# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 2 Related Work
### The DIKWP Model and Artificial Consciousness  
#### 2.3.1 The DIKWP Model  
The DIKWP model was proposed by [PERSON] et al. [12; 22; 23]. This model extends the classic DIKW hierarchy by introducing \"Purpose\" as an essential component. According to the DIKWP model, the transformation from Data to Wisdom is fundamentally incomplete unless guided by an explicit purpose or intent. Within this context, Purpose (P) encompasses the objectives, motivations, or overarching directives that influence cognitive processes, mirroring the role of goals and needs in human decision-making [24]. The DIKWP model incorporates dynamic and bidirectional interactions among its layers, evolving the traditional hierarchical structure into a more flexible, networked representation capable of describing complex cognitive processes. The DIKWP model is illustrated in Figure 2.  
To illustrate DIKWP in a healthcare scenario, consider the task of medical diagnosis, broken down as follows:  
* Data (D): Raw inputs such as patient sensor readings, reported symptoms, and lab results, are essentially unprocessed facts. For example, an IoT wearable provides an ECG waveform and a thermometer reading. * Information (I): Processed data that yield meaningful values or patterns. This could involve calculating a heart rate from the ECG waveform or recognizing that a body temperature of 38.5 \({}^{\circ}\)C indicates a fever. At the information stage, data are filtered, formatted, and given basic interpretation (e.g., identifying that \"heart rate = 110 bpm\" or \"fever present\"). * Knowledge (K): Contextualized information applied to models or rules to infer higher-level insights. Here the system might combine multiple pieces of information (fever + cough + low blood oxygen) and apply medical knowledge (learned associations or rules) to recognize a potential condition (e.g., these signs together might indicate pneumonia). This stage is akin to forming a hypothesis or an intermediate conclusion using domain knowledge. * Wisdom (W): The actionable decision or recommendation based on the knowledge. In this example, the system arrives at a diagnosis or a treatment suggestion--e.g., \"patient likely has pneumonia; recommend antibiotic X and schedule a chest X-ray.\" Wisdom implies that the system not only infers what is happening, but also determines what should be done about it. * Purpose (P): The overarching goals or constraints that guide the entire process. For instance, the purpose might be \"ensure patient safety and comfort while minimizing unnecessary interventions.\" This goal can influence all the preceding layers--what data to focus on, which patterns are considered significant, and what is deemed an appropriate action. In our example, if the purpose emphasizes avoiding unnecessary intervention, the system might be conservative in diagnosing pneumonia (perhaps suggesting observation or additional tests first); conversely, if the purpose is life-saving at all costs, the system might aggressively flag pneumonia at the earliest hint, to begin treatment immediately. The inclusion of Purpose imbues the DIKWP process with contextual awareness and adaptability. Each step \"is guided by our purpose or intent,\" meaning that what information is extracted, what knowledge is inferred, and which decision is considered wise may all depend on what we are trying to achieve. In a dynamic medical environment, such goal-driven adaptability is critical. For example, in emergency care, the primary goal (purpose) may prioritize rapid action over exhaustive analysis, whereas in managing a chronic condition, the goal might prioritize patient comfort and long-term monitoring,  
Figure 2: The DIKWP model. leading to more measured decision-making. A DIKWP-based system can, in principle, adjust its behavior according to such purpose settings.