## 4 Simulation Results  
To evaluate the effectiveness of our scheme, we performed simulations with the GloMoSim (Global Mobile Information Systems Simulation Library) simulator [28] under various parameters. We configured a grid sensor network with 100 nodes, and the average number of one-hop neighbors per node is eight. A sink node is located at a corner of the network and the normal traffic from the sensor nodes is destined for the sink node. As comparative routing mechanisms, static routing with the shortest path, AODV routing [11] and JAM [8] with a detour route are all simulated. As performance metrics, the PDR (Packet Delivery Ratio) and average end-to-end delay of normal traffic are measured. In addition, we figure the ratio of the enhanced PDR and delay in comparison with the JAM approach. For the simulation parameters, we vary the number of normal flows, the number of attack flows, the interval of an attack flow and the number of forward nodes. All jamming traffic is located at the center of network, thus generated normal traffic would be destined for the jamming area in normal routing. All normal traffic is generated by CBR (Constant Bit Rate) traffic at 500 millisecond intervals, and general jamming traffic is generated by CBR traffic at 5 millisecond intervals. In our DMP, the number of multiple paths, \(a\), is usually 2. The label \"Normal\" means that the network has no jamming traffic.  
In the case of increasing normal traffic, as shown in Figure 5, a dynamic routing protocol, AODV, does not provide fast route recovery against jamming, thus the PDR is very low. The reason that AODV has a low delay is that most normal traffic is blocked in the jamming area and only a little traffic is fast transmitted. JAM provides a high PDR to a degree, but the PDR decreases in the case with a lot of normal traffic and this induces a high delay. On the contrary, DMP provides a high PDR and a low delay that is almost the same as Normal. Through this simulation, the results definitely show that a jamming evasion with a detour route is not sufficient, especially with a lot of normal traffic.  
Second, we increase the amount of attack traffic as shown in Figure 6, that is, the size of the jamming area. Two, four, and six attack traffic flows produce a jamming effect on 10, 15 and 22 nodes, respectively. In this case also, both AODV and JAM flounder. DMP somewhat worsens the PDR and delay in the big jamming area, because of the long detour routes, but it provides the best performances.  
Third, we vary the interval of the attack traffic with nine normal flows, as shown in Figure 7. Jamming with 1 millisecond is the severest attack. When the strength of jamming is reduced, static routing with the shortest path increases the PDR, but it induces a high delay because the routes pass through the jamming area. JAM enhances the PDR in weak jamming, but it still has a high delay. DMP enhances the PDR more than the normal situation, due to distributed routing with multiple paths.  
Figure 5: Performance comparison of (a) PDR and (b) delay according to number of normal flows.  
Figure 6: Performance comparison of (a) PDR and (b) delay according to number of attack flows.  
Lastly, we change the number of forward nodes at a detour node in the big jamming area, which has six attack flows. Figure 8 shows that the more forward nodes are determined, the better the performance of DMP in case that jamming is severe.  
Figure 8: Performance comparison of (a) PDR and (b) delay according to number of forward nodes.  
Figure 9 depicts the performance improvement of DMP in comparison with JAM. In all cases, DMP enhances the performance more than JAM and the performance improvement in the PDR is largest in the case with many forward nodes. In particular, the performance improvement in the delay is large, for example it is about 30 times better.  
Figure 7: Performance comparison of (a) PDR and (b) delay according to interval of an attack flow.