Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
Authored by Mohsen Guizani, Yue Zhang, Bin Song, Ying Zhang, Xiaojiang Du
Date Published: 2016
DOI: 10.3390/s16122021
Sponsors:
Chinese National Natural Science Foundation
Platforms:
MATLAB
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
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.
Tags
Vehicles
Social internet
Smart cities
Things