# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 3 DIKWP Cognitive Framework for Smart Healthcare Systems
### DIKWP Cognitive Architecture Overview  
The DIKWP cognitive architecture in our system is a distributed implementation of the DIKWP model, spread across multiple agents (software instances) running at different layers (wearable device, edge node, cloud) [17]. Each agent embodies the full DIKWP stack to some degree, but with differing emphases:  
* Edge Device Agent (Wearable AI): This lightweight agent focuses on the lower DIKWP levels (Data, Information, Knowledge). It directly interfaces with sensors (Data acquisition), performs signal preprocessing (extracting Information like \"heart rate = 110 bpm\"), and can even apply simple Knowledge-based rules (e.g., \"heart rate > 100 bpm and user is resting = possible tachycardia\"). The edge agent can generate local Wisdom in certain cases (e.g., vibrate to alert the user to sit down if dizziness is detected) if it is a time-critical and low-risk decision. * Edge Node Agent (Local Gateway/Smartphone AI): This agent receives processed information or knowledge from one or more edge devices. It has more computational resources to do complex analysis, integrate multiple data streams (e.g., combine heart rate + blood pressure + activity level), and might run more sophisticated knowledge models (like a machine learning model predicting risk of atrial fibrillation from a combination of vital signs). The edge node agent can make intermediate decisions (Wisdom) such as determining that an emergency might be occurring, and it can interact with the user (e.g., through a phone app) for confirmation or additional input. It also enforces the Purpose constraints locally--for instance, if the patient's care plan (Purpose) says \"avoid false alarms at night unless life-threatening,\" the edge node might delay an alert triggered by edge device until it cross-checks severity. * Cloud Agent (Central AI): The cloud agent aggregates knowledge from many patients/devices over longer periods. It represents the higher DIKWP levels (Wisdom and Purpose) more strongly. It can update the global models of knowledge (for example, retrain a risk prediction model on a larger dataset that includes the latest data from all patients, akin to learning new medical knowledge). The cloud agent formulates overarching Wisdom--like determining which patients need urgent attention in a hospital ward--and sets or updates the Purpose for individual edge agents. For example, if the cloud detects a patient's condition deteriorating over days, it might update that patient's Purpose parameter to \"high monitoring priority,\" causing the edge to become more sensitive in detection thresholds. All agents communicate through a DIKWP semantic protocol. Rather than sending arbitrary data, messages are structured to carry DIKWP elements. For example, an edge device might send a message labeled as meaning \"I infer patient might be dehydrated\" rather than raw sensor readings. This semantic labeling ensures that both ends understand the context of the data and can process it accordingly (the cloud knows it is receiving a hypothesis or knowledge piece, not just numbers). Figure 3 schematically illustrates how the DIKWP layers function and interact in the multi-agent system. The five layers (D, I, K, W, P) are depicted not just as a linear stack but as an interconnected network. Data flow upward, transforming at each step, while higher-level influences (Purpose, Wisdom feedback) flow downward or laterally to modulate the processing of lower layers. In our healthcare use-case:  
* The upward flow might be sensor readings \(\rightarrow\) vital sign information \(\rightarrow\) health state knowledge \(\rightarrow\) recommended action (wisdom) \(\rightarrow\) fulfills purpose of care.