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

被引：1
作者：
Zhang, Yue [1 ]
Song, Bin [1 ]
Zhang, Ying [1 ]
Du, Xiaojiang [2 ]
Guizani, Mohsen [3 ]
机构：
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA
[3] Univ Idaho, Dept Elect & Comp Engn, Moscow, ID 83844 USA
来源：
SENSORS | 2016年 / 16卷 / 12期
基金：
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词：
agent-based modelling; emerging sensor networks; Internet of Things; market model; reinforcement learning; resource allocation; topology management; SOCIAL INTERNET; VEHICLES; THINGS;
D O I：
10.3390/s16122021
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
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.
引用
收藏
页数：16
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