Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"A formal specification language and automatic modeling method of asset securitization contract","authors":"Yang Li ,&nbsp;Kai Hu ,&nbsp;Jie Li ,&nbsp;Kaixiang Lu ,&nbsp;Yuan Ai","doi":"10.1016/j.jksuci.2024.102163","DOIUrl":"10.1016/j.jksuci.2024.102163","url":null,"abstract":"<div><p>Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization – Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102163"},"PeriodicalIF":5.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002520/pdfft?md5=9af49e4b57c4f2d8d674b3287497b478&pid=1-s2.0-S1319157824002520-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs","authors":"Runyu Zhang,&nbsp;Lening Zhou,&nbsp;Mingjie Li,&nbsp;Yunlin Tan,&nbsp;Chaoshu Yang","doi":"10.1016/j.jksuci.2024.102156","DOIUrl":"10.1016/j.jksuci.2024.102156","url":null,"abstract":"<div><p>Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102156"},"PeriodicalIF":5.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002453/pdfft?md5=b3f5e8288e8205e799d78965f416b571&pid=1-s2.0-S1319157824002453-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LDNet: High Accuracy Fish Counting Framework using Limited training samples with Density map generation Network","authors":"Ximing Li ,&nbsp;Yitao Zhuang ,&nbsp;Baihao You ,&nbsp;Zhe Wang ,&nbsp;Jiangsan Zhao ,&nbsp;Yuefang Gao ,&nbsp;Deqin Xiao","doi":"10.1016/j.jksuci.2024.102143","DOIUrl":"10.1016/j.jksuci.2024.102143","url":null,"abstract":"<div><p>Fish counting is crucial in fish farming. Density map-based fish counting methods hold promise for fish counting in high-density scenarios; however, they suffer from ineffective ground truth density map generation. High labeling complexities and disturbance to fish growth during data collection are also challenging to mitigate. To address these issues, LDNet, a versatile network with attention implemented is introduced in this study. An imbalanced Optimal Transport (OT)-based loss function was used to effectively supervise density map generation. Additionally, an Image Manipulation-Based Data Augmentation (IMBDA) strategy was applied to simulate training data from diverse scenarios in fixed viewpoints in order to build a model that is robust to different environmental changes. Leveraging a limited number of training samples, our approach achieved notable performances with an 8.27 MAE, 9.97 RMSE, and 99.01% Accuracy on our self-curated Fish Count-824 dataset. Impressively, our method also demonstrated superior counting performances on both vehicle count datasets CARPK and PURPK+, and Penaeus_1k Penaeus Larvae dataset when only 5%–10% of the training data was used. These outcomes compellingly showcased our proposed approach with a wide applicability potential across various cases. This innovative approach can potentially contribute to aquaculture management and ecological preservation through counting fish accurately.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102143"},"PeriodicalIF":5.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002325/pdfft?md5=ec92694818fa8a8041843f53d8c6b66e&pid=1-s2.0-S1319157824002325-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging syntax-aware models and triaffine interactions for nominal compound chain extraction","authors":"Yinxia Lou ,&nbsp;Xun Zhu ,&nbsp;Ming Chen ,&nbsp;Donghong Ji ,&nbsp;Junxiang Zhou","doi":"10.1016/j.jksuci.2024.102153","DOIUrl":"10.1016/j.jksuci.2024.102153","url":null,"abstract":"<div><p>Recently, Nominal Compound Chain Extraction (NCCE) has been proposed to detect related mentions in a document to improve understanding of the document’s topic. NCCE involves longer span detection and more complicated rules for relation decisions, making it more difficult than previous chain extraction tasks, such as coreference resolution. Current methods achieve certain progress on the NCCE task, but they suffer from insufficient syntax information utilization and incomplete mention relation mining, which are helpful for NCCE. To fill these gaps, we propose a syntax-guided model using a triaffine interaction to improve the performance of the NCCE task. Instead of solely relying on the text information to detect compound mentions, we also utilize the noun-phrase (NP) boundary information in constituency trees to incorporate prior boundary knowledge. In addition, we use biaffine and triaffine operations to mine the mention interactions in the local and global context of a document. To show the effectiveness of our methods, we conduct a series of experiments on a human-annotated NCCE dataset. Experimental results show that our model significantly outperforms the baseline systems. Moreover, in-depth analyses reveal the effect of utilizing syntactic information and mention interactions in the local and global contexts.