Next Article in Journal
Measurement of M2-Curve for Asymmetric Beams by Self-Referencing Interferometer Wavefront Sensor
Next Article in Special Issue
Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments
Previous Article in Journal
Localization Based on Magnetic Markers for an All-Wheel Steering Vehicle
Previous Article in Special Issue
An Improved Mobility-Based Control Protocol for Tolerating Clone Failures in Wireless Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(12), 2021; doi:10.3390/s16122021

Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning

1
The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
2
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
3
Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Muhammad Imran, Athanasios V. Vasilakos, Thaier Hayajneh and Neal N. Xiong
Received: 29 September 2016 / Revised: 16 November 2016 / Accepted: 24 November 2016 / Published: 29 November 2016
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
View Full-Text   |   Download PDF [917 KB, uploaded 29 November 2016]   |  

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. View Full-Text
Keywords: agent-based modelling; emerging sensor networks; Internet of Things; market model; reinforcement learning; resource allocation; topology management agent-based modelling; emerging sensor networks; Internet of Things; market model; reinforcement learning; resource allocation; topology management
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, Y.; Song, B.; Zhang, Y.; Du, X.; Guizani, M. Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning. Sensors 2016, 16, 2021.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top