# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 3 Varying Alert Thresholds  
In our DIKWP framework, one configurable parameter is the alert threshold--e.g., the threshold on the anomaly score or specific vital sign level that triggers an alert. We conducted experiments with three different threshold settings (labeled low, medium, and high threshold) to further quantify this trade-off and identify an optimal setting. A low threshold means the system triggers alerts on small deviations (similar to the high sensitivity mode above), a high threshold means only large deviations trigger an alert (similar to low sensitivity above), and medium is an intermediate value. Table 8 presents the performance metrics under these three threshold levels. The trends are consistent with the earlier sensitivity analysis: at the low threshold, the system detects nearly all anomalies (100% of events in our test were caught) but the false alert count is high (precision drops, with \(\sim\)15% of alerts being false alarms). Users in this scenario gave a somewhat lower satisfaction rating (\(\sim\)3.5/5) due to frequent unnecessary alerts. The high threshold setting, conversely, yielded almost no false alarms (only the most significant events triggered alerts, false alarm rate \(\sim\)0%) and users reported fewer interruptions (satisfaction \(\sim\)4.2/5 since they were rarely bothered by alerts); however, the detection rate fell to \(\sim\)85%, meaning some milder events did not trigger any alert at all. The medium threshold provided a balanced outcome--a high detection rate (\(\sim\)95%) with a low false alarm rate (\(\sim\)2%), and the highest average user satisfaction (\(\sim\)4.5/5). This suggests that the medium threshold (our default in other experiments) is close to the optimal point on the precision-recall curve for this application. From a latency perspective, lower thresholds produced faster alerts (often immediate at the first sign of anomaly), whereas higher thresholds sometimes introduced slight delays while waiting for the signal to breach the higher limit. These results reinforce the importance of choosing an appropriate alert threshold: it directly influences the wisdom of the DIKWP agent's decisions, i.e., ensuring that alerts are neither too frequent (crying wolf) nor too scarce. In a real deployment, this threshold could be configurable per patient or context, or even dynamically adjusted by the system to maintain an acceptable false alert rate. Appropriate threshold selection is crucial. A threshold too low causes many false alarms (eroding user trust), while too high a threshold can miss important warnings. The medium threshold here achieves a near-optimal balance, as reflected in the highest user satisfaction score. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **High Sensitivity** & **Low Sensitivity** \\ \hline Event detection rate (recall) & 99\% & 90\% \\ False alarm rate & 10\% & 1\% \\ Avg. detection latency & 1.5 s & 3.0 s \\ Avg. power consumption & \(\sim\)60 mW & \(\sim\)50 mW \\ Alerts per day & 12 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance under high-sensitivity versus low-sensitivity configurations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Low Threshold** & **Medium Threshold** & **High Threshold** \\ \hline Event detection rate (recall) & 100\% & 95\% & 85\% \\ False alarm rate & \(\sim\)15\% & 2\% & \(\sim\)0\% \\ Avg. detection latency & \(\sim\)1 s & 2 s & 3 s \\ User satisfaction & 3.5/5 & 4.5/5 & 4.2/5 \\ Alerts per hour & 2.0 & 0.5 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance metrics under different alert threshold settings. System Architecture Resource Analysis  
To understand the feasibility and scalability of the DIKWP-based artificial consciousness in practical IoT deployments, we analyzed the resource usage of each major component of the system's architecture. Recall that our DIKWP agent is composed of four main functional components: data preprocessing, inference, reasoning, and communication.