 67]. SpMV is also utilized in newer compressed Deep Neural Networks with pruned layers for computations [68]. The sparse matrices used for our experiments are from the University of Florida Sparse Matrix Collection consisting of a varied number of applications such as thermal, economics, optimization, and structural design ([[https://sparse.tamu.edu/](https://sparse.tamu.edu/)]([https://sparse.tamu.edu/](https://sparse.tamu.edu/))). We have studied the performance of the UbiPriSEQ system for a range of SpMV computations and metrics including SINR, privacy metric, latency, and utility function and have compared it with constrained Markov Decision Process (CMDP) [44]. The experiments were run on a machine with 12 core Intel Xeon CPU with clock frequency of 3 GHz, 32 GB RAM, and an Nvidia Quadro GPU. The edge nodes based on Jetson Nano has a Quad-core ARM A57 CPU with 1.43 GHz frequency, 4 GB RAM, and Ethernet connectivity. The storage of fog nodes is using microSD. The experiments were performed based on one-second time slots. The number of devices in IoT Devices Layer to the number of fog nodes is 1:3. The \(\epsilon\) for the greedy selection in DRL is \(x^{t}\), where \(x\) is a real number in the range \((0,1)\), and \(t\) is the time [57]. The default network parameters used in the experiments have been depicted in Table 1. The jamming attacks are modeled as interfering signals/noise in the experiment. Some devices and fog nodes are modeled as \"rogue\" and UbiPriSEQ detects and defend itself from these. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline System bandwidth & 10 MHz \\ Transmit power of user nodes & 10 dBm \\ Number of fog nodes & 30 \\ Number of user nodes & 90 \\ Processing Density & 4 double-precision FLOPS/cycle [69] \\ Fog compute power & 10 GHz \\ Sparse Matrix Size & [10, 40,000] KB \\ Learning Rate & 0.8 \\ Discount Factor & 0.7 \\ Privacy weight & 5 \\ Task arrival rate & [0, 5] \\ Selection probability: \(\epsilon\) & \(x^{t}\), \(\forall x\in(0,1)\), \(t\in\mathbb{R}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: UbiPriSec: Experimental Network Parameters. Figure 3 compares performance of UbiPriSEQ (both DevM  and NetM  combined) with CMDP in terms of PrivacyMetric, SINR, latency, and utility against clock-time. The utility is the average of all DevM and NetMutilities (Sections 4.3.4 and 4.4). The PrivacyMetric is also an average (Sections 4.3.2 and 4.4). The latency is the average latency including the tasks' aggregated computational costs, and the queuing and transmission delays. Figure 2(a) compares provided privacy levels and shows that UbiPriSEQ  outperforms CMDP by 25% on average (computed by the average difference between the two privacy levels). UbiPriSEQ achieves a higher-level of privacy at a faster rate due to the data migration policies not seen in Reference [44]. We achieve a higher QoS due to better defenses against the attacks. Figure 2(b) plots SINR and shows an average improvement in the network QoS by 22% in the presence of jamming signals and eavesdroppers. Figure 2(c) shows that UbiPriSEQ minimizes the average latency at a faster rate then CMDP with the maximum difference against CMDP of 74% \(2.19\times 10^{5}\) seconds (lower difference in latency is seen later after the systems stabilize). Figure 2(d) shows better average utility for UbiPriSEQ compared to CMDP, 33% on average.