# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 2 Related Work
They show that the combination of the two proposed schemes results in a notable improvement in content validity at the expense of a certain degradation in both server-hit and hop reduction ratios. However, it requires a more extensive and in-depth analysis of the trade-offs and challenges associated with the proposed solutions. Authors in [25] presented a secure and energy-efficient framework using IoMT for E-healthcare (SEF-IoMT) and explored the growing popularity of IoT in the healthcare sector. The proposed framework explored the need for an improved framework to address issues related to energy consumption, communication costs, and data security. The proposed framework reduces energy consumption and communication overhead in IoMT. Comparative experimental results show the effectiveness of the proposed method in comparison to existing methods. However, it lacks a transparent evaluation of metrics and an in-depth analysis of its findings to validate its scientific contribution. Another article [26] presents a secure and energy-efficient IoT model for e-health, focusing on the secure transmission and retrieval of biomedical images over IoT networks. The authors utilize compressive sensing and a five-dimensional hyper-chaotic map (FDHC) for image encryption, addressing the challenge of hyper-parameter tuning in FDHC. The encryption technique is image-sensitive, depending on the initial scrambled row and column for permutation and diffusion operations. Experimental results indicate that the proposed method outperforms existing image encryption techniques. Consequently, it is considered suitable for securing communication in energy-efficient IoT networks. However, a clear discussion of the hyper-parameter tuning problem, sensitivity to input images, and the practicality of the proposed method is missing. The study presented in [27] addresses a crucial issue in the field of healthcare IoT by emphasizing the need for data security and energy efficiency. This study addresses the critical challenges of storing sensitive medical data securely and preventing cyber threats in the rapidly growing IoMT network. The integration of homomorphic secret sharing and artificial intelligence to enhance the maintainability of disease diagnosis systems and reinforce secure communication is highly important. However, the analysis of the results shows the proposed model suffers from an increased packet drop ratio and imbalanced energy consumption in the presence of high network load among IoT nodes. Age-based cooperative caching is presented in [28] for the efficient utilization of cache and routing in ICN. This framework is designed to minimize heavy computation and communication between routers. The algorithm dynamically updates the freshness of data in routers by pushing popular data over the network edges, while less popular data reside away from the edges. The method can be considered efficient for ICN, but these schemes work with special-purpose applications, i.e., P2P systems that mostly benefit web applications like web caching and distributed file systems. However, the evolving nature of ICN and how this approach might adapt to changing network conditions and requirements has not been considered for the evaluation and validation of the proposed model. In another article, the authors [29] present a model that aims to balance between multi-hop communication costs and the freshness of transient data items. IoT devices create extra traffic load, and it is necessary to make caching decisions to minimize the traffic. The short lifetime of IoT data items leads to extra complexity in handling caching decisions. The cost function is used for data items generated at the source node to address the issue. The results analysis shows that this model effectively describes the effect of data transiency and accurately represents the benefits of a caching system, particularly in terms of reducing network load, and especially for substantially requested data items.