# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 2 Related Work  
IoMT comprises intelligent devices, including wearables and medical monitors that are utilized in healthcare monitoring spanning personal use, homes, communities, clinics, and hospitals. These devices enable real-time location tracking, telehealth services, and various other functionalities. IoMT facilitates secure wireless communication among remote devices via the Internet, enabling swift and adaptable medical data analysis. Its impact on the healthcare industry is multifaceted, with significant benefits observed when deploying IoMT in various contexts, whether in a home setting, on an individual's body, within communities, or even within hospital facilities to obtain the most current data [14; 15]. IoMT consumers consistently seek the latest published data due to its frequent updates, where IoT data are cached away from the network edge [16]. IoT devices are small and the data collected from billions of devices is so large that it can disrupt regular network traffic. Therefore, ICN can be used to enhance the network scalability of IoMT [17]. The integration of ICN with IoT is more suitable as it results in reliable data transmission and consumes less bandwidth as compared to IP-based network communication [18]. ICN plays a vital role in ensuring content availability over the network and facilitating rapid content access [2]. Several techniques have been proposed to address cache scheduling including the implementation of caching mechanisms with different objectives, such as data size, data lifetime, sensing cache, time-based cache, and collaborative caching. Some models utilize cloud computing to store medical data in cloud storage, where additional operations like scheduling and resource utilization prediction are performed [19; 20; 21; 22]. Tag-based Caching Strategy (TCS) uses tag filters for the matching and lookup of the requested contents for dissemination to the target node [23]. In TCS, all network nodes are linked with specific lists of tags to identify highly requested content. Tag filters are generated from the tag list through a hash function to enhance content distribution. As the network node receives a request from a user, the corresponding tag filter decides whether to transmit the content cache to the intermediate location or not. In this caching strategy, the tags linked with the required contents are checked by the tag filters inside the CS, and all the tags are hashed and mapped in a counter to find the most requested content. If a tag counter for specific content crosses the threshold, then the content is considered popular. As a result, all the nodes check the tags to identify the content location and decide whether the content that needs to be cached reaches the preferred location or not. TCS appears to be a promising approach for improving content distribution efficiency in networks. However, its complexity and resource requirements, as well as the need for careful parameter tuning, are potential drawbacks that need to be carefully considered. Another article [24] presents the Client Cache (CC) strategy in which the cached contents inside the network nodes are observed and considered valid. The on-path caching approach is selected to cache transmitted contents which eliminates the requirements to inform the client which node is appropriate to cache the preferred content. Moreover, the number of nodes is reduced to be aware of the most essential nodes in terms of content quality and cache size. The main objective of CC is to extend the content validity, which is examined as the requested content is found inside the cache. The content material is considered valid if the lifetime within the publisher is higher than the lifetime of the version within the cache. While selecting the requested content, a validity test is performed to check the content inside the content publisher whenever a content material request arrives at the neighborhood-caching node.