## 5 Conclusions  
Based on the definition of DoS, this study comprehensively summarizes the DoS attacks in WSNs that can be addressed through trust models, including well-known DoS attacks as well as the new types of DoS attacks that have emerged with the development of WSNs. Furthermore, this study identifies the trust evidence needed to develop effective trust models for the detection of DoS attacks in WSNs and for the mitigation of their impact, and it compares trust evidence extraction methods and the trust evaluation methods. The challenges identified in this study can be used as an evaluation framework for trust models that aim to protect WSNs against DoS attacks. As trust models consider node behavior manifestations rather than the cause of such behavior, the findings of this study are applicable as well to models that aim to maintain WSN functionality and reliability even when a node is faulty or has been compromised in an internal attack. Further research needs to consider the three significant challenges that emerged: determining the threshold limits in trust evaluation, allocating trust metric weightings, and managing trust information loss. Design considerations to guide the development of future trust models include the impact of link quality on trust evaluation, the impact of authentication delay on trust evaluation, and the need to integrate trust models with routing protocols to actively defend against attacks rather than only focusing on detection. Another direction for further research is the development of trust models for countering DoS attacks specific to the SDWSN domain. It is essential to ensure a high security standard when deploying an SDWSN architecture on a large scale and for geographically dispersed applications of WSNs. This study has several limitations. First, our literature review is limited to studies published in English. Second, our review focuses solely on the research of trust models for dealing with DoS attacks in WSNs. It does not cover studies of trust models related to other security issues, such as privacy protection and access control, or other concerns, such as data aggregation and filtering. Third, to streamline our study, we excluded research involving AI and blockchains. These limitations can be overcome in further studies. Conceptualization, L.W., K.P. and M.L.Y. ; methodology, L.W., K.P. and M.L.Y. ; validation, K.P. and M.L.Y. ; formal analysis, L.W. ; investigation, L.W. ; resources, M.L.Y. ; data curation, L.W. ; writing--original draft preparation, L.W., K.P. and M.L.Y. ; writing--review and editing, L.W. and K.P. ; visualization, L.W. ; supervision, K.P. and M.L.Y. ; project administration, K.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflicts of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**No** & **Author, Year** & **Trust Evidence** & **Evaluation Methods** & **Trust Metric Range** & **Trust Metric** & **Trust Metric** & **DoS Attack** & **Simulation Types** & **Tool** & **Domain** \\ \hline
5 & [PERSON] and [PERSON], Züll8 [39] & Probability of packet reached & ✓ & ✓ & Bayesian beta method, forgetting curve. & A realm number & ✓ & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & \\ \hline
6 & [PERSON] et al., 2018 [13] & Energy consumption, power consumption & & & Bayesian beta method, penalty factor, threshold, Pearson correlation & An unsigned integer & ✓ & ✓ & Hybrid DoS Attacks.