# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 7 Conclusions  
In this work, we have provided a survey of the literature on dummy packets source anonymity techniques. Such techniques are very effective against global adversaries in WSNs. They out-perform other methods such as separate path routing, network location anonymization, and network coding. Further, the source anonymity problem has been discussed, and we present a detailed explanation of the most popular dummy techniques including their assumptions for the global adversary  
Figure 10: Time correlation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Technique** & **Time Correlation** & **Visualization** & **Machine Learning** & **Statistical Analysis** \\ \hline \(TESP^{2}\)[48] & used & not used & not used & not used \\ _CSPSLP_[49] & not used & not used & not used & not used \\ _DWUS_[50] & not used & not used & not used & not used \\ _GAFG_[51] & used & not used & not used & used \\ _SoSi_[52] & not used & not used & not used & not used \\ _SECLOUD_[53] & used & used & not used & not used \\ _UHIT_[54] & used & not used & not used & not used \\ _PA-SLP_[30] & used & not used & not used & used \\ _FACP_[34] & used & not used & not used & used \\ _DUID_[55,56] & used & used & used (Neural Network) & used \\ _DAD_[55,56] & used & used & used (Neural Network) & used \\ _CAD_[55,56] & used & used & used (Neural Network) & used \\ _EDAD_[57] & used & used & used (Neural Network) & used \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 9: Solid circles indicate nodes with rate of 0.2 per second. Dashed circles indicate nodes with rate of 0.4 per second.  
models. In addition, a comparison of all techniques has been provided to show the difference in terms of intervals, timers, random numbers, probability, clusters/grids/cells, proxies/filters, uniform/constant distribution, exponential distribution and Poisson distribution. Other surveys have not addressed such taxonomy in details. Another comparison for the global adversary models is also conducted. All models are compared to each other in terms of topology information, sink location, GPS/localization, interval distribution, rate monitoring, time correlation, visualization, machine learning, and statistical analysis. Some of the presented techniques are promising and have the potential for improvement. The main aim of the paper is to help those who are interested in the wireless sensor networks area choose a suitable algorithm that satisfies their applications' demands by providing all the characteristics and assumptions of each technique in details. This will help them to make the right choice to get the maximum possible performance out of their applications. We did not cover all techniques that use dummy packets but we chose those which are mature, noble and advanced in performance. In the future, the dummy packet injections techniques can be expanded and improved by merging them with other methods such as network location anonymization to reduce the overhead of the dummy traffic, which is the main concern of the current techniques. The Internet of things (IoT) and WSN are both Machine-to-Machine (M2M) networks. IoT is a technology integrator for many already existing technologies such as embedded systems, sensing, data mining, cloud computing, etc. Furthermore, we can consider WSN as a part of the family of IoT depending on the scale of the application. Some of these algorithms/techniques/protocols could be used for IoT and would depend very much on the complexity of the IoT system on hand.  
Conceptualization, A.B. and A.A.; Methodology, A.B. and A.A.; Formal Analysis, A.B. and A.A.; Writing--Original Draft Preparation, A.B.; Writing--Review and Editing, A.A., and A.B.; Supervision, A.A. and A.M.; and Project Administration, A.A and A.M.  
This research received no external funding.  
The authors declare no conflicts of interest.