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102153"},"PeriodicalIF":5.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002428/pdfft?md5=68d28a739630245dadca6d14bfb1c2d3&pid=1-s2.0-S1319157824002428-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous network link prediction based on network schema and cross-neighborhood attention","authors":"Pengtao Wang ,&nbsp;Jian Shu ,&nbsp;Linlan Liu","doi":"10.1016/j.jksuci.2024.102154","DOIUrl":"10.1016/j.jksuci.2024.102154","url":null,"abstract":"<div><p>Heterogeneous network link prediction is a hot topic in the analysis of networks. It aims to predict missing links in the network by utilizing the rich semantic information present in the heterogeneous network, thereby enhancing the effectiveness of relevant data mining tasks. Existing heterogeneous network link prediction methods utilize meta-paths or meta-graphs to extract semantic information, heavily relying on the priori knowledge. This paper proposes a heterogeneous network link prediction based on network schema and cross-neighborhood attention method (HNLP-NSCA). The heterogeneous node features are projected into a shared latent vector space using fully connected layers. To resolve the issue of prior knowledge dependence on meta-path, the semantic information is extracted by using network schema structures uniquely in heterogeneous networks. Node features are extracted based on the relevant network schema instances, avoiding the problem of meta-path selection. The neighborhood interaction information of input node pairs is sensed via cross-neighborhood attention, strengthening the nonlinear mapping capability of the link prediction. The resulting cross-neighborhood interaction vectors are combined with the node feature vectors and fed into a multilayer perceptron for link prediction. Experimental results on four real-world datasets demonstrate that the proposed HNLP-NSCA mothed outperforms the baseline models.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102154"},"PeriodicalIF":5.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S131915782400243X/pdfft?md5=269ef08ce93e8cf6ae0df3df90173eac&pid=1-s2.0-S131915782400243X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial relaxation transformer for image super-resolution","authors":"Yinghua Li ,&nbsp;Ying Zhang ,&nbsp;Hao Zeng ,&nbsp;Jinglu He ,&nbsp;Jie Guo","doi":"10.1016/j.jksuci.2024.102150","DOIUrl":"10.1016/j.jksuci.2024.102150","url":null,"abstract":"<div><p>Transformer-based approaches have demonstrated remarkable performance in image processing tasks due to their ability to model long-range dependencies. Current mainstream Transformer-based methods typically confine self-attention computation within windows to reduce computational burden. However, this constraint may lead to grid artifacts in the reconstructed images due to insufficient cross-window information exchange, particularly in image super-resolution tasks. To address this issue, we propose the Multi-Scale Texture Complementation Block based on Spatial Relaxation Transformer (MSRT), which leverages features at multiple scales and augments information exchange through cross windows attention computation. In addition, we introduce a loss function based on the prior of texture smoothness transformation, which utilizes the continuity of textures between patches to constrain the generation of more coherent texture information in the reconstructed images. Specifically, we employ learnable compressive sensing technology to extract shallow features from images, preserving image features while reducing feature dimensions and improving computational efficiency. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches in both qualitative and quantitative evaluations.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102150"},"PeriodicalIF":5.