# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 5 Results and Discussion
### Scenario 2: Resilience to Noise  
In this scenario, we designed two experiments to assess the robustness of the proposed algorithm for implementation in IoT. The first experiment computes the bit error rate (\(BER\)) on our stego channel in the \(AWGN\) channel and the second experiment measures the dependency of the expected throughput of the stego channel on the signal-to-noise ratio (\(SNR\)). In each experiment, all modulations and coding schemes such as \(BPSK\), \(QPSK\), \(16-QAM\), and \(64-QAM\) are tested. Our results show that the proposed algorithm while using \(64-QAM\) and \(16-QAM\) modulations achieves a better average throughput performance than other modulation schemes (Figure 8a), i.e., at \(SNR=10\) dB, we registered almost 1 Mbps using \(64-QAM\) modulation against 0.5 Mbps in \(QPSK\) modulation. In all modulations schemes, throughput values increase as \(SNR\) increases. A further analysis of the behavior of the proposed algorithm using all modulation and coding schemes in a noisy environment (Figure 8b) shows that the \(BER\) level proportionally increases when the noise level alleviates in all tested modulation schemes. In addition, Figure 9 demonstrates that the algorithm is resilient to noise even at low \(SNR\) and is almost equivalent to the performance of the channel without engaging the stego channel. There is, however, an inherited percentage of uncontrollable erroneous bits due to channel noise, which is not related to our algorithm. Figure 9a-d show that the proposed algorithm did not induce additional \(BER\), since the \(BER\) rate is almost the same with and without engaging our algorithm. The \(BER\) shown in the above figures is due to the channel condition which is not the focus of this paper. In \(BPSK\) modulation, Figure 9a, for instance, at \(SNR=-10\) dB, we registered 0.3274% of \(BER\) while using the channel without engaging data hiding versus 0.3298% in the presence of the stego channel. The \(BER\) values overlap, in the channel with and without the proposed steganographic algorithm, starting from \(SNR=-5.4\) dB. This behavior was observed for all modulation schemes. There will be, however, a successful payload retrieval trade-off between the maximum throughput and the channel noise level. This trade-off does not impact the quality of the retrieved hidden data if the latter are a signal or image. The visual and auditory human system tolerates a certain level of degradation. However, the payload of nature text or number, while using \(16-QAM\) or \(64-QAM\) with \(SNR=-10\) dB and throughput around 0.25 Mbps (Figure 8a), will survive at a relatively higher \(AWGN\) power i.e., 6 dB and up. Overall, our algorithm shows resilience to noise for \(SNR\) values, from low to high \(dB\) values. Therefore, the \(BER\)  
Figure 7: \(SNR\), \(PESQ\) and payload (Kbps) test results using frames of 4 ms length and varying LSB key varies from 1 st to 4 th LSB layer.  
analysis demonstrates the scheme's capability in terms of reducing the IoT retransmission probability and therefore increasing the traffic load.