Table 8 Detailed evaluation of existing and proposed approach.
From: A secure and efficient blockchain enabled federated Q-learning model for vehicular Ad-hoc networks
Authors | Techniques used | Performance metrics |
|---|---|---|
Kumar et al.21 | RF | Accuracy (1.16%), detection time (1.18 s) |
Fardad et al.22 | BEVEC | Latency (18%), energy consumption (65%) |
Gharehchopogh et al.23 | DDQN | Latency (6.87%) Energy consumption (26.4%) Computational cost (7.41%) |
Shukla et al.24 | DNN | Throughput (70%) Energy consumption (40.32%) |
Mohammed et al.25 | MORFLB | Delay (72%) |
Li et al.26 | BTWACS | Accuracy (80%), encryption time (78%) |
Zhang et al.27 | DRL | Latency (7.5%) Accuracy (73.2%) Energy consumption (42.51%) |
Proposed | EX-ECC, federated Q-learning model, IPFS, DPBFT | The number of vehicles 100, throughput (102465.8 KB/s), communication overhead (360.57 Mb), average latency (864.425 ms), communication time (19.51 s), encryption time (0.98 ms), decryption time (1.97 ms), consensus delay (50 ms) and validation delay (1.68 ms) |
