# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 2 Related Work
### The DIKWP Model and Artificial Consciousness
#### 2.3.2 Artificial Consciousness  
Artificial consciousness is a field of research aiming to endow machines or software agents with aspects of consciousness, including self-awareness, understanding, intentionality, and adaptability. While true human-like consciousness in machines remains a topic of philosophical debate, practical frameworks of \"machine consciousness\" have been proposed to enhance AI systems with more advanced cognitive abilities. These frameworks often draw from cognitive science and psychology, seeking to replicate how humans integrate perception, memory, knowledge, and goals to guide intelligent behavior. One such framework is the DIKWP model. A key aspect of DIKWP theory is the potential for bidirectional interactions among the layers. Unlike a simple one-way pipeline, human cognition has feedback loops--higher-level understanding can cause us to reinterpret raw data (for instance, knowing a patient's context might make a doctor revisit an abnormal lab result in a different light). The DIKWP model acknowledges that while data typically flows upward, higher layers (Knowledge, Wisdom, or Purpose) can send guidance downward in the form of expectations, filters, or focus. [PERSON] et al. suggest that DIKWP layers do not only have a linear adjacency to each other, but can communicate directly as needed [25]; for example, a Purpose can directly shape how data are collected (e.g., a goal to detect arrhythmias might trigger higher-frequency ECG sampling at the Data layer), or a Wisdom-level decision might trigger seeking new raw data to confirm itself [13]. In other words, the model allows dynamic feedback akin to a cognitive loop, rather than a rigid sequential pipeline. In the context of artificial consciousness, the DIKWP model provides a scaffold to implement what might be called a cognitive architecture or high-level reasoning loop. It captures elements of perception (Data \(\rightarrow\) Information), comprehension (Information \(\rightarrow\) Knowledge), deliberation (Knowledge \(\rightarrow\) Wisdom), and motivation (P), which collectively could produce a form of machine \"awareness\" of its environment and objectives. Notably, DIKWP-based AC emphasizes semantic and symbolic reasoning in addition to numeric computation. Each layer's content can be represented in a human-interpretable form (for example, symbolic labels for concepts at the Knowledge level), facilitating explainability. This naturally lends itself to systems that can explain their reasoning: an AC system built on DIKWP can in principle trace the transformation from raw data to final decision, referencing intermediate information, knowledge, and the purpose that influenced the outcome. Because decisions are made through an explicit chain of reasoning (rather than emerging opaque from a black-box neural network), one can audit each step--i.e., what data were considered, what information was derived, what knowledge/hypothesis was formed, and how the purpose guided the chosen action. This kind of transparency is valuable in medicine, where trust and accountability are paramount [8,26]. There is an expanding body of literature beginning to apply the DIKWP model and related frameworks in practical scenarios. Recently, [PERSON] et al. have utilized the DIKWP paradigm for enhancing doctor-patient interactions through semantic modeling, effectively bridging communicative gaps in clinical contexts [12]. For example, [PERSON] et al. describe a semantic DIKWP model to mediate doctor-patient dialogues, ensuring that the AI captures both the medical knowledge context (Data \(\rightarrow\) Information \(\rightarrow\) Knowledge \(\rightarrow\) Wisdom)and the patient's intentions or concerns (P) to produce more relevant and empathetic responses [12]. Other researchers have further extended the applicability of this model to multi-agent systems and hardware-level implementations, particularly focusing on DIKWP resource modeling, semantic transformation, and security protection within edge computing environments.