Market model for resource allocation in emerging sensor networks with reinforcement learning

Yue Zhang, Bin Song, Ying Zhang, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users’ patterns. Reinforcement learning methods are introduced to estimate users’ patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management.

Original languageEnglish
Article number2021
JournalSensors (Switzerland)
Volume16
Issue number12
DOIs
StatePublished - 1 Dec 2016

Keywords

  • Agent-based modelling
  • Emerging sensor networks
  • Internet of Things
  • Market model
  • Reinforcement learning
  • Resource allocation
  • Topology management

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