2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 9766-9782· 1 citation· 35 references
TL;DR
Experimental results demonstrate significant reductions in end-to-end latency, redundant traffic transmission, and synchronization overhead, while improving caching efficiency, traffic utilization, and adaptive content dissemination performance compared with conventional IP-based blockchain communication systems.
Abstract
Sixth-generation (6G) communication networks are expected to support immersive, intelligent, and latency-sensitive applications that require scalable, secure, and adaptive communication infrastructures. However, conventional IP-based blockchain systems suffer from significant communication overhead, redundant data dissemination, synchronization delays, and limited scalability, which hinder their applicability in dynamic 6G environments. To address these challenges, this paper proposes a Quality of Experience (QoE)- driven multimodal multi-agent Named Data Networking (NDN)-enabled blockchain framework for adaptive 6G communication systems. The proposed architecture integrates content-centric networking, in-network caching, multicast dissemination, and blockchain-based decentralized trust management to improve communication efficiency and scalability. Furthermore, the framework incorporates multimodal data fusion, cooperative multi-agent coordination, QoE-aware adaptive learning, and federated edge intelligence to enable intelligent and user-centric communication optimization. A distributed peer-to-peer overlay and optimized NDN-based communication protocols are designed to support efficient blockchain synchronization while maintaining interoperability with existing Ethereum infrastructures. The proposed framework is evaluated using ndnSIM, NS-3, and a modified Go-Ethereum client across multiple distributed network topologies. Experimental results demonstrate significant reductions in end-to-end latency, redundant traffic transmission, and synchronization overhead, while improving caching efficiency, traffic utilization, and adaptive content dissemination performance compared with conventional IP-based blockchain communication systems. The results validate the effectiveness of integrating NDN, blockchain, multimodal intelligence, and distributed adaptive coordination to establish a scalable, secure, and QoE-aware foundation for future intelligent 6G communication ecosystems.
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
A blockchain-based dynamically adaptive restructuring framework that enables real-time IoV cluster restructuring by splitting overloaded IoVs to reduce communication overhead, or merging nearby IoVs to optimize resource utilization, offering a proactive, adaptive security paradigm for intelligent transportation frameworks.
Jiawei Shi, Yebo Feng, Konglin Zhu et al.· ACM Transactions on Internet...· 0 citations
The Metaverse is emerging as a next-generation digital ecosystem that integrates immersive virtual environments with real-time communication, artificial intelligence (AI), extended reality (XR), Internet of Things (IoT), blockchain, and cloud-edge computing technologies. However, supporting seamless interaction among millions of concurrent users requires a robust communication infrastructure capable of delivering ultra-low latency, high bandwidth, reliable connectivity, and secure data exchange. This paper presents an intelligent Metaverse communication infrastructure that integrates 6G wireless networks, Multi-access Edge Computing (MEC), Software-Defined Networking (SDN), Network Function Virtualization (NFV), AI-driven resource management, and blockchain-enabled security to optimize communication performance in immersive environments. The proposed architecture dynamically allocates network resources using machine learning-based traffic prediction, deploys edge intelligence for latency-sensitive applications, and employs blockchain for decentralized identity management and secure data transactions. Furthermore, digital twin-assisted network monitoring continuously analyzes communication conditions and enables proactive optimization to improve service reliability and Quality of Experience (QoE). Experimental evaluation demonstrates that the proposed framework significantly reduces communication latency, improves network throughput, enhances resource utilization, and strengthens security compared with conventional cloud-centric communication architectures. The proposed infrastructure provides a scalable, intelligent, and secure communication framework for future Metaverse applications, including virtual collaboration, digital healthcare, industrial automation, online education, and immersive entertainment.
Sheelam Abhiram, N. Vishnuvardhan, P. K. Reddy· International Journal of Sci...· 0 citations
The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe constraints in IoT scenes, such as privacy issues, network congestion, and network bottlenecks. The paper has presented a new 6G-integrated federated learning system which builds on the native intelligence of 6G networks to support secure, efficient, and scalable cooperative learning among heterogeneous edge-IoT devices. The suggested architecture combines terahertz frequencies to synchronize model communication in real-time at ultra-low latency, reconfigurable intelligent surfaces to improve the quality of communication, and network slicing to provide differentiated quality-of-service assurances. Another new hierarchical federated learning system integrates intra-edge aggregation and inter-edge cooperation that can reduce communication overhead by 85-percent and achieve the same accuracy in models. This framework integrates blockchain-based trust management involving zero-knowledge proofs of verifiable model update, to provide integrity and accountability without impacting on privacy. Experimental analysis of massive scale edge-IoT applications has revealed that the suggested scheme attains 97.2% model precision and lowers communication expenses by 87 percent and convergence rate by 3.4 times that of traditional federated learning techniques. The framework has high-uniform performance in adversarial environments where 99.6 percent of malicious model updates are identified with a small false positive. The results define the 6G-integrated federated learning as a framework of reliable and scalable edge-IoT cooperation.
T.Muthumanickam, D. Jayalakshmi, Sathiyamoorthy M et al.· 2026 6th International Confe...· 0 citations
In blockchain systems, peer-to-peer (P2P) overlay networks play a crucial role in providing reliable, scalable and efficient message-delivery services to upper layers. However, the consensus layer and the underlying P2P network remain mutually opaque in existing blockchains, waiving the opportunity for further improvement. In contrast to other P2P applications, blockchain can naturally be abstracted as a state machine. We therefore leverage this abstraction to record network topologies and latencies in a trusted and coordinated manner. With this support, we propose three improvements to rotating-leader consensus protocols and their underlying P2P networks: (1) accelerating leader rotation; (2) introducing a reliable-broadcast paradigm that employs tree-based dissemination in the normal case and falls back to gossip only when necessary; and (3) constructing latency-aware dissemination trees. We integrate the above ideas into Tendermint and libp2p, and conduct empirical evaluation on Amazon EC2 platform using up to 300 nodes distributed across 10 regions. The results demonstrate that, compared with gossip-based dissemination over the same topology, our prototype improves peak throughput by up to $7.26\times$.
Yifeng Ye, Rongji Huang, Gerui Wang et al.· 0 citations
With the evolution of Software-Defined Networking (SDN) toward distribution and servitization, multi-controller architectures have been widely adopted to enhance scalability. However, such architectures face challenges in inter-domain trust and scalability. To address these issues, this paper proposes a blockchain-enabled trusted routing scheme for SDN inter-domain communication, denoted as BTR-SDN. First, a multi-dimensional model based on the Fuzzy Analytic Hierarchy Process (FAHP) is designed to quantify node trust values, and a sliding-window trust recovery mechanism with a time-decay factor is introduced to enable penalized nodes to regain trust through compliant behaviors gradually. Second, a trust-aware hierarchical topology reconstruction and route-pruning mechanism is introduced to adaptively adjust the network view and path selection strategy based on trust evaluation results. Third, to address scalability bottlenecks, we construct a layered “on-chain anchoring + off-chain storage” architecture. Critical trust proofs are anchored on-chain for attestation, while voluminous routing data is stored off-chain. Experimental results demonstrate that BTR-SDN achieves a 20.5% higher malicious node detection rate and improves bandwidth utilization by 10.7% compared to benchmark schemes, maintaining high throughput even under dynamic adversarial conditions.
Shuang Yang, Zhongsheng Jiang, Wansu Pan et al.· Journal of Network and Syste...· 0 citations