# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 6 Conclusions and Future Work  
Tow-ACKs Trust (TAT) Routing protocol is proposed to detect and isolate the malicious sensors to create a free-malicious- end-to-end route, increasing the network's throughput and decreasing the network's energy consumption. This paper suggests a new technique based on the cross-layer concept, TAT, which uses first-hand and second-hand information and the acknowledgments from data link and TCP layers to compute and update the trust values. The TAT performance was evaluated using different numbers of malicious sensors in the network and different network densities. In addition, we compared TAT protocol with existing protocols, namely PeerTrust, and BTRM. TAT increases the network expected lifetime and increases the malicious detection ratio packet delivery ratio, and decreases the energy consumption. The experimental results of evaluation TAT with different network sizes show that the proposed model is still efficient when the network size increases, which means TAT is scalable. Moreover, the results show that the proposed model showed better performance compared to PeerTrust, and BTRM models, in terms of Malicious Detection Ratio (MDR), Packet Delivery Ratio (PDR), and Average Energy Consumption (AEC), even with a high percent of malicious sensors. An analytical model for the proposed technique will be presented to extend the current work. Moreover, the proposed model will be examined against dynamic WSNs. Furthermore, the earlier Multi-Threshold Energy (MTE) technique [38] for the TAT method to minimize energy consumption can be applied for further performance enhancement. Moreover, End-to-End delay between trusted destinations could be used as a metric to evaluate the proposed technique in terms of time. Moreover, utilizing Artificial Intelligence (AI) techniques could be one of the valuable extensions to the work done in this paper, including artificial neural networks and particle swarm optimization with center of gravity (PSOCoG) [39] selecting the best settings values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Malicious\%**} & \multicolumn{3}{c}{**AEC**} \\ \cline{2-4}  & **Peertrust** & **BTRM** & **TAT** \\ \hline
5 & 2.6 \(\times\) 10\({}^{6}\) & 2.2 \(\times\) 10\({}^{6}\) & 1.4 \(\times\) 10\({}^{6}\) \\
10 & 3.7 \(\times\) 10\({}^{6}\) & 2.9 \(\times\) 10\({}^{6}\) & 1.7 \(\times\) 10\({}^{6}\) \\
15 & 4.8 \(\times\) 10\({}^{6}\) & 3.4 \(\times\) 10\({}^{6}\) & 1.9 \(\times\) 10\({}^{6}\) \\
20 & 6.8 \(\times\) 10\({}^{6}\) & 5.9 \(\times\) 10\({}^{6}\) & 3.3 \(\times\) 10\({}^{6}\) \\
25 & 10.5 \(\times\) 10\({}^{6}\) & 8.8 \(\times\) 10\({}^{6}\) & 7.3 \(\times\) 10\({}^{6}\) \\
30 & 19.8 \(\times\) 10\({}^{6}\) & 15.1 \(\times\) 10\({}^{6}\) & 12.3 \(\times\) 10\({}^{6}\) \\
35 & 43.9 \(\times\) 10\({}^{6}\) & 39.8 \(\times\) 10\({}^{6}\) & 30 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total AEC (u) in the network for TAT, PeerTrust, and BTRM Models.  
**Author Contributions:** [PERSON] and [PERSON] carried out conceptualization, methodology, software, and validation; [PERSON] and [PERSON] were responsible for formal analysis, data curation, writing, and the original draft preparation; [PERSON], [PERSON] and [PERSON] performed analysis, reviewing and editing. [PERSON] and [PERSON] performed reviewing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been funded by the Scientific Research Deanship at the University of Ha'il--Saudi Arabia through project number BA-2101.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.