Trust-Aware Virtual Network Embedding in Wireless Sensor Networks

Rezaeimoghaddam, Parinaz; Al‐Anbagi, Irfan

doi:10.1109/jsen.2023.3240386

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…For example, [31] establishes security levels for VNs, but does not consider the security of physical nodes and the underlying physical network. References [29] and [32] incorporate constraints on reputation or security level of physical nodes into the node importance ranking process. All the methods in [29], [31], [32] are designed for centralized VNE algorithms and rely on manually formulated node reliability assessment, which may not accurately reflect the actual network node situation and cannot ensure the security of VNE decisions.…”

Section: Security-aware Virtual Network Embedding Algorithmsmentioning

confidence: 99%

Network resource allocation method based on blockchain and federated learning in IoT

Zhi,

Wang

2024

J. Commun. Netw.

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Virtual network embedding (VNE) is an effective approach to solve the resource allocation problem in IoT networks. But most existing VNE methods are centralized methods, they not only impose an excessive burden on the central server but also result in significant communication overhead. Therefore, this paper proposes a distributed resource allocation method based on federated learning (DRAM-FL) to alleviate the computing and communication overhead, and improve network resource utilization. When utilizing DRAM-FL, it is essential to address the security challenges arising from the unreliable nature of IoT devices. So, we introduce blockchain into DRAM-FL, and propose a distributed resource allocation method based on blockchain and federated learning (DRAM-BFL). In DRAM-BFL, a dual-chain structure is designed to facilitate reliable information exchange among nodes, a node reliability assessment method and EPBFT-NRA consensus algorithm are proposed to improve the security of VNE. Simulation results demonstrate that, compared with other methods, DRAM-BFL can increase the VN acceptance rate and long-term average revenue-to-expenditure ratio while improving system security. In addition, DRAM-BFL exhibits good scalability, and has superior throughput and delay performance in IoT with malicious nodes.

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