# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 3 DIKWP Cognitive Framework for Smart Healthcare Systems
### Semantic Reasoning and Knowledge Representation  
To support the DIKWP process, we employ semantic reasoning techniques and appropriate knowledge representations at each layer:  
* At the Information (I) level, semantic labeling of sensor data is performed. For example, instead of just storing \"HR = 110\", the system might attach a concept label like \"tachycardia\" if heart rate is above normal for the context. We utilize simple ontologies for vital signs, where ranges of values map to qualitative states (\"normal\", \"elevated\", \"high\"). This adds meaning early in the pipeline [38; 39]. * At the Knowledge (K) level, we represent knowledge using a combination of rules and probabilistic models. Certain medical knowledge is encoded as if-then rules (e.g., IF tachycardia AND low blood pressure THEN possible shock) [40]. These rules are human-understandable and come from medical expertise (guidelines or doctor input). Alongside, we have machine-learned models (like a classifier for arrhythmia from ECG patterns). We treat the output of those models also as \"knowledge\"--for instance, a model might output a probability of arrhythmia, which we then interpret as a knowledge element (e.g., \"arrhythmia_likely\" if probability > 0.9). All knowledge elements are represented in a knowledge graph structure for the patient, which might include nodes like \"symptom: dizziness\" connected to \"condition: dehydration\" with certain confidence. Using a graph allows merging disparate knowledge (symptoms, sensor alerts, history) and reasoning over connections. * At the Wisdom (W) level, the decision is often a choice among actions (alert, log, intervene, ignore). We implement a simple decision logic that selects actions to best satisfy the Purpose given the current knowledge. This can be seen as a utility-based or goal-based agent planning step. For example, if purpose = avoid hospitalizations, the wisdom layer may prefer an action that increases monitoring or calls a telehealth con  
Figure 3: Multi-agent interaction within the DIKWP cognitive architecture for smart healthcare. sultation rather than immediately calling an ambulance, unless knowledge indicates a life-threatening emergency. We formalize this as a small decision tree or state machine that considers the knowledge elements and purpose to output an action. * The Purpose (P) is represented as a set of parameters or rules that the user (patient, caregiver, or system policy) can set. In our prototype, Purpose is configurable per patient. Examples include alert sensitivity (e.g., \"low\" to reduce false alarms, \"high\" for high-risk patients), primary objective (e.g., \"comfort\" vs. \"survival\", which might trade off how aggressive interventions should be), or privacy level (how much data can be shared, guiding if any raw data ever leave the device). Semantic reasoning occurs when the knowledge elements are evaluated against the purpose and context. For instance, the system might reason: \"Patient complains of dizziness (knowledge), and I know from medical context that dizziness + tachycardia could imply dehydration. Purpose says patient is on a diuretic medication (increase dehydration risk) and objective is prevent hospital visit. Therefore wisdom: advise patient to drink water and rest, rather than calling ambulance immediately.\" This line of reasoning involves understanding concepts and relationships (dizziness, dehydration, medication effect) rather than just numeric thresholds. We achieved this by coding a small rulebase using semantic web technologies (OWL/RDF) to define relations like <Medication X> causes <Risk Y>. The DIKWP agent's reasoning engine (a lightweight forward-chaining inference engine) uses these semantic rules to draw conclusions from the available facts (data-information). This approach ensures that the system's decisions are not only data-driven but knowledge-driven and context-aware. It elevates the interactions from mere sensor triggers to something closer to clinical reasoning.