{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T18:26:32Z","timestamp":1782239192920,"version":"3.54.5"},"reference-count":65,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2023R97"],"award-info":[{"award-number":["PNURSP2023R97"]}]},{"name":"Deanship of Scientific Research, Taif University","award":["PNURSP2023R97"],"award-info":[{"award-number":["PNURSP2023R97"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.<\/jats:p>","DOI":"10.3390\/systems11110529","type":"journal-article","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T05:01:08Z","timestamp":1698555668000},"page":"529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4738-3216","authenticated-orcid":false,"given":"Elmustafa","family":"Sayed Ali","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), P.O. Box 407, Khartoum 00407, Sudan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9872-081X","authenticated-orcid":false,"given":"Rashid A.","family":"Saeed","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim Khider","family":"Eltahir","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), P.O. Box 407, Khartoum 00407, Sudan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maha","family":"Abdelhaq","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2350-5661","authenticated-orcid":false,"given":"Raed","family":"Alsaqour","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, P.O. Box 93499, Riyadh 93499, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rania A.","family":"Mokhtar","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), P.O. Box 407, Khartoum 00407, Sudan"},{"name":"Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102304","DOI":"10.1016\/j.simpat.2021.102304","article-title":"IoUT: Modelling and simulation of Edge-Drone-based Software-Defined smart Internet of Underwater Things","volume":"109","author":"Kamalika","year":"2021","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nayyar, A., Ba, C.H., Cong Duc, N.P., and Binh, H.D. (2019, January 19). Smart-IoUT 1.0: A Smart Aquatic Monitoring Network Based on Internet of Underwater Things (IoUT). Proceedings of the International Conference on Industrial Networks and Intelligent Systems, Ho Chi Minh, Vietnam.","DOI":"10.1007\/978-3-030-05873-9_16"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, H., and Cho, H.-S. (2017). SOUNET: Self-Organized Underwater Wireless Sensor Network. Sensors, 17.","DOI":"10.3390\/s17020283"},{"key":"ref_4","first-page":"1","article-title":"Underwater Sensor Network Applications: A Comprehensive Survey","volume":"2015","author":"Felemban","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4514","DOI":"10.3390\/s21134514","article-title":"Adaptive Node Clustering for Underwater Sensor Networks","volume":"21","author":"Khan","year":"2021","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e7815","DOI":"10.1002\/cpe.7815","article-title":"Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review","volume":"35","author":"Gola","year":"2023","journal-title":"Concurr. Comput. Pract. Exper."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khalifa, O.O., Roubleh, A., Esgiar, A., Abdelhaq, M., Alsaqour, R., Abdalla, A., Ali, E.S., and Saeed, R. (2022). An IoT-Platform-Based Deep Learning System for Human Behavior Recognition in Smart City Monitoring Using the Berkeley MHAD Datasets. Systems, 10.","DOI":"10.3390\/systems10050177"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6025","DOI":"10.3390\/s23136025","article-title":"Advancements in Neighboring-Based Energy-Efficient Routing Protocol (NBEER) for Underwater Wireless Sensor Networks","volume":"23","author":"Shah","year":"2023","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9005","DOI":"10.1109\/JIOT.2021.3055857","article-title":"Game-Theory-Based Clustering Scheme for Energy Balancing in Underwater Acoustic Sensor Networks","volume":"8","author":"Xing","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11176","DOI":"10.1109\/ACCESS.2017.2713640","article-title":"Review on Clustering, Coverage and Connectivity in Underwater Wireless Sensor Networks: A Communication Techniques Perspective","volume":"5","author":"Sandeep","year":"2017","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21118","DOI":"10.1109\/ACCESS.2019.2897872","article-title":"Mobile Data Gathering with Hop-Constrained Clustering in Underwater Sensor Networks","volume":"7","author":"Ghoreyshi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1109\/COMST.2021.3053118","article-title":"Internet of Underwater Things and Big Marine Data Analytics\u2014A Comprehensive Survey","volume":"23","author":"Jahanbakht","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.engappai.2019.04.007","article-title":"Swarm intelligence for clustering\u2014A systematic review with new perspectives on data mining","volume":"82","author":"Figueiredo","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103594","DOI":"10.1016\/j.jnca.2023.103594","article-title":"A systematic review on energy efficiency in the internet of underwater things (IoUT): Recent approaches and research gaps","volume":"213","author":"Elmustafa","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103295","DOI":"10.1016\/j.jnca.2021.103295","article-title":"A survey on energy efficiency in underwater wireless communications","volume":"198","author":"Islam","year":"2022","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6470359","DOI":"10.1155\/2019\/6470359","article-title":"Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges","volume":"2019","author":"Awan","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/COMST.2021.3134955","article-title":"Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey","volume":"24","author":"Wei","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103319","DOI":"10.1016\/j.advengsoft.2022.103319","article-title":"An empirical study on underwater acoustic sensor networks based on localization and routing approaches","volume":"175","author":"Kamal","year":"2023","journal-title":"Adv. Eng. