Differential Game for Resource Allocation in Energy Harvesting Wireless Sensor Networks

被引:8
作者
Al-Tous, Hanan [1 ]
Barhumi, Imad [2 ]
机构
[1] Aalto Univ, Dept Commun & Networking, Espoo 00076, Finland
[2] United Arab Emirates Univ, Coll Engn, Al Ain, U Arab Emirates
来源
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING | 2020年 / 4卷 / 04期
关键词
Wireless sensor network; energy harvesting; differential game; open-loop Nash equilibrium; receding horizon; RECEDING HORIZON CONTROL; POWER ALLOCATION; CHANNELS; OFFLINE; SYSTEMS;
D O I
10.1109/TGCN.2020.3009268
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In this paper, we consider power control and data scheduling in an Energy Harvesting (EH) multi-hop Wireless Sensor Network (WSN) using Differential Game (DG) framework. The network consists of M sensor nodes aiming to send their data to a sink node. Each sensor node has a battery of limited capacity to save the harvested energy and a buffer of limited size to store both the sensed and relayed data from neighboring nodes. Each sensor node can exchange information within its neighborhood using single-hop transmission. Our goal is to develop a distributed algorithm that adaptively changes the transmitted data and power according to the traffic load and available energy such that the sensed data are received at the sink node. DG framework is proposed to efficiently utilize the available harvested energy and balance the buffer of all sensor nodes. The solution is obtained based on the open-loop receding horizon Nash equilibrium. Simulation results demonstrate the merits of the proposed solution.
引用
收藏
页码:1165 / 1173
页数:9
相关论文
共 36 条
[1]  
ALTOUS H, 2018, P IEEE ICC 2018 KANS
[2]  
[Anonymous], 2001, P 40 IEEE C DECISION
[3]  
[Anonymous], 1998, SIAM
[4]   A Learning Theoretic Approach to Energy Harvesting Communication System Optimization [J].
Blasco, Pol ;
Guenduez, Deniz ;
Dohler, Mischa .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2013, 12 (04) :1872-1882
[5]  
Conte C, 2012, IEEE DECIS CONTR P, P6819, DOI 10.1109/CDC.2012.6426138
[6]  
Engwerda J.C., 2005, LQ DYNAMIC OPTIMIZAT
[7]   Distributed Receding Horizon Control of Constrained Networked Leader-Follower Formations Subject to Packet Dropouts [J].
Franze, Giuseppe ;
Casavola, Alessandro ;
Famularo, Domenico ;
Lucia, Walter .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2018, 26 (05) :1798-1809
[8]  
Giannakis G. B, 2011, CROSS LAYER DESIGNS, P1
[9]  
GonzalezSanchez D, 2013, SPRINGERBRIEF MATH, P1, DOI 10.1007/978-3-319-01059-5
[10]  
Grant Michael, 2014, Cvx: Matlab software for disciplined convex programming (web page and software)