Machine-Learning-Based Optimal Cooperating Node Selection for Internet of Underwater Things

被引:6
作者
Ahmad, Ishtiaq [1 ]
Narmeen, Ramsha [1 ]
Kaleem, Zeeshan [2 ]
Almadhor, Ahmad [3 ]
Alkhrijah, Yazeed [4 ]
Ho, Pin-Han [5 ]
Yuen, Chau [6 ]
机构
[1] Czech Tech Univ, Fac Elect Engn, Prague 16000, Czech Republic
[2] King Fahd Univ Petr & Minerals KFUPM, Dept Comp Engn, Dhahran 31261, Saudi Arabia
[3] Jouf Univ, Coll Comp & Informat Sci, Dept Comp Engn & Networks, Sakakah 72388, Saudi Arabia
[4] Imam Mohammad Ibn Saud Islam Univ, Coll Engn, Dept Elect Engn, Riyadh 11564, Saudi Arabia
[5] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[6] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
来源
IEEE INTERNET OF THINGS JOURNAL | 2024年 / 11卷 / 12期
关键词
Routing protocols; Reliability; Propagation delay; Heuristic algorithms; Underwater communication; Routing; Internet of Things; Deep learning; Internet of Underwater Things (IoUT); multihop communication; underwater wireless sensor network; ROUTING PROTOCOL; ACOUSTIC COMMUNICATION; ENERGY-EFFICIENT; NETWORKS;
D O I
10.1109/JIOT.2024.3381834
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multihop communication has gained prominence within the realm of the Internet of Underwater Things (IoUT) owing to its exceptional reliability amidst the challenges posed by the underwater acoustic environment. Despite this, the persistence of limitations caused by propagation delay, high collision rate, and limited energy in underwater communication remains, representing the most formidable hurdles in ensuring the successful transmission of data gathered by sensor nodes. To address these challenges, we employ a machine learning (ML)-based optimal cooperating node selection for each hop, considering the Shortest propagation delay, minimal residual Energy, and a low Collision rate (referred to as SEC). For this purpose, we initially assemble the sensor nodes to create a list of cooperative nodes, considering the aspect of SEC. Then, using an assembled list of cooperating sensor nodes, we employ ML-based algorithms, such as reinforcement learning (RL-SEC), deep Q-networks (DQN-SEC), and deep deterministic policy gradient (DDPG-SEC), to predict the optimal cooperating node for each hop. The simulation results of the DDPG-SEC demonstrate a significant improvement of approximately 56% when compared with RL-SEC, DQN-SEC, and other state-of-the-art techniques.
引用
收藏
页码:22471 / 22482
页数:12
相关论文
共 36 条
[1]   Dynamic clustering and management of mobile wireless sensor networks [J].
Abuarqoub, Abdelrahman ;
Hammoudeh, Mohammad ;
Adebisi, Bamidele ;
Jabbar, Sohail ;
Bounceur, Ahcene ;
Al-Bashar, Hashem .
COMPUTER NETWORKS, 2017, 117 :62-75
[2]  
Ahmad I., 2019, ELECTRONICS-SWITZ, V8, P1297, DOI DOI 10.3390/electronics8111297
[3]   Effective SNR Mapping and Link Adaptation Strategy for Next-Generation Underwater Acoustic Communications Networks: A Cross-Layer Approach [J].
Ahmad, Ishtiaq ;
Chang, Kyunghi .
IEEE ACCESS, 2019, 7 :44150-44164
[4]   Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing [J].
Berger, Christian R. ;
Zhou, Shengli ;
Preisig, James C. ;
Willett, Peter .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (03) :1708-1721
[5]   A simple cooperative diversity method based on network path selection [J].
Bletsas, A ;
Khisti, A ;
Reed, DP ;
Lippman, A .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2006, 24 (03) :659-672
[6]  
Chen YG, 2021, CHINA COMMUN, V18, P224, DOI 10.23919/JCC.2021.08.016
[7]   ACOA-AFSA Fusion Dynamic Coded Cooperation Routing for Different Scale Multi-Hop Underwater Acoustic Sensor Networks [J].
Chen, Yougan ;
Zhu, Jianying ;
Wan, Lei ;
Huang, Shenqin ;
Zhang, Xinhai ;
Xu, Xiaomei .
IEEE ACCESS, 2020, 8 :186773-186788
[8]   Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service [J].
Du, Jun ;
Jiang, Chunxiao ;
Wang, Jian ;
Ren, Yong ;
Debbah, Merouane .
IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2020, 15 (04) :122-134
[9]   Opportunistic Cooperative Transmission for Underwater Communication Based on the Water's Key Physical Variables [J].
El-Banna, Ahmad A. Aziz ;
Wu, Kaishun ;
ElHalawany, Basem M. .
IEEE SENSORS JOURNAL, 2020, 20 (05) :2792-2802
[10]  
Han C, 2022, IEEE INT CONF COMMUN, P162, DOI 10.1109/ICCC55456.2022.9880743