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1186\/s13638-019-1533-y","article-title":"Improved energy-balanced algorithm for underwater wireless sensor network based on depth threshold and energy level partition","volume":"2019","author":"Feng","year":"2019","journal-title":"J. Wirel. Commun. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.3390\/s22041618","article-title":"Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks","volume":"22","author":"Mohan","year":"2022","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fattah, S., Gani, A., Ahmedy, I., Idris, M.Y.I., and Hashem, I.A.T. (2020). A Survey on Underwater Wireless Sensor Networks: Requirements, Taxonomy, Recent Advances, and Open Research Challenges. Sensors, 20.","DOI":"10.3390\/s20185393"},{"key":"ref_22","unstructured":"Elmustafa, S., and Rashid, A. (2021). Intelligent Wireless Communications, IET."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sathish, K., Cv, R., Ab Wahab, M.N., Anbazhagan, R., Pau, G., and Akbar, M.F. (2023). Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe. Sensors, 23.","DOI":"10.3390\/s23104844"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahmed, G., Zhao, X., and Fareed, M.M.S. (2019). A Hybrid Energy Equating Game for Energy Management in the Internet of Underwater Things. Sensors, 19.","DOI":"10.3390\/s19102351"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25027","DOI":"10.1109\/JIOT.2022.3195223","article-title":"An Unequal Clustering Method Based on Particle Swarm Optimization in Underwater Acoustic Sensor Networks","volume":"9","author":"Hou","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108309","DOI":"10.1016\/j.comnet.2021.108309","article-title":"Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks","volume":"197","author":"Khan","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, L., Qiu, Y., and Xu, J. (2022). A K-Means Clustered Routing Algorithm with Location and Energy Awareness for Underwater Wireless Sensor Networks. Photonics, 9.","DOI":"10.3390\/photonics9050282"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e5283","DOI":"10.1002\/dac.5283","article-title":"Energy hole mitigation through optimized cluster head selection and strategic routing in Internet of Underwater Things","volume":"35","author":"Gupta","year":"2022","journal-title":"Int. J. Commun. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Subramani, N., Prakash, M., Youseef, A., Saleh, A., and Osamah, I.K. (2022). An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks. Sensors, 22.","DOI":"10.3390\/s22020415"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e5560","DOI":"10.1002\/dac.5560","article-title":"Energy-optimized cluster head selection based on enhanced remora optimization algorithm in underwater wireless sensor network","volume":"36","author":"Singh","year":"2023","journal-title":"Int. J. Commun. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1117787","DOI":"10.3389\/fmars.2023.1117787","article-title":"Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization","volume":"10","author":"Salil","year":"2023","journal-title":"Front. Mar. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"116932","DOI":"10.1109\/ACCESS.2023.3325311","article-title":"Reliable and Delay Aware Routing Protocol for Underwater Wireless Sensor Networks","volume":"11","author":"Ullah","year":"2023","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Khan, Z.U., Gang, Q., Muhammad, A., Muzzammil, M., Khan, S.U., El Affendi, M., Ali, G., Ullah, I., and Khan, J. (2022). A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks. Electronics, 11.","DOI":"10.3390\/electronics11193015"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Almazrouei, K., Kamel, I., and Rabie, T. (2023). Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning. Appl. Sci., 13.","DOI":"10.3390\/app13148174"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"76","DOI":"10.4018\/IJITWE.2020070105","article-title":"FLCEER: Fuzzy Logic Cluster-Based Energy Efficient Routing Protocol for Underwater Acoustic Sensor Network","volume":"15","author":"Natesan","year":"2020","journal-title":"Int. J. Inf. Technol. Web Eng. IJITWE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"72907","DOI":"10.1109\/ACCESS.2022.3188654","article-title":"Efficient Energy Mechanism in Heterogeneous WSNs for Underground Mining Monitoring Applications","volume":"10","author":"Alsaqour","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, X., Wang, B., Sun, W., Du, H., and Zhao, Y. (2023). Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework. Sensors, 23.","DOI":"10.3390\/s23125759"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ahmad, I., Rahman, T., Zeb, A., Khan, I., Othman, M.T.B., and Hamam, H. (2022). Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks. Sensors, 22.","DOI":"10.3390\/s22186945"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e696","DOI":"10.7717\/peerj-cs.696","article-title":"A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments","volume":"7","author":"Qawqzeh","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xiao, X., Huang, H., and Wang, W. (2021). Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms. Appl. Sci., 11.","DOI":"10.3390\/app11010312"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Khanna, A., Polkowski, Z., and Castillo, O. (2023). Data Analytics and Management, Springer. