# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 3 Varying Alert Thresholds
These roughly correspond to the DIKWP pipeline stages--data processing (Data \(\rightarrow\) Information), machine inference for pattern recognition (Information \(\rightarrow\) Knowledge), higher-level reasoning for decision-making (Knowledge \(\rightarrow\) Wisdom), and the communication or actuation of decisions (applying wisdom toward a purpose, Wisdom \(\rightarrow\) Purpose). We assessed each component's requirements in terms of CPU utilization, memory footprint, and power consumption on three classes of hardware: a smartwatch (wearable device), a smartphone, and an edge hub (a local gateway or mini-server). Figure 9 summarizes the estimated resource usage for each component across these platforms. These estimates are based on profiling our prototype implementation and known specifications of typical devices (e.g., smartwatch with \(\sim\)1 GHz ARM CPU, 512 MB-1 GB RAM, 300 mAh battery; smartphone with octa-core CPU, 4 GB RAM, \(\sim\)3000 mAh battery; edge hub with quad-core CPU, 8+ GB RAM, wall power). Several important observations can be made from Figure 9. First, the data preprocessing component is lightweight on all the devices. On the smartwatch, basic filtering and feature extraction uses roughly 5-10% of the CPU (on one core) and under 1 MB of memory, consuming only a few milliwatts of power. This is efficient enough to run continuously on a wearable. The smartphone and edge hub easily handle preprocessing with negligible load (\(<\)2% CPU). The inference component (which might involve running a machine learning model on sensor data) is more demanding. On a resource-constrained smartwatch, running a complex inference (e.g., a neural network) could use \(\sim\)20% CPU and a few MB of memory per inference, which in continuous operation would draw on the order of tens of milliwatts of power. In our design, heavy inference can be offloaded to the smartphone or hub: on a smartphone, the same model might use only \(\sim\)5-10% CPU (thanks to more powerful processors and possibly hardware accelerators) but require more memory (e.g., 20-50 MB to store the model). The power cost on the phone for inference is higher in absolute terms (\(\sim\)100 mW) but acceptable given a larger battery. The edge hub, having significant computing power, would see minimal CPU impact (\(<\)2%) for inference and can easily accommodate model memory; power is not a limiting factor for the plugged-in hub. The reasoning component (which implements the higher-level conscious reasoning, knowledge integration, and decision logic) tends to be computationally heavy due to  
Figure 9: Estimated resource usage for each DIKWP agent component on different hardware platforms. (_Note: The edge hub is typically wall-powered; thus, its power consumption is not a limiting factor_). complex algorithms or knowledge base queries. If one attempted to run full reasoning on a smartwatch, it might consume at least 30% of the CPU and several MB of memory, which is impractical for sustained use (and would drain a watch battery quickly). In our architecture, the intensive reasoning tasks are assigned to the smartphone or edge hub. On the smartphone, reasoning algorithms use roughly 10% of CPU and around 5-10 MB of RAM during operation, consuming on the order of 30-50 mW--a moderate load. The edge hub can execute the reasoning with plenty of headroom (e.g., 15% CPU of a small hub device, using ~100 MB RAM if it maintains a substantial knowledge base or context history). Finally, the communication component (which handles transmitting data and alerts between devices or to the user) has modest resource requirements. On the smartwatch, communication (typically via Bluetooth to the phone) uses ~5-10% CPU intermittently and a tiny memory buffer (<0.5 MB), but wireless transmission can be a significant energy draw (~15 mW for Bluetooth during data transfer). On the smartphone, communication either to the edge hub or directly to cloud/services might use a similar small CPU load and memory, with power consumption around 20 mW when using Wi-Fi or cellular for alerts.