# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 2 Related Work  
### Smart Healthcare, IoT, and Edge Computing  
In recent years, IoT-based smart healthcare systems have gained momentum as a means to improve patient outcomes and reduce burdens on facilities [3, 15]. IoT smart healthcare refers to an infrastructure where wearable sensors, implantable devices, smartphones, and ambient environmental sensors continuously collect health-related data for analysis, enabling applications such as early disease detection, remote patient monitoring, and personalized treatment adjustments [4]. These range from smart home health monitoring for the elderly to hospital smart wards and even city-wide public health surveillance as part of smart city initiatives [16]. A defining characteristic of IoT healthcare data is that it is distributed and heterogeneous. Vital-sign sensors generate time-series data (e.g., heart rate, blood pressure), imaging devices produce complex medical images, and ambient sensors (room temperature, motion) add contextual information. Traditionally, all these data were sent to centralized servers or the cloud for processing. However, the cloud-centric model can suffer from high latency, network bandwidth constraints, and increased risk of sensitive personal data being exposed or intercepted in transit. To mitigate these issues, there is a strong trend toward edge computing in healthcare IoT, which involves processing data closer to where they are generated (e.g., on the device or a nearby gateway) rather than relying solely on remote clouds. By performing computations locally, edge computing reduces the amount of raw data that must be transmitted and enables faster, real-time responses when needed [3, 4]. This approach can improve overall system performance and alleviate privacy concerns by keeping personal data near their source. Several studies have explored edge or fog computing architectures for health monitoring. A general three-layer architecture (Devices-Edge-Cloud) is often proposed, where wearable or implantable devices form the perception layer, sending data to a nearby gateway or smartphone (the edge layer) for intermediate processing, and then a cloud layer handles heavier analytics and long-term storage [14]. Figure 1 illustrates a representative architecture from the literature for an edge-cloud enabled healthcare system, consisting of a wearable device (Edge Device Layer), an intermediary edge node (Edge Node Layer,e.g., a smartphone or local gateway), and cloud services (cloud layer). This layering is widely adopted to balance the load and improve scalability. The wearable or sensor at the Edge Device Layer acquires raw physiological data; the edge node aggregates data from multiple local sensors and performs preliminary processing (e.g., filtering, feature extraction, or packaging data in a standard format such as JSON); finally, the cloud layer runs advanced machine learning algorithms on the aggregated data and provides long-term data storage and remote access for healthcare providers [1]. Such an architecture has been demonstrated, for example, in a smart health framework that uses a wearable multi-sensor device, a smartphone edge gateway, and cloud analytics, achieving efficient data handling and system scalability [17]. Empirical research in smart healthcare has also shown the benefits of distributing computation across these layers. For instance, a case study on non-invasive glucose monitoring employed a wearable edge-IoT device to preprocess sensor readings and send only summarized features to the cloud for predictive modeling; this approach achieved accurate glucose level predictions while significantly reducing cloud communication load. Another study introduced a fog computing architecture with local processing for vital sign monitoring, showing that reducing latency at the edge is critical for time-sensitive applications like arrhythmia detection [18; 19].