Table 1 Comparative Analysis of Blockchain-Based Security Frameworks.
From: Resilient security architecture for smart buildings using DLT powered encryption
Author & Year | Proposed Study | Methodology/Parameters | Key Results | Strengths | Weaknesses |
|---|---|---|---|---|---|
Padma and Ramaiah (2024)9 | SecPrivPreserve framework for blockchain platform security across different frameworks | \(\bullet\) Password-based authentication \(\bullet\) Advanced encryption algorithms \(\bullet\) Cryptographic hashing | \(\bullet\) Response time: 34s \(\bullet\) Computational overhead: 94s \(\bullet\) Encryption time: 0\(\bullet\)87s | \(\bullet\) Multi-layered security approach \(\bullet\) Framework agnostic design \(\bullet\) Reasonable encryption performance | \(\bullet\) High computational overhead (94s) \(\bullet\) Slow response time \(\bullet\) Limited scalability metrics |
Mishra and Chaurasiya (2024)12 | Blockchain-based distributed network for smart cities security | \(\bullet\) Hybrid deep learning algorithm \(\bullet\) Long Short Term Memory (LSTM) \(\bullet\) Support Vector Machines (SVM) | \(\bullet\) Accuracy: 97% \(\bullet\) Specificity: 98% | \(\bullet\) Excellent accuracy metrics \(\bullet\) Hybrid ML approach \(\bullet\) Smart city focus | \(\bullet\) No throughput/latency metrics \(\bullet\) Limited energy analysis \(\bullet\) Scalability concerns |
Sisi and Souri (2024)20 | Energy-aware mobile crowd sensing with blockchain technology analysis | \(\bullet\) Applied optimization algorithms \(\bullet\) Multi-factor evaluation framework \(\bullet\) Diverse environment testing | \(\bullet\) Private blockchain: 35% \(\bullet\) Public blockchain: 33% \(\bullet\) Consortium: 24% \(\bullet\) Hybrid: 8% | \(\bullet\) Comprehensive blockchain analysis \(\bullet\) Energy-aware design \(\bullet\) Mobile integration | \(\bullet\) Distribution rather than performance \(\bullet\) Limited quantitative metrics \(\bullet\) No baseline comparison |
Padma and Ramaiah (2024)19 | Trust-based consensus mechanism for reducing network overhead and energy consumption | \(\bullet\) Grey Wolf Optimization (GWO) \(\bullet\) Proof of Work (PoW) enhancement \(\bullet\) Proof of Stake (PoS) integration | \(\bullet\) Throughput increase: 12.5% \(\bullet\) Mining delay reduction: 19.5% \(\bullet\) Energy usage reduction: 18.3% | \(\bullet\) Optimization-based consensus \(\bullet\) Significant energy savings \(\bullet\) Improved mining efficiency | \(\bullet\) Moderate improvements \(\bullet\) Complex hybrid mechanism \(\bullet\) Limited real-world validation |
