Symbiotic Communication improves spectrum efficiency and sustainability in 6G through cooperative resource exchange among heterogeneous devices. Its integration with blockchain enables Symbiotic Blockchain Networks (SBNs), which provide decentralized and immutable agreements for symbiotic services. However, these systems assume that off-chain wireless observations are correct. This assumption is unreliable in adversarial and noisy PHY-layer environments. This paper identifies a fundamental trust gap between off-chain observations and on-chain consensus. We adapt OCR-style off-chain aggregation to Symbiotic Blockchain Networks (SBNs) and analyze its role as a pre-consensus validation layer for noisy and adversarial wireless service observations. The contributions are threefold: (i) formulation of trust in symbiotic 6G systems as a decentralized aggregation problem under uncertain PHY observations; (ii) design of a selective anchoring mechanism that records only quorum-approved outcomes; and (iii) analytical and experimental evaluation indicating improved trust robustness while preserving scalability, latency, and energy efficiency. Our proposed approach provides a practical foundation for trustworthy symbiotic service exchange in large-scale 6G environments.
Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.
Yayu Gao, Yong Xiao, Hao Hu et al.· IEEE Transactions on Cogniti...· 0 citations
The rapid growth of Internet of Things (IoT) systems has introduced significant challenges related to privacy, trust, scalability, and attack resilience, particularly in resource-constrained and location-sensitive environments. Existing blockchain consensus mechanisms provide decentralised trust, but they often suffer from high communication overhead, weak physical-context awareness, and limited privacy protection when deployed in large-scale IoT networks. This paper proposes Elastic Proof-of-Location Byzantine Fault Tolerance (EPoLBFT), a privacy-preserving and location-aware blockchain consensus framework for IoT systems. The proposed design enables IoT nodes to prove regional eligibility without revealing exact coordinates while restricting consensus participation to trusted and geographically verified validators. EPoLBFT is evaluated using the Blockchain IoT Consensus Algorithm (BICA) simulator under normal, high-load, Byzantine, Sybil, location-spoofing, and denial-of-service scenarios. The evaluation considers latency, throughput, communication overhead, energy consumption, and attack resilience. The results show that EPoLBFT reduces communication overhead and improves consensus efficiency compared with conventional PBFT-based approaches while strengthening resilience against location-based and identity-based attacks. The study also discusses the privacy–latency trade-off introduced by zk-PoL, the assumptions related to trusted location anchors, and the limitations of simulation-based evaluation.
Yunus Kareem, D. Djenouri, Essam Ghadafi· Future Internet· 0 citations
Trust management in heterogeneous Internet of Things (IoT) deployments remains unresolved because most frameworks treat blockchain reputation persistence and edge-AI anomaly detection as separate subsystems, so the graded evidence a detection model produces cannot be indexed or propagated by a ledger that was built without reference to it. This study presents BEAT (Blockchain–Edge AI Trust), which closes that gap through co-design of three coupled layers: a Graph Attention Network–Long Short-Term Memory (GAT-LSTM) edge module that converts device interaction graphs into normalised trust evidence vectors; a Hyperledger Fabric 2.5 ledger whose TrustCC chaincode enforces monotone trust updates over a formally defined trust lattice; and a Proximal Policy Optimisation (PPO) admission controller whose advantage over contextual-bandit and PID baselines is established both theoretically and experimentally. A companion gossip protocol, GARP, propagates reputation deltas across edge orchestrators with a proven logarithmic convergence guarantee. Prototyped on a 12-node Raspberry Pi 5 cluster attached to a three-peer Fabric channel and evaluated on UNSW-NB15, N-BaIoT, TON IoT, and CICIoT2023, BEAT reaches a cross-dataset mean F1 of 0.961, an on-chain throughput of 1,847 transactions per second, and a median trust-decision latency of 38 ms. BEAT outperforms a GNN-Transformer baseline (F1 0.944) and a CNN-BiLSTM-Transformer baseline (F1 0.939) on UNSW-NB15, and ablation results show that removing any single BEAT layer costs at least four F1 points cross-dataset, evidence that the system-level integration, not the detection model alone, drives the gain. Formal Byzantine resilience bounds accompany the prototype evaluation, alongside experimental Sybil-resistance and evidence-poisoning results.