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-981-19-7615-5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/15501329221117118","article-title":"A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks","volume":"18","author":"Lilhore","year":"2022","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"55868","DOI":"10.1109\/ACCESS.2022.3177722","article-title":"Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks","volume":"10","author":"Chinnasamy","year":"2022","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jalal, R.D., and Aliesawi, S.A. (2023, January 9\u201312). Enhancing TEEN Protocol using the Particle Swarm Optimization and BAT Algorithms in Underwater Wireless Sensor Network. Proceedings of the 15th International Conference on Developments in eSystems Engineering (DeSE), Baghdad, Iraq.","DOI":"10.1109\/DeSE58274.2023.10100062"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gadal, S., Mokhtar, R., Abdelhaq, M., Alsaqour, R., Ali, E.S., and Saeed, R. (2022). Machine Learning-Based Anomaly Detection Using K-mean Array and Sequential Minimal Optimization. Electronics, 11.","DOI":"10.3390\/electronics11142158"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liang, J., Jiang, S., and Chen, W. (2016). A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms. Sensors, 16.","DOI":"10.3390\/s16020212"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xiao, X., and Huang, H. (2020). A Clustering Routing Algorithm Based on Improved Ant Colony Optimization Algorithms for Underwater Wireless Sensor Networks. Algorithms, 13.","DOI":"10.3390\/a13100250"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102317","DOI":"10.1016\/j.adhoc.2020.102317","article-title":"Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization","volume":"110","author":"Maheshwari","year":"2021","journal-title":"Ad Hoc Netw."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Aadil, F., Bajwa, K.B., Khan, S., Chaudary, N.M., and Akram, A. (2016). CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154080"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1007\/s11277-020-07418-8","article-title":"Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks","volume":"116","author":"Zehra","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s11590-009-0168-z","article-title":"Minimum average routing path clustering problem in multi-hop 2-D underwater sensor networks","volume":"4","author":"Kim","year":"2020","journal-title":"Optim. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cui, Y., Zhu, P., Lei, G., Chen, P., and Yang, G. (2023). Energy-Efficient Multiple Autonomous Underwater Vehicle Path Planning Scheme in Underwater Sensor Networks. Electronics, 12.","DOI":"10.3390\/electronics12153321"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"93831","DOI":"10.1109\/ACCESS.2021.3093113","article-title":"Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications","volume":"9","author":"Ghorpade","year":"2021","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sun, W., Tang, M., Zhang, L., Huo, Z., and Shu, L. (2020). A Survey of Using Swarm Intelligence Algorithms in IoT. Sensors, 20.","DOI":"10.3390\/s20051420"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e5147","DOI":"10.1002\/dac.5147","article-title":"A range based node localization scheme with hybrid optimization for underwater wireless sensor network","volume":"35","author":"Nain","year":"2022","journal-title":"Int. J. Commun. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3578002","DOI":"10.1155\/2022\/3578002","article-title":"Energy-Efficient Markov-Based Lifetime Enhancement Approach for Underwater Acoustic Sensor Network","volume":"2022","author":"Sivakumar","year":"2022","journal-title":"J. Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101912","DOI":"10.1016\/j.adhoc.2019.101912","article-title":"Energy efficient multi-objective evolutionary routing scheme for reliable data gathering in Internet of underwater acoustic sensor networks","volume":"93","author":"Faheem","year":"2019","journal-title":"Ad Hoc Netw."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6837780","DOI":"10.1155\/2022\/6837780","article-title":"Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load","volume":"2022","author":"Mukhtar","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Saeed, M.M., Saeed, R.A., Abdelhaq, M., Alsaqour, R., Hasan, M.K., and Mokhtar, R.A. (2023). Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics, 12.","DOI":"10.3390\/electronics12153300"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Touafek, N., Tayeb, F.B.-S., and Ladj, A. (2023). A Reinforcing-Learning-Driven Artificial Bee Colony Algorithm for Scheduling Jobs and Flexible Maintenance under Learning and Deteriorating Effects. Algorithms, 16.","DOI":"10.3390\/a16090397"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fairee, S., Prom-On, S., and Sirinaovakul, B. (2018). Reinforcement learning for solution updating in Artificial Bee Colony. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0200738"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lu, Y., He, R., Chen, X., Lin, B., and Yu, C. (2020). Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks. Sensors, 20.","DOI":"10.3390\/s20041025"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chang, H., Feng, J., and Duan, C. (2019). Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility. Sensors, 19.","DOI":"10.3390\/s19020256"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102953","DOI":"10.1016\/j.adhoc.2022.102953","article-title":"Adaptive clustering routing protocol for underwater sensor networks","volume":"136","author":"Sun","year":"2022","journal-title":"Ad Hoc Netw."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"10810","DOI":"10.1038\/s41598-023-37952-x","article-title":"Underwater wireless sensor network-based multihop data transmission using hybrid cat cheetah optimization algorithm","volume":"13","author":"Vijay","year":"2023","journal-title":"Sci. Rep."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/11\/529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:13:38Z","timestamp":1760130818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/11\/529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,29]]},"references-count":65,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["systems11110529"],"URL":"https:\/\/doi.org\/10.3390\/systems11110529","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,29]]}}}