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002398/pdfft?md5=0a1496797663e5c523b9ebe20a3e23aa&pid=1-s2.0-S1319157824002398-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality","authors":"Haidong Wang,&nbsp;Huicheng Lai,&nbsp;Haohao Du,&nbsp;Guxue Gao","doi":"10.1016/j.jksuci.2024.102146","DOIUrl":"10.1016/j.jksuci.2024.102146","url":null,"abstract":"<div><p>Due to its asymptotic optimality, the Rapidly-exploring Random Tree star (RRT*) algorithm is widely used for robotic operations in complex environments. However, the RRT* algorithm suffers from poor path quality, slow convergence, and unstable generation of high-quality paths in the path planning process. This paper proposes an Improved Bi-Tree Obstacle Edge Search Artificial Potential Field RRT* algorithm (IBPF-RRT*) to address these issues. First, based on the RRT* algorithm, this paper proposes a new obstacle edge search artificial potential field strategy (ESAPF), which speeds up the path search and improves the path quality simultaneously. Second, a bi-directional pruning strategy is designed to optimize the bi-directional search tree branch nodes and combine the bi-directional search strategy to reduce the number of iterations for convergence speed significantly. Third, a novel path optimization strategy is proposed, which enables high-quality paths to be generated stably by creating an entirely new node between two path nodes and then optimizing the paths using a pruning strategy based on triangular inequalities. Experimental results in three different scenarios show that the proposed IBPF-RRT* algorithm outperforms other methods in terms of optimal path quality, algorithm stability, and the number of iterations when compared to the RRT*, Q-RRT*, PQ-RRT*, F-RRT* and CCPF-RRT* algorithms, and proves the effectiveness of the proposed three strategies.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102146"},"PeriodicalIF":5.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002350/pdfft?md5=5a50e8f318b478ea8f87375c2c517352&pid=1-s2.0-S1319157824002350-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lumbar intervertebral disc detection and classification with novel deep learning models","authors":"Der Sheng Tan ,&nbsp;Humaira Nisar ,&nbsp;Kim Ho Yeap ,&nbsp;Veerendra Dakulagi ,&nbsp;Muhammad Amin","doi":"10.1016/j.jksuci.2024.102148","DOIUrl":"10.1016/j.jksuci.2024.102148","url":null,"abstract":"<div><p>Low back pain (LBP) is a prevalent spinal issue, affecting eight out of ten individuals. Notably, lumbar intervertebral disc (IVD) abnormalities frequently contribute to LBP. To diagnose LBP, Magnetic Resonance Imaging (MRI) is crucial for obtaining detailed spinal images. This paper employs deep learning (DL) to detect and locate lumbar IVD in sagittal MR images. It further classifies lumbar IVDs as healthy or herniated, utilizing both novel convolutional neural network (CNN) and conventional CNN models. The dataset utilized comprises MR images from 32 patients, with 10 exhibiting healthy discs and the remaining 22 posing a mix of healthy and herniated discs, totaling 160 lumbar discs, incorporating 112 healthy and 48 herniated discs. In this study, ResNet-50 architecture in the Novel Lumbar IVD detection (NLID) model served as the feature extractor to segment the five lumbar IVDs from MR images. The features extracted from ResNet-50 were input into YOLOv2 for the identification of the region of interest (ROI). The findings indicate that optimal performance was achieved at the 22nd Rectified Linear Unit (ReLU) activation layer, boasting a remarkable 99.59% average precision, 97.22% F1-score, 94.59% precision, and a perfect 100% recall. This commendable performance consistently held above the 85% threshold until the 22nd ReLU activation layer. Regarding imbalanced dataset classification, AlexNet emerged as the frontrunner among other pre-trained networks, boasting the highest test accuracy of 90.63%, and an impressive F1 score of 88.77%. Meanwhile, the Novel Lumbar IVD Classification (NLIC) model achieved superior results with 93.75% test accuracy, and 92.27% F1-score. In the setting of the balanced dataset, NLIC achieved 96.88% test accuracy, and 96.46% F1-score with fewer epochs compared to AlexNet, affirming the robustness of the novel trained-from-scratch network. These findings distinctly underscore the effectiveness of CNNs in both medical image segmentation and classification.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102148"},"PeriodicalIF":5.