BQBCC: Design of an Augmented Bioinspired Model for Improving QoS of Blockchain IoT Deployments via Context-based Consensus
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Abstract
Blockchain-deployments are highly secure, but lack in terms of scalability due to exponential increase in mining delay w.r.t. chain lengths. To overcome these issues, researchers have proposes used for low-complexity mining, sharing techniques, and other machine learning optimizations. But these models either depend on underlying blockchain, or showcase larger computational delays, which limits their scalability levels. Moreover, most of these models do not consider consensus optimizations, which further limits their deployment capabilities for large-scale networks. To overcome these issues, this text proposes design of an efficient bioinspired model for improving QoS of blockchain IoT (Internet of Things) deployments via context-based consensus. The proposed model initially collects temporal mining performance from existing miner nodes, and deploys a novel Proof-of-Temporal Trust (PoTT) based consensus for validating responses of these miners. The PoTT Model uses temporal mining delay, energy consumed while mining, and throughput levels for selection of high-performance miners for processing block-addition requests. Requests approved by these miners are stored on a set of Bacterial Foraging Optimized (BFO) sidechains. These sidechains are automatically tuned based on spatial QoS performance of the network under real-time conditions. The BFO Model assists in segregating existing single-length blockchains into QoS-optimized sidechains. To perform this segregation, the BFO Model uses an exhaustive consistency metric that combines QoS & security levels that can be applied to specialized applications like Industrial IoTs. Thus, segregation into sidechains is done while maintaining high security under heterogenous attacks. Due to these optimizations, the model was able to reduce mining delay by 3.9%, reduce energy needed for mining by 2.5%, improve throughput by 4.5%, while maintaining high attack-detection efficiency under Sybil, Distributed Denial of Service (DDoS), and Masquerading attacks.
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