# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Network Overhead  
Figure 12 presents an overhead cost comparison between the OLSR and DCFM schemes. Overhead is measured based on the average TC message size. Our scheme creates a block after detecting an adversary, so it incurs overhead for distributing the block through the network via TC messages. Because adversaries can be detected within five seconds after an attack begins and the TC interval is five seconds in the OLSR scheme, a block will be disseminated within 10 s after an attack. In every second of the TC interval after a malicious node is identified, the overhead will be slightly greater than that of the DCFM scheme.  
However, consider a scenario in which six attacks are launched in a network. We launched three different attacks in the first part of the simulation (100 s). The three attackers were detected and three blocks were disseminated through the network. When those attackers attacked different victims during another 100 s period, the detection process was no longer necessary and the attackers were completely ignored by the nodes in the network. Even though six attacks occurred in the network, no additional detection overhead was incurred for the same attackers. Therefore, the overhead of our scheme is significantly reduced for repeated attacks from the same attackers. As shown in Figure 12, the overhead of the proposed scheme is lower than that of the default OLSR scheme because the number of nodes in the network is reduced. This is because previous attackers are ignored in the network. Therefore, it can be assumed that the overhead of the proposed scheme is the same as that of the conventional OLSR scheme.  
Additionally, we evaluated the computational complexity for an arbitrary node when it is detecting a malicious node (Table 4). In this scenario, there were six attacks in the simulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block** & **No** & **No** & **Yes** & **Yes** \\
**DCFM** & **Off** & **On** & **Off** & **On** \\ \hline With Attack & 21.7 & 66.45 & 66.51 & 66.51 \\ Without Attack & 66.51 & 66.51 & 66.51 & 66.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentages of message Received with DCFM On and Off Before and After Creating Block.  
Figure 12: Comparison of Average Overheads (average TC message size).  
When the victim node detected adversary A, according to the DCFM scheme in Section 2, rule 1 incurred computational complexity of O(n) during the hello interval and rule 2 incurred computational complexity of O(\(n^{2}\)) during the TC interval. The same process is used to detect new attackers in the proposed scheme. Because there were no blocks in the network, our scheme incurred the same complexity. Next, attacker B launched an attack and was detected by a node in both schemes. Subsequently, a different victim was attacked by [PERSON] again. Attacker B was re-detected by the new victim in DCFM, but not in the proposed scheme. Because there was a block containing attacker B in the network, every other node in the network was aware that [PERSON] was an adversary. Therefore, they did not need to perform the detection process, which reduced the computational complexity to O(1). Because the DCFM checks if a specific node is an adversary, the necessary computational complexity is O(n) in every hello interval. However, in our scheme, there is no additional complexity incurred for repeated attacks from the same attackers.