# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 2 Related Work
A dynamic threshold adaptation algorithm allows the sensing cache to adjust its parameters in real time, maximizing the combined hit rate of the sensing service from multiple sensors. The sensor devices harvest energy from the environment to save energy for sending or receiving information. However, sensing errors may occur that can create problems with data freshness. The smart caching scheme for the IoT platform presented in [35] creates and maintains the balance between the freshness of data and energy of nodes. Freshness and energy consumption are the main parameters considered. When IoT data are combined over the Internet, issues like freshness loss, energy consumption, and increase in traffic load are raised. The freshness of data items and energy are considered parameters of the cache to provide fresh data to users while the energy of the sensors remains balanced. Data redundancy is also minimized in the smart cache scheme by dropping the data item from the cache nodes that are no longer needed. This leads to data freshness and less energy consumption. However, there are no details provided about the experiment, the methodology, or the results and it lacks critical analysis and empirical evidence. Freshness Aware Reverse Proxy (FARP) caching scheme is presented in [36]. The scheme is based on data item freshness and access performance. The caching scheme brings the advantage of access performance but when the data in the cache is not fresh and expires at the source node, the method creates additional burden. The cost function is used to balance the switching between used and unused data items present at the cache nodes. The method also adjusts the two parameters for the cache node to provide fresh data to the user. However, when the data expires, it may overlap with the latest published data at the source node. A probabilistic caching scheme for IoT networks is presented in [37]. This model considers the freshness of IoT data, energy, and storage as measurement parameters to optimize the retrieval of data. ICN content is fetched through unique name and location tags to ensure the freshness of data, data retrieval, ease of transmission cost, and sharing of data. In-network caching also helps to optimize data retrieval and manage network load. The proposed caching scheme also considers resource constraints, i.e., energy and space to provide fresh data to users. However, it does not provide a direct comparison with the state-of-the-art models. A coherent caching freshness scheme for IoT is proposed in [38]. The method increases the speed of data retrieval while minimizing the network load. The aim is to increase the cache hit rate and reduce network delays. The authors worked on on-path caching scheme to improve the energy of caching nodes. In this caching technique, the intermediate nodes not only resolve the problem of energy but also resolve the problems of data freshness and redundancy. The method uses the cost function for the energy-efficient utilization of caching nodes. However, several critical aspects have not been considered in this study including scalability, security, and interoperability. All the cache frameworks discussed above have their advantages. Millions of IoT devices are connected to the Internet which causes huge traffic load and poses several challenges. The need for the efficient utilization of available resources to benefit from the IoMT network is still challenging. The performance and lifetime of the sensors in IoMT are concerned with energy consumption, computation, and other parameters. The solutions designed for this purpose should consider the relation between related parameters to ensure that improving one parameter does not affect other parameters. Therefore, more research and robust cache frameworks are needed to achieve the maximum advantages of IoMT over ICN networks.