# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 3 Materials and Methods
### Proposed Framework  
This article presents a Dynamic Caching Scheme (DCS) to improve the freshness of the cache contents and network performance. The proposed technique neither needs extensive computations nor tags, filters, metadata, and additional network communication that create extra overhead in IoT networks. Moreover, if the source node and user are at longer distances, it may extend the retrieval delay because of the smaller size of the cache nodes. In the IoMT network, each content has a lifetime and is expected to expire in some instance of time. The proposed framework is designed to dynamically push popular content towards edge nodes over the network by changing the data ages. Data ages are used to handle the lifetime of content over the network while removing the contents that are no longer needed. Each content in the cache has an age that decides the lifetime of the cached content. Popular content over the network has a longer age. First, at the router node, the content age is estimated in correlation with the freshness of the data item. If the age of the data item is not matched with the source node, it is removed from the cache. In the proposedframework, an Interest Record Table (IRT) integrated with cache control is added at the router to improve the working of CS while avoiding traffic load at the source node. Figure 1 illustrates the workflow of popular content across the network. This network consists of a client and server, each connected to their respective routers, \(R1\) and \(R2\). The server is responsible for generating two types of content: popular and less popular. Notably, both routers can store at least one content item at a time. In deciding whether to cache data or not, especially for transient small-sized data, a \"period\" field is introduced within the packet. This field supplies updated data directly from the source node. To accomplish this, the system incorporates the content's age with the data item, with the first router node establishing the initial age baseline. The quantitative findings indicate that when data items are not cached at the nodes, this incurs a higher cost in the IoT network. Caching policies are typically implemented in intermediate nodes. Each node possesses specific cache threshold requirements and can dynamically adjust these thresholds based on the request rate. To minimize retrieval delays, edge nodes employ smaller caching thresholds, allowing them to cache more data items. In contrast, the root node has a larger caching threshold, enabling it to cache data items for a longer duration. The system carefully considers both data lifetime and request rates to reduce energy consumption, data retrieval delays, and the overall network traffic load. When the request rate reaches the specified threshold, it is recommended to cache the requested data in the edge node. Figure 2 illustrates the architecture of the proposed model, showing the frequently requested content cached at the edge nodes, i.e., \(R6\) and \(R9\). When the cache capacity of an edge node reduces, and the validity of the content persists, it is stored in the cache of an intermediate node. This strategy prevents excessive network load by accommodating incoming requests at intermediate nodes, avoiding the need to route them back to the source node. The Pending Interest Table (PIT) is involved in tracking unhandled interest packets and recording relevant data names, along with their incoming and outgoing edges. The Forwarding Information Base (FIB) aids in network bridging and routing by determining the appropriate output network edge controller. Content replacement across the network follows an LRU cache policy, primarily targeting content flows. Retrieving content directly from the source node can lead to delays in terms of cache hit ratios for IoT networks. The proposed framework utilizes diverse data types to optimize the bandwidth utilization and alleviate network congestion.