## 7 Conclusions and Future Work  
In this paper, an HF-based security architecture for IoMT-based health monitoring systems has been proposed and deployed. With the proposal of this novel architecture, we have tried to address the security issues in the field of IoMT-based health monitoring systems and have tried to fill the gap caused by the lack of lightweight blockchain-based security architectures for IoMT-based health monitoring systems. The detailed description of its main components (i.e., perception domain, HF blockchain network), along with the interactions among them, have also been provided. In addition, a deployment of the simulation set-up of the proposed HF-based security architecture (i.e., HF-based network) for IoMT-based health monitoring systems has been described and evaluated in terms of latency (i.e., min (s), max (s), and average (s)), send rate (i.e., transactions per second), throughput (i.e., transactions per second), and memory usage (MB). However, due to the fact that there are no similar architectures to the proposed one in the literature, we cannot compare our evaluation results directly to evaluation results from other works. Nevertheless, we compared our results to results from works on IoT networks for real-time healthcare applications [45; 46] and from a work on the performance evaluation of HF for use in IoT [38]. In this regard, our results demonstrate a good performance of the proposed architecture, allowing its use in resource-constrained environments, such as the IoMT, and enabling more advanced functionalities to be implemented on top of this architecture. It is worth noting that our experiments were conducted in a virtual environment, and potential variations in the performance of the proposed HF-based architecture in a real-world context may occur. Furthermore, the proposed architecture can be secure and effective against security attacks due to the functionalities provided by HF (i.e., TLS, MSP). As future work, we plan to develop and evaluate a Collaborative Intrusion Detection System (CIDS) for IoMT-based health monitoring systems, based on the developed HF-based security architecture, that will be responsible for collecting behavioral information (i.e., log files)  
Figure 19: Peer memory usage (MB), (**a**) per \"asset\" (**b**) per \"worker\".  
from the IoMT devices, information from the IoMT network traffic, and for detecting malicious incidents in such systems.  
Conceptualization, F.P.-O., J.C.R., G.M., G.S. and J.G.; methodology, F.P.-O., J.C.R., G.M., G.S. and J.G.; investigation, F.P.-O., J.C.R., G.M., G.S. and J.G.; resources, F.P.-O., J.C.R., G.M., G.S. and J.G.; writing--original draft preparation, F.P.-O., J.C.R., G.M. and G.S.; writing--review and editing, F.P.-O., J.C.R., G.M., G.S. and J.G.; visualization, F.P.-O., J.C.R. and G.M.; supervision, G.M., G.S. and J.G.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.  
This research work has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 876487. The JU receives support from the European Union's Horizon 2020 research and innovation program and Finland, Spain, Italy, Germany, Czech Republic, Belgium, and the Netherlands.  
Not applicable.  
Author [PERSON] would like to acknowledge the REACT project, which has received funding from European Union's Horizon Europe research and innovation program under the Marie Sklodowska-Curie grant agreement No 101069053 (REACT).  
The authors declare no conflict of interest.