Robust networking: Dynamic topology evolution learning for internet of things

Ning Chen, Tie Qiu, Mahmoud Daneshmand, Dapeng Oliver Wu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

Original languageEnglish
Article number3446937
JournalACM Transactions on Sensor Networks
Volume17
Issue number3
DOIs
StatePublished - Aug 2021

Keywords

  • Dynamic topology
  • Evolution learning
  • Internet of Things
  • Robustness optimization

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