# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 4 System Architecture and Design
### Multi-Layer Edge-Cloud Architecture with DIKWP Agents  
Physically, our system is structured into three main layers, similar to Figure 1's depiction: device layer, edge layer, and cloud layer. This aligns with modern IoT architectures that distribute processing across device/edge and cloud to address bandwidth and latency constraints [44]. However, we enhance each layer with appropriate intelligence:  
* Device Layer: This includes wearable sensors and potentially implantable or home IoT devices (e.g., a smart glucometer, a blood pressure cuff, motion sensors in the room). Each such device has an embedded DIKWP micro-agent (limited by the device's capability). For a simple sensor, the agent might only do data filtering (Data \(\rightarrow\) Information) and minimal knowledge extraction (like flag if reading is out of normal range). More advanced wearables (e.g., a smartwatch) might run a tiny machine learning model (knowledge) and make a local decision (vibrate alarm if serious)--that is a full Data \(\rightarrow\) Information \(\rightarrow\) Knowledge \(\rightarrow\) Wisdom on the device, with Purpose  
Figure 4: Overall architecture of DIKWP-driven smart healthcare platform. set perhaps to \"emergency only\" because you would not want a wearable to constantly distract for minor issues. * Edge Layer: This is typically a smartphone or gateway device near the patient. For someone at home, it could be a home IoT hub; for someone mobile, their phone; in a hospital, maybe an edge server in a ward handling multiple patients' data. The Edge DIKWP Agent here is more powerful: it collects data/knowledge from all device layer entities for a patient, integrates them, and runs the higher DIKWP processes as needed. The edge layer is crucial for intermediate decision-making. It is where many alerts can be decided (or canceled) and where data are packaged semantically for the cloud. The edge agent also communicates with the user interface--for example, showing notifications or explanations to a clinician or patient app. * Cloud Layer: This consists of centralized services--it could be in the hospital data center or on a secure cloud platform that aggregates multiple hospitals or a national health service cloud. We have a Cloud DIKWP Agent that receives updates from edges, retrains models, compares patients, and can issue broad commands. The cloud typically has subsystems like databases, a knowledge base, and possibly an AI engine for heavy tasks. Communication among these layers is facilitated by a secure IoT network, potentially employing software-defined networking principles. For example, a Message Broker (like MQTT) is deployed to route messages topically, making sure that, say, patient's data goes only to the cloud instance handling that patient, and to any authorized subscriber (like a doctor's dashboard) [45]. SDN controllers ensure quality of service for critical health messages. To illustrate the interplay, consider a scenario: A patient is wearing a heart monitor and motion sensor and carrying a phone. The wearable sends heart rate data to the phone continuously (perhaps every second). Most of these data are not urgent. The phone's DIKWP agent (edge layer) processes it, and maybe every minute it compiles a summary (e.g., average, any irregular events = knowledge) and sends to the cloud for record-keeping. Suddenly, the patient's heart rate spikes and the motion sensor indicates a fall. The wearable device agent itself might classify this as an emergency knowledge (\"fall_detected\") and immediately sends that to the phone. The phone agent corroborates with heart data (\"HR spike\") and decides (Wisdom) this is likely a syncopal episode (fainting). It triggers an alert to emergency services (action) [46] and simultaneously sends a high-priority alert to the cloud with the relevant info (to update medical record and notify doctors). The Purpose here (ensuring patient safety) justifies overriding any privacy or normal data suppression--in emergencies, more data can be shared freely by design.