## 5 Conclusions  
This study addresses the security issues in the RPL protocol for IoT and WSN devices, focusing on wormhole attacks, which are serious threats to the network. The research proposes a separate controller for the trust calculation to minimize energy consumption and packet loss and to increase the network lifetime. The model is evaluated using different parameters such as network lifetime, packet loss ratio, energy consumption, average residual energy, and detection time. The proposed method uses a Subjective Logic Framework-based control, resulting in a decrease in energy consumption, a 10% decrease in packet loss ratio, and an increase in attack detection time in comparison with the PCC-RPL. The study concludes that this model can effectively detect and mitigate wormhole attacks on RPL-based IoT and WSN devices, making it a promising approach for future research. The result of the study will enable researchers to continue working out the betterment of security for resource-constrained devices of the IoT and WSNs using RPLs. This model can be extended to tackle other attacks on the RPL protocol.  
The study successfully surmounted numerous challenges by adopting a distinct topology in contrast to existing methodologies. Notably, the SLF-RPL introduces an additional layer of security beyond the node level for trust calculations, yielding enhanced resource utilization. While it demonstrated effectiveness in simulations, translating this success to real-time applications may pose deployment challenges.  
The study exhibits certain limitations. Firstly, although the proposed SLF-RPL framework demonstrated efficacy in mitigating wormhole attacks, its adaptability for addressing various other low-power device vulnerabilities within the IoT landscape remains unexplored. This presents an avenue for future research to extend its applicability to a broader spectrum of security challenges.  
Secondly, the study's scope is confined to the RPL protocol in the IoT domain. Nevertheless, there exists potential for enhancing its performance by extending its applicability to diverse IoT protocols through the integration of advanced artificial intelligence and deep-learning techniques. Techniques such as machine and deep learning models will be used to detect and isolate internal attacks as suggested by authors in [28] ant colony optimization, Markov chain distribution, and the deep neural networks described in can be tested within the framework of the SLF-RPL to further optimize attack detection rates, thereby expanding its utility and impact in safeguarding IoT ecosystems.  
Conceptualization, S.J., A.S. and T.K.; methodology, S.J., A.S. and T.K.; software, I.U.K., A.S. and S.J.; validation, S.J. and A.S.; formal analysis, T.K.; investigation, I.U.K.; resources, writing--original draft preparation, I.U.K. and C.D.; writing--review and editing, F.C., C.D. and H.J.C.; visualization, H.J.C. and F.C.; supervision, A.S. and I.U.K.; project administration, C.D. and F.C.; funding acquisition, F.C. and H.J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are currently not publicly available due to participant privacy, but they are available from the corresponding author upon reasonable request.  
All the authors would like to give thanks for the support and help from Satya Wacana Christian University and to others who took part in this work.  
The authors declare no conflict of interest.