TL;DR
This work proposes techniques that combine the use of gossip and tree-based dissemination that are aimed at combining the robustness of gossip with the resource-efficient of dissemination and aggregation trees while preserving scalability of blockchain protocols.
This work proposes an architecture that supports the dynamic selection of trees based on the node resources, network conditions, and past behaviour of participants to improve the performance of the blockchain system.
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 $7.26\times$.
The growth of the Internet of Things (IoT) has introduced significant security challenges, mainly due to the resource constraints of devices and the limitations of centralized architectures. This paper proposes a blockchain-based Zero-Trust framework for secure and scalable IoT systems. The approach is architecture-agnostic and combines decentralized identity management, hybrid data storage, and edge-assisted computation. To optimize resource usage, raw data are stored off-chain while cryptographic hashes are anchored on the blockchain, ensuring integrity and immutability. A Merkle tree structure is employed to aggregate data efficiently, reducing communication overhead and blockchain transaction costs. Experimental results demonstrate that lightweight cryptographic mechanisms, combined with Merkle-based aggregation, provide strong security guarantees with low energy consumption. The proposed framework achieves improved scalability, robustness, and efficiency, making it suitable for resource-constrained IoT environments.
Blockchain technology provides a decentralized and secure data-management system. However, PoW consensus remains vulnerable to the risk of 51% attacks, where a single miner or pool controls over half of the network and can manipulate blocks and execute double-spending. This paper proposes a defense mechanism called Consensus-Level Restriction (CLR) for Ethereum Classic. It limits the number of sequential blocks from a single miner to reduce the feasibility of 51% attacks. We evaluated the effectiveness of CLR by using BlockSim simulator. Results show that CLR reduces the success rate of 51% attacks while maintaining a decentralized structure. This work strengthens blockchain security without compromising PoW principles.
The convergence of Federated Learning (FL), Blockchain, and Edge Computing presents a transformative paradigm for decentralized, secure, and privacy-preserving machine learning at the network edge. FL enables collaborative model training without centralizing data, while Blockchain provides immutable and transparent mechanisms for trust, accountability, and coordination among distributed edge nodes. Edge computing further enhances this ecosystem by offering low-latency computation near data sources. Despite the promise of this triad, significant challenges persist in terms of scalability, energy efficiency, consensus mechanisms, data and model security, and system heterogeneity. This paper provides a comprehensive survey of the intersection of FL, Blockchain, and Edge Computing, analyzing key opportunities, current solutions, and open challenges. We also discuss architectural frameworks, real-world applications, and future research directions.