A Novel Enhanced Lightweight Node for Blockchain

Zhao, Yue; Niu, Baoning; Fan, Xing

doi:10.1007/978-981-15-2777-7_12

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…Currently, a number of solutions have been put forth by some scholars. Zhao et al [27] developed a lightweight enhanced SPV (simplifed payment verifcation) node named ESPV. ESPV nodes store all new blocks and reserve some old blocks according to their storage capability.…”

Section: Blockchain Storage Optimizationmentioning

confidence: 99%

“…To some extent, the previous work can relieve the storage pressure of nodes, but the following issues still exist: (1) lightweight clients allow nodes to store only a small amount of data, but these lightweight clients cannot participate in the consensus process [27][28][29][30], (2) unloading and compressing historical blocks lacks protection for the integrity of ledger data [31][32][33][34][35], and (3) the sharding technology is used to deal with the storage problem, but the problem of cross-sharding transaction is generated [36][37][38][39]. Under the premise of preserving data integrity, our storage optimization strategy enables those resource-constrained nodes to participate in the consensus by storing only a small amount of data.…”

Section: Blockchain Storage Optimizationmentioning

confidence: 99%

“…Terefore, in each consensus iteration, the ID is changing and unpredictable. In addition, in order to prevent malicious nodes from forging multiple ID Require: params: BH; R Val ; Target; t 1 , t 2 : time interval Ensure: consensus (1) procedure (2) P←Hash(R Val ‖ID i ) (3) if (P < Target) then (4) generate Proof(P, ID i , PK i , Sign(R Val )) (5) broadcast Proof (6) waiting for t 1 , and: (7) receive Proof (incrementing N cand ) (8) if (N cand < W/2‖N cand > W) then (9) update (Target) (10) broadcast "consensus failure, new Target" (11) go to (line 2) ( 12) else (13) select the luck Proof: the P of Proof is minimum (14) if (my Proof is the luck) then (15) generate a new block (16) update block height: BH←BH + 1 (17) update R Val ← Hash(BH‖H pre ‖N cand ) (18) update Target←currentTarget/log 3W/4 N cand (19) broadcast new (R Val , Target, Block) (20) go to (line 2) ( 21) else (22) waiting for t 2 , and: (23) if (the new Block has arrived) then (24) verify it (25) if (the Block passes validation) then (26) update Blockchain (27) update (R Val , Target) (28) broadcast new (R Val , Target, Block) (29) go to (line 2) (30) end if (31) else (32) update (Target) (33) broadcast "consensus failure, new Target" (34) go to (line 2) (35) end if (36) end if (37) end if (38) else (39) wait for the new (R Val , Target, Block) or (consensus failure, new Target), go to (line 2) (40) end if (41) end procedure ALGORITHM 1: Proof of random.…”

Section: Proof Of Randommentioning

confidence: 99%

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HLOChain: A Hierarchical Blockchain Framework with Lightweight Consensus and Optimized Storage for IoT

Xie

Dong

Xia

2023

Security and Communication Networks

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Blockchain can effectively deal with the security and trust issues in Internet of Things (IoT) due to its salient features including decentralization, immutability, traceability, openness, and transparency. However, most IoT devices have too limited computing, storage, and bandwidth resources to maintain the complete operation of a blockchain system. To this end, we propose a hierarchical blockchain framework called HLOChain for IoT scenarios. First, according to computing and storage capabilities, the IoT devices are classified into three levels, i.e., high, medium, and low. They are deployed on different layers. In this way, a hierarchical blockchain architecture is designed. Second, we propose a lightweight proof of random (PoR) consensus mechanism to provide low-energy block mining, so that even the medium nodes can participate in the consensus task. Third, in order to reduce the ledger storage overhead, we design a blockchain storage optimization strategy based on the account model. Finally, the security analysis demonstrates that our HLOChain is secure against double-spend attack, Sybil attack, and so on. The experimental evaluation shows that our HLOChain achieves better performance in ledger storage cost, consensus computing cost, throughput, and transaction confirmation latency.

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Section: Blockchain Storage Optimizationmentioning

confidence: 99%

Section: Blockchain Storage Optimizationmentioning

confidence: 99%

Section: Proof Of Randommentioning

confidence: 99%

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HLOChain: A Hierarchical Blockchain Framework with Lightweight Consensus and Optimized Storage for IoT

Xie

Dong

Xia

2023

Security and Communication Networks

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“…Notably, our approach diverges from others [27,28] as we do not aim to alter the consensus algorithm. Instead, like [33][34][35][36][37] schemes, our emphasis lies in optimizing data replication, considering dynamic factors such as cross-shared communication overhead, shard allocation, BC pricing, and the overall cost of offloading to assist users in making an optimal offloading decision.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Aliyu,

Oh,

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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The computing in the network (COIN) paradigm has emerged as a potential solution for computation-intensive applications like the metaverse by utilizing unused network resources. The blockchain (BC) guarantees task-offloading privacy, but cost reduction, queueing delays, and redundancy elimination remain open problems. This paper presents a redundancy-aware BC-based approach for the metaverse's partial computation offloading (PCO). Specifically, we formulate a joint BC redundancy factor (BRF) and PCO problem to minimize computation costs, maximize incentives, and meet delay and BC offloading constraints. We proved this problem is NP-hard and transformed it into two subproblems based on their temporal correlation: real-time PCO and Markov decision process-based BRF. We formulated the PCO problem as a multiuser game, proposed a decentralized algorithm for Nash equilibrium under any BC redundancy state, and designed a double deep Q-networkbased algorithm for the optimal BRF policy. The BRF strategy is updated periodically based on user computation demand and network status to assist the PCO algorithm. The experimental results suggest that the proposed approach outperforms existing schemes, resulting in a remarkable 47% reduction in cost overhead, delivering approximately 64% higher rewards, and achieving convergence in just a few training episodes.

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“…Literature [1][2] introduced the Residual Number System (RNS) and the Redundant Residual Number System (RRNS) to partition the account tree based on the research of literature [3] to reduce the storage pressure of nodes and ensure the integrity of data, but still failed to solve the problem of transaction data loss. In literature [3][4][5][6], hash consistency algorithm, erasure correction code, improved Shamir secret sharing technology [7] and routing table are respectively used to reduce the storage pressure of nodes. This paper proposes a blockchain storage expansion model based on redundant residue number system (RRNS) on the basis of existing research.…”

Section: Introductionmentioning

confidence: 99%

Blockchain storage optimization model based on remainder system

Wang,

Xu,

Wang

et al. 2023

International Conference on Electronic Materials and Information Engineering (EMIE 2023)

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A Novel Enhanced Lightweight Node for Blockchain

Cited by 13 publications

References 10 publications

HLOChain: A Hierarchical Blockchain Framework with Lightweight Consensus and Optimized Storage for IoT

HLOChain: A Hierarchical Blockchain Framework with Lightweight Consensus and Optimized Storage for IoT

Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Blockchain storage optimization model based on remainder system

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