Leveraging graph convolutional-LSTM for energy-efficient caching in blockchain-based green IoT

Ge Chen, Jun Wu*, Wu Yang*, Ali Kashif Bashir, Gaolei Li, Mohammad Hammoudeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Nowadays, adopting blockchain technology to Internet of Things has become a trend and it is important to minimize energy consumption while providing a high quality of service (QoS) in Blockchain-based IoT networks. Pre-caching popular and fresh IoT content avoids activating sensors frequently, thus effectively reducing network energy consumption. However, the user equipment in regions covered by base stations will generate distributed and time-varying data requests, hence modeling the base station topology to capturing spatio-temporal request patterns is required for the data storage pre-allocation. Traditional solutions typically fail to pay attention to the topology, resulting in the sensor being activated redundantly. In this paper, we propose Request Graph Convolutional-LSTM to capture the spatio-temporal request patterns in Blockchain-based IoT networks and make predictions. Moreover, a heuristic algorithm based on the predictions is proposed to develop pre-caching strategy, which determines the data and location to be cached to minimize the mean data retrieval latency restricted by the cache space of IoT network entities and the freshness of IoT content. Experiments show that our proposed frame provides a low energy consumption.

Original languageEnglish
Article number9388892
Pages (from-to)1154-1164
Number of pages11
JournalIEEE Transactions on Green Communications and Networking
Issue number3
Publication statusPublished - 2021 Sept
Externally publishedYes


  • Internet of Things
  • blockchain
  • graph convolution networks
  • pre-caching
  • spatial-temporal

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications


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