## 6 Evaluation
### Performance Study
#### 6.4.2 Hybrid Consensus Scalability Analysis  
In this section, we analyze the scalability of AuRa and BABE consensus along with the GRANDPA finalization algorithm. The scalability results for AuRA and BABE are represented in Figures 13 and 14, respectively. The results of AuRa are explained by the O(n\({}^{2}\)) message complexity which shows a decreasing throughput in proportion to the number of nodes. In addition, the GRANDPA finalization message complexity bottleneck of O(n\({}^{2}\)) further augments the decrease in throughput. Still, the difference in the GRANDPA algorithm is that finalization is performed on the chain of blocks rather than individual blocks of AuRa. Also, another inference based on the result is that higher input transaction per second (TPS) greater than the optimum 1000 increases the forks in the network as well, which in turn decreases the throughput as represented in Figure 15. This fork is explained by the processing and consensus-induced bottleneck affecting the liveness and consistency of chains but resolved by GRANDPA through additional computation.  
Figure 12: BABE consensus block period influence.  
BABE consensus has an algorithmic complexity of O(n) but suffers a decrease in scalability throughput due to the occurrence of forks in the system given the susceptibility of the consensus algorithm to elect multiple validators for a single block height. This results in a larger number of secondary blocks rather than primaries which are attributed to the nature of the algorithm. Accompanied by the computational effort of verifiable random function, it affects the throughput of BABE, which is absent in the AuRa algorithm. The fork occurrence increases with higher input transactions per second, as represented in Figure 16 augmented by increased transaction processing, message communication overhead, and consistency issues.  
Figure 14: BABE and GRANDPA consensus scalability performance.  
Figure 13: AuRa and GRANDPA consensus scalability performance.  
In summary, for the implementation evaluation of data monetization architecture, we realize that the BFT algorithms of AuRa and BABE have a scalability issue. On a positive note, due to their hybrid nature with GRANDPA, they offer finalization and better protocol security but at the cost of additional computation. Both protocols are affected by scalability issues, where the AuRa consensus is more stable than the BABE protocol. BABE, along with the GRANDPA protocol, suffers from consistency issues [31; 32] as explained earlier. It offers a higher degree of security in the form of an impossibility of predicting the next successive block proposer using a verifiable random function. GRANDPA BFT offers the finalization of chains in the presence of forks, a hybrid protocol to work with AuRa and BABE in the substrate framework. It is destined to resolve the forks, but its liveness can be affected if most validators are affected by chain consistency issues. The results can be slightly higher if a more straightforward transaction payload is considered. Still, the  
Figure 16: BABE and GRANDPA consensus fork study.  
Figure 15: AuRa and GRANDPA consensus fork study.  
throughput of around 300 transactions per second in this data monetization implementation signifies that this architecture is acceptable and can be extended to enterprise mobility use cases.