HGNN: A Hierarchical Graph Neural Network Architecture for Joint Resource Management in Dynamic Wireless Sensor Networks

被引:1
作者
Giang, Le Tung [1 ]
Tung, Nguyen Xuan [1 ]
Viet, Vu Hoang [2 ]
Chien, Trinh Van [2 ]
Hoa, Nguyen Tien [3 ]
Hwang, Won Joo [4 ]
机构
[1] Pusan Natl Univ, Dept Informat Convergence Engn, Busan 46241, South Korea
[2] Hanoi Univ Sci & Techl, Sch Informat & Commun Technol, Hanoi 100000, Vietnam
[3] Hanoi Univ Sci & Technol, Sch Elect & Elect Engn, Hanoi 100000, Vietnam
[4] Pusan Natl Univ, Sch Comp Sci & Engn, Ctr Artificial Intelligence Res, Busan 46241, South Korea
基金
新加坡国家研究基金会;
关键词
Wireless sensor networks; Sensors; Resource management; Throughput; Servers; Power demand; Graph neural networks; Wireless communication; Quality of service; Internet of Things; Access point (AP) selection; hierarchical graph neural networks (HGNNs); hierarchical wireless sensor network (HWSNs); Internet-of-Things (IoT) sensor networks; power allocation; POWER ALLOCATION; OPTIMIZATION; INTERNET;
D O I
10.1109/JSEN.2024.3485058
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
During the flourishing era of the Internet of Things (IoTs), wireless sensor networks (WSNs) have emerged as a critical backbone for sensing, connectivity, and automation in 6G communications. Due to limited energy sources, minimizing power consumption is the primary focus in extending the lifespan of WSNs. Unfortunately, conventional approaches often face difficulties with scalability and computation complexity, thereby making them insufficient for large-scale WSNs. To address these challenges, graph neural networks (GNNs) have gained significant research attention, thanks to their scalability and generalization capabilities. Nonetheless, existing GNN architectures may struggle to effectively capture the hierarchical topology of WSN systems, where interactions between different levels significantly influence overall network performance. To overcome this challenge, this article proposes a novel hierarchical GNN (HGNN) architecture to learn power allocation and sensor-access point (AP) selection policies that minimizes power consumption in hierarchical WSNs (HWSNs). In this architecture, node and edge update mechanisms are designed to reflect the internal structure of WSNs. Besides, the proposed HGNN is guaranteed representational power, ensuring its ability to capture the graph's information. Numerical results demonstrate the superior performance of the solution produced by the proposed HGNN in reducing power consumption under various network settings. The HGNN can reduce total power consumption by approximately 30% compared with the model-based approaches.
引用
收藏
页码:42352 / 42364
页数:13
相关论文
共 44 条
[1]   Multipath aware scheduling for high reliability and fault tolerance in low power industrial networks [J].
Ahrar, Erfan Mozaffari ;
Nassiri, Mohammad ;
Theoleyre, Fabrice .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2019, 142 :25-36
[2]  
Alon U, 2021, Arxiv, DOI arXiv:2006.05205
[3]   Data-Driven Resource Allocation for Deep Learning in IoT Networks [J].
Chun, Chang-Jae ;
Jeong, Cheol .
IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (02) :2082-2096
[4]   A Detailed Review of Multi-Channel Medium Access Control Protocols for Wireless Sensor Networks [J].
EkbataniFard, GholamHossein ;
Monsefi, Reza .
INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS, 2012, 19 (01) :1-21
[5]   An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges [J].
Elijah, Olakunle ;
Rahman, Tharek Abdul ;
Orikumhi, Igbafe ;
Leow, Chee Yen ;
Hindia, M. H. D. Nour .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (05) :3758-3773
[6]  
Evolved Universal Terrestrial Radio Access (E-UTRA), 2016, NB-IoT
[7]  
Technical Report for BS and UE Radio Transmission and Reception, document TR36.802
[8]   5G Waveforms for IoT Applications [J].
Franco de Almeida, Ivo Bizon ;
Mendes, Luciano Leonel ;
Rodrigues, Joel J. P. C. ;
da Cruz, Mauro A. A. .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2019, 21 (03) :2554-2567
[9]   Resource and Power Allocation for Sum-Throughput Maximization in RIS-Assisted TDMA Wireless Sensor Networks [J].
Ghasemi, Omid Abachian ;
Amirani, Mehdi Chehel ;
Azghani, Masoumeh .
IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (13) :24123-24133
[10]  
Gilmer J, 2017, PR MACH LEARN RES, V70