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002374/pdfft?md5=242b16e1864b249a5d6a3f20dfd70a71&pid=1-s2.0-S1319157824002374-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141961047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Planning the development of text-to-speech synthesis models and datasets with dynamic deep learning","authors":"Hawraz A. Ahmad ,&nbsp;Tarik A. Rashid","doi":"10.1016/j.jksuci.2024.102131","DOIUrl":"10.1016/j.jksuci.2024.102131","url":null,"abstract":"<div><p>Synthesis of Text-to-speech (TTS) is a process that involves translating a natural language text into a speech. Speech synthesisers face a major challenge when recognizing the prosodic elements of written text, such as intonation (the rise and fall of the voice in speaking), and length. In contrast, continuous speech features are influenced by the personality and emotions of the artist. A database is maintained to store the synthesized speech pieces. Its output is determined by how similar the person utters the words and how capable they are of being implied. In the past few years, the field of text-to-speech synthesis has been heavily impacted by the emergence of deep learning, an AI technology that has gained widespread popularity. This review paper presents a taxonomy of models and architectures that are based on deep learning and discusses the various datasets that are utilised in the TTS process. It also covers the evaluation matrices that are commonly used. The paper ends with a look at the future directions of the system and reaches to some Deep learning models that give promising results in this field.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102131"},"PeriodicalIF":5.2,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002209/pdfft?md5=73c94f11cbc25ec7eb6841c1af93654a&pid=1-s2.0-S1319157824002209-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient routing protocols for UWSNs: A comprehensive review of taxonomy, challenges, opportunities, future research directions, and machine learning perspectives","authors":"Sajid Ullah Khan ,&nbsp;Zahid Ulalh Khan ,&nbsp;Mohammed Alkhowaiter ,&nbsp;Javed Khan ,&nbsp;Shahid Ullah","doi":"10.1016/j.jksuci.2024.102128","DOIUrl":"10.1016/j.jksuci.2024.102128","url":null,"abstract":"<div><p>Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different and more complex challenges than terrestrial wireless sensor networks (TWSNs). The main challenges faced by UWSNs are limited include high propagation delays, poor bandwidth, low throughput, and high energy consumption. Replacing sensor batteries in such networks becomes extremely difficult as they are usually deployed in remote areas where limited human interaction is possible. The unbalanced and inefficient usage of energy by various network nodes poses another issue, as it may reduce the applicability and feasibility of the network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) is crucial to improve the performance and lifespan of these networks. Due to the challenges mentioned earlier, this research presents an extensive analysis of several different E-ER-Ps intended for UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, as ML-based approaches have shown significant potential in resolving the intricate challenges faced by UWSNs. This paper aims to present a critical review of different E-ER-Ps from various prospects for UWSNs. To better comprehend the structure and uses of these protocols, we provide an innovative taxonomy for their classification. While ML-based protocols are evaluated for their flexibility, predictive power, and overall efficiency advancements, traditional protocols are evaluated based on their routing tactics and energy-efficiency improvements. A thorough comparative analysis highlights the advantages, disadvantages, and possible uses for different protocols. Furthermore, a critical analysis of ML’s function, incorporating intelligent and adaptive routing approaches, is presented, highlighting the technology’s potential to completely alter UWSN management. To formulate and implement E-ER-Ps for UWSNs, the article concludes by highlighting the present obstacles, including the need for real-time flexibility, resilience to environmental alters, and interaction with pre-existing network infrastructures. The development of ML-based approaches, hybrid approaches that combine conventional and ML-based methodologies, and the design of protocols that can adapt dynamically to the changing circumstances of underwater habitats are highlighted as future research objectives. This research provides the foundation for future advancements in this crucial field by presenting a comprehensive overview of the state-of-the-art UWSN E-ER-Ps.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102128"},"PeriodicalIF":5.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002179/pdfft?md5=0ca24269fca8e21ff16074a33686ceaa&pid=1-s2.0-S1319157824002179-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}