Pavansai Ramarao Maddali, Vijay Kumar Damera, Ratna Kumar Prathipati· International Journal of Adv...· 0 citations
To address the risks of centralized single-point failures and data privacy leaks associated with massive data storage in Intelligent Transportation Systems (ITS) and Vehicle-to-Everything (V2X) environments, this paper proposes a distributed secure storage architecture based on blockchain. However, traditional Practical Byzantine Fault-Tolerant (PBFT) algorithms suffer from scalability bottlenecks in large-scale dynamic networks, such as high communication overhead and low consensus efficiency. Therefore, this paper designs a hierarchical and reputation-aware improved consensus mechanism (H-PBFT). First, this mechanism introduces a geographical location grouping strategy, dividing all network nodes into several local consensus groups and leveraging edge computing characteristics to achieve rapid consensus within each group. Second, a multi-dimensional reputation evaluation model (comprehensively considering historical behavior, performance, and availability) is constructed to dynamically elect representative nodes from each group to participate in global consensus, thereby significantly reducing the communication complexity from O(N2). Simulation results show that compared with standard PBFT, Q-PBFT, and APBFT, H-PBFT exhibits significant advantages in consensus latency, throughput, and view switching recovery time, and maintains high system robustness even in complex network environments with malicious nodes.
Zhenhua Wang, Jiangang Hu, Xinmeng Wang et al.· Future Internet· 0 citations
The rapid development of the Internet of Things (IoT) has placed considerable pressure on both security and stability in heterogeneous, resource-constrained networks. In such dynamic environments, trust management is a central issue to determine which service providers can be trusted and to combat malicious activity. Although blockchain-based solutions have offered a means for decentralized, tamper-resistant trust management, most rely on classical cryptographic primitives, which are vulnerable to future quantum computing attacks. This study proposes a Quantum-Resistant Blockchain-Based Trust Management (QR-BCTM) framework in which Post-Quantum Cryptographic mechanisms, Permissioned Blockchain Platform, and Fog-assisted Trust Management architecture are combined and utilized in IoT networks. The framework introduces a quantum-aware trust computation model that combines behavioral trust, indirect recommendations, and a cryptographic assurance score quantifying each participant’s compliance with security requirements. Trust evidence is compressed to reduce blockchain storage and communication overhead, while the hierarchical fog-blockchain architecture offloads computationally intensive operations from resource-constrained IoT devices. The performance of the framework has been simulated in the presence of an adversary, including bad-mouthing, ballot-stuffing, on-off behavior, and identity attacks using a Sybil-type mechanism. Trust accuracy, false trust acceptance, communication overhead, and computation cost were measured, and a sensitivity analysis on the trust-weight parameters was performed. The simulation results suggest that QR-BCTM can enhance the accuracy of trust evaluation, mitigate the impact of malicious nodes, and remain scalable and efficient despite the existing cryptographic overhead. Post-quantum digital signatures and formal security analysis provide protection against quantum-era threats and attacks, while classical threats are mitigated through behavioral trust aggregation and recommendation filtering. In summary, QR-BCTM provides a scalable, simulation-validated and quantum-aware framework for trustworthy IoT network operation, offering practical guidelines for future deployment and prototyping.
M. A. Al-Khasawneh, D. Alsekait, K. Alkayid et al.· Scientific Reports· 0 citations
Blockchain can preserve supply-chain records, but ledger integrity alone does not show whether a participant should be trusted in a future risk-sensitive transaction. Existing reputation systems mainly address product evidence, global feedback aggregation, or review authenticity, while giving less attention to repeated bilateral inflation, identity multiplicity, and unfair decay for honest participants with sparse histories. We present \RC, a participant trust framework that uses blockchain as an evidence and provenance layer rather than as the source of trust. Governed interaction outcomes are converted into bounded evidence. Repeated interactions between the same pair are discounted, low counterparty diversity is penalized, governance-supplied identity confidence weights positive evidence, and scores decay toward a neutral prior according to verified interaction volume. Identity, contract, outcome, and update provenance remain on chain, while nonlinear reputation computation is performed off chain and checked on chain for admissibility. In controlled simulations with 30 seeded runs and matched interaction traces, the full model reduces mean collusive gain to 0.1443, compared with 0.3688 for naive mean evidence and 0.3585 for static decay. With ten identities under one controller, the reputation inflation ratio falls to 0.8723, while three comparison baselines remain above 1.08. On identical newcomer traces, volume-aware decay increases mean newcomer reputation from 0.6626 to 0.7589 and reduces the false low-trust rate from 0.3633 to 0.1683. Paired analysis confirms these improvements across runs. The results support a bounded reduction in reputation distortion, not attacker detection. Deployment evaluation and calibration with operational data are still required before production use.
Adnan Iftekhar, Chengliang Zheng, Xiaohui Cui et al.· 0 citations