HSTBWNQ: Design of an Efficient & Highly Secure Trust-based Blockchain Powered Wireless Network Model with QoS Awareness
Keywords:
Wireless, Security, Blockchain, Sidechain, EHO, QoS, DDoS, Finney, SybilAbstract
Blockchains are dominating the field of distributed network security due to their immutability, trust ability, traceability, and non-centralized computing capabilities. But QoS of blockchain networks is highly volatile, and varies exponentially w.r.t. length of the blockchain under deployment. Moreover, blockchains reserve a set of miner nodes which are responsible for verification of new blocks before they are added to the blockchain. An attack on these nodes, compromises security of the whole network, which reduces its deployment capabilities for large scale scenarios. To overcome these limitations, this text proposes design of a novel highly secure trust-based blockchain powered Wireless network with QoS awareness. The proposed network model initially deploys a trust-based framework for identification of miner nodes. These nodes are selected based on their temporal security & QoS performance, which assists in high-speed and high-reliability miner identification. The selected nodes are further segregated based on their location, which assists in incorporating stochastic miner selection. Decisions of these miners assist in validating blockchain’s authenticity, which increases its resilience against a wide variety of internal & external attacks. The model also deploys an Elephant Herding Optimization (EHO) based sidechaining process, which assists in intelligent merging & splitting of the main blockchain into multiple parts. To form these parts, the EHO model utilizes chain length, delay needed for mining blocks, and energy consumed while mining blocks. Each of these parts are mined via location-aware miners, which assists in achieving high-fidelity and low-complexity mining for large-scale deployments. It was observed that the proposed model performed consistently under distributed denial of service (DDoS), Finney, and Sybil attacks, which makes it useful for real-time network deployments. The network model was tested under different attack scenarios, & different node configurations, and compared with various state-of-the-art models. It was observed that the proposed model showcased 15.3% lower computational delay, 9.4% lower energy consumption, 3.2% better packet delivery ratio, and 5.9% better throughput when compared with these models. This performance was consistent under multiple attack scenarios, which assists in deploying the model for large-scale internet of things (Wireless) application scenarios.
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