## 7 Results and Performance Evaluation
### Performance Evaluation
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|} \hline \multirow{3}{*}{**Jammer Type**} & \multicolumn{4}{c|}{**True Detection Ratio (TDR \%) for 100 nodes configuration**} \\ \cline{2-10}  & \multicolumn{4}{c|}{**25 \(<\) JI \(<\) =50**} & \multicolumn{4}{c|}{**50 \(<\) JI \(<\) = 75**} & \multicolumn{4}{c|}{**75 \(<\) JI \(<\) = 100**} \\ \cline{2-10}  & **jnr** & **jnr** & **jnr** & **jnr** & **jnr** & **jnr** & **jnr** & **jnr** & **jnr** \\  & **25\%** & **50\%** & **100\%** & **25\%** & **50\%** & **100\%** & **25\%** & **50\%** & **100\%** \\ \hline DECH & 99.55 & 99.6 & 99.85 & 99.45 & 99.5 & 99.75 & 99.50 & 99.6 & 99.8 \\ COH & 99.5 & 99.6 & 99.8 & 99.40 & 99.55 & 99.75 & 99.45 & 99.6 & 99.8 \\ DECN & 99.45 & 99.5 & 99.6 & 99.35 & 99.4 & 99.5 & 99.35 & 99.5 & 99.6 \\ CON & 99.35 & 99.5 & 99.6 & 99.15 & 99.45 & 99.5 & 99.25 & 99.5 & 99.55 \\ REH & 99.25 & 99.4 & 99.55 & 99.10 & 99.2 & 99.25 & 99.20 & 99.3 & 99.4 \\ REN & 99.20 & 99.3 & 99.5 & 99.00 & 99.1 & 99.25 & 99 & 99.2 & 99.3 \\ \hline RADECH & 99.15 & 99.25 & 99.4 & 99.00 & 99.05 & 99.10 & 99.05 & 99.1 & 99.2 \\ RADECH & 99.05 & 99.20 & 99.25 & 98.90 & 99 & 99.10 & 98.95 & 99 & 99.1 \\ RACH & 98.90 & 99.10 & 99.15 & 98.80 & 98.90 & 99 & 98.90 & 98.95 & 99.1 \\ RACH & 99.00 & 99.05 & 99.10 & 98.60 & 98.70 & 98.8 & 98.85 & 98.9 & 99 \\ \hline \end{tabular}
\end{table} 
Table 8: It shows the values of TDR for 100 nodes configuration for different types of jammers, JIs and jnr. We now compare our performance evaluation parameters, TDR and FDR with their corresponding counter-parts in the model by [PERSON] _et al._, 'Detection Rate' and 'False Positive Rate' in Tables 10 and 11 respectively, as they have now been reduced to an almost matching denominator. However, the comparison is to be studied with caution as the model by [PERSON] _et al._ have preferred to use the Gilbert-Elliot model for simulating transmission losses based on two event discreet Markov chain, as they consider that the radio unit provides either good or bad transmission service. Figure 11: Graphic representation of TDR for different jnr for 100 nodes configuration for 75 \(<\) JI \(<\) = 100. Figure 10: Graphic representation of TDR for different jnr for 100 nodes configuration for 50 \(<\) JI \(<\) = 75. We thus find that our performance parameters indicate good results and are either better or matching with the existing methods of jamming detection in wireless sensor networks whose figures are publically available. We evaluated our approach with CBR traffic (a rather high traffic), and one may wonder whether the approach would also work for other traffic patterns, e.g., in a setting where the nodes usually do \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{**Type of jammer**} & \multicolumn{3}{c|}{**TDR% (proposed model) and ‘Detection Rate \%’( model by [PERSON] _et al._)**} \\ \hline \multirow{3}{*}{**Proposed model**} & \multirow{3}{*}{**[PERSON] _et al._**} & **jnr** & \multicolumn{2}{c|}{**jnr**} & \multicolumn{2}{c|}{**jnr**} \\  & & \multicolumn{2}{c|}{**25\%**} & \multicolumn{2}{c|}{**50\%**} & \multicolumn{2}{c|}{**100\%**} \\ \cline{3-8}  & & **Proposed** & **[PERSON]** & **Proposed** & **[PERSON]** & **Proposed** & **[PERSON]** \\ \hline \multirow{3}{*}{DECH} & \multirow{3}{*}{Deceptive (bad)} & 99.45 & 99.35 & 99.5 & 99.38 & 99.75 & 99.44 \\  & & 99.40 & 99.32 & 99.55 & 99.37 & 99.75 & 99.42 \\  & & 99.35 & 99.25 & 99.40 & 99.29 & 99.50 & 99.43 \\  & & 99.15 & 99.20 * & 99.45 & 99.28 & 99.50 & 99.34 \\  & & 99.10 & 99.15* & 99.20 & 99.18 & 99.25 & 99.32* \\  & & 99.00 & 99.05* & 99.10 & 99.10 & 99.25 & 99.25 \\ RADECH & Random (bad) & 99.00 & 99.06* & 99.05 & 99.06* & 99.10 & 99.16* \\ RADECH & - & 98.90 & - & 99.00 & - & 99.10 & - \\  & & 98.80 & - & 98.90 & - & 99.00 & - \\  & & 98.60 & 98.82* & 98.70 & 98.90* & 98.80 & 99.10* \\ \hline \end{tabular}
\end{table}