Concurrency and Computation-Practice & Experience最新文献

{"title":"A Verifiable and Efficient Multi-Keyword Fuzzy Rank Search Scheme Over Encrypted Data With Privacy-Preserving","authors":"Fengyi Gao,&nbsp;Na Wang,&nbsp;Jianwei Liu,&nbsp;Zhiquan Liu,&nbsp;Junsong Fu,&nbsp;Lunzhi Deng","doi":"10.1002/cpe.70149","DOIUrl":"https://doi.org/10.1002/cpe.70149","url":null,"abstract":"<div>\u0000 \u0000 <p>Searchable Encryption (SE) enables searching over encrypted data. Exact keyword search is supported in most SE schemes, which achieve higher search accuracy but suffer from lower completeness due to the inability to handle similar expressions. To realize fuzzy keyword search, some schemes employ Bloom Filters (BFs), but these may incur high false positive rates and risk exposing the Bloom Filter's internal values to cloud servers (CS). Besides, most existing schemes ignore the fact that CS may engage in malicious behaviors (e.g., undercounting parameters or forging results). To address these issues, we propose an efficient and verifiable ranked fuzzy multi-keyword search scheme based on BFs. We propose a Twin Bloom Filter (TBF) to conceal insertion positions and introduce random numbers to obfuscate uninserted bits. Search results are ranked using Term Frequency-Inverse Document Frequency (TF-IDF) scores to improve relevance. To ensure correctness and integrity, we employ Real Homomorphic Message Authentication Codes (RealHomMAC) and a random challenge technique, respectively. Security analysis proves that our scheme remains secure under both the known-ciphertext model and the known-background model. Theoretical and experimental performance analysis confirms that our scheme achieves efficient and accurate keyword search.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DSPCNN: Deep Spiking Parallel Convolutional Neural Network for Video Recommendation System","authors":"Andavarapu Sravani,&nbsp;G. Lavanya Devi","doi":"10.1002/cpe.70130","DOIUrl":"https://doi.org/10.1002/cpe.70130","url":null,"abstract":"<div>\u0000 \u0000 <p>The recommendation system infers the interest of users by collecting their behaviors and producing the recommendation list. A video recommender system aims to satisfy the requirements of users based on their interests. Many traditional video recommendation systems rely on the linear relationship between users and their neighbors, often overlooking higher-order relationships among users, which can negatively affect recommendation accuracy. Here, a Deep Spiking Parallel Convolutional Neural Network (DSPCNN) is developed for a video recommendation system. Initially, the user-video accumulation matrix is calculated. Thereafter, the video grouping is conducted employing the Fuzzy Local Information Cluster means (FLICM). Subsequently, bi-level matching between the user query and the video group is performed using squared-chord distance and Matusita distance, allowing for the retrieval of the user-preferred video. Finally, the video recommendation is performed using the proposed DSPCNN. Moreover, the merging of Spiking Deep Residual Network (S-ResNet) and Parallel Convolutional Neural Network (PCNN) creates the DSPCNN. Here, the video having maximum reach metrics is considered as the recommended one, which is newly developed based on click rate, number of likes, follows, comments, and forwards. Finally, the recommended video is provided to the users. It can be identified that DSPCNN has obtained an accuracy of 91%, a mean squared error (MSE) of 0.070, and a root mean squared error (RMSE) of 0.265. The source code is “https://github.com/SravaniAndavar/DSPCNN.git.”</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy Preserving Federated Reinforcement Imitation Learning Framework With Robust Aggregation for Cloud-Based Domestic Robots","authors":"Hema Priya Natarajan","doi":"10.1002/cpe.70153","DOIUrl":"https://doi.org/10.1002/cpe.70153","url":null,"abstract":"<div>\u0000 \u0000 <p>The evolution of cloud-based web services for the control and tracking of robotic devices has significantly transformed the field of robotics. Domestic robotic devices performing household activities are becoming increasingly popular, as they collect large volumes of data, transmit it to the cloud, and leverage web services for collaborative learning. These devices interconnect and learn from their peers over the cloud. However, this distributed and interconnected learning environment introduces a serious vulnerability to model poisoning attacks, where malicious participants can deliberately corrupt the learning process. These attacks are a direct result of Byzantine behavior, where certain participants act arbitrarily or adversarially, undermining the integrity of the global model. These attacks pose a critical threat to the reliability, safety, and privacy of robotic systems operating in real-world environments. To accelerate learning, peers connected through cloud-based services contribute data and updates, but this collaboration inevitably leads to the exposure of sensitive information, further escalating privacy concerns. To tackle these pressing issues, we propose a novel framework called Federated Reinforcement Imitation Learning (FRIL). The framework involves the design of the FRIL architecture, an in-depth analysis of threats in a distributed setting, and the development of a robust algorithm specifically designed to defend against model poisoning attacks. Experimental results demonstrate a high learning accuracy of 88 percent using the Edge IIoT dataset. The collaborative, decentralized, and privacy-preserving nature of the proposed framework, combined with imitation learning, makes it highly resilient against adversarial interference, ensuring the stability and integrity of the Federated Learning process in domestic robotic environments. This work directly targets the growing threat of model poisoning attacks and provides a concrete solution to secure collaborative learning in intelligent robotic systems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast-Optimizing and Adaptable Intrusion Detection System Based on Progressively Optimized Support Vector Machines","authors":"Hüseyin Güney","doi":"10.1002/cpe.70156","DOIUrl":"https://doi.org/10.1002/cpe.70156","url":null,"abstract":"<div>\u0000 \u0000 <p>Computer networking technologies play a crucial role in daily activities. However, they pose significant security challenges, and deploying cybersecurity systems to protect sensitive data is vital. A fast-optimizing and adaptable system can quickly learn new cyberattacks and adapt to ever-changing threats. Recent research has shown that the feature selection integrated classifier optimization algorithm (FSCOA) is promising for intrusion detection systems (IDS); however, its exhaustive search-based classifier optimization is time-consuming. To overcome this drawback, the present study proposes a new optimization framework, namely the Progressive Classifier Optimization Algorithm (PCOA), to enhance FSCOA in terms of time efficiency and develop fast-optimizing support vector machines (SVM). The proposed method was evaluated on five modern intrusion detection datasets. In addition, 15 intrusion detection datasets with various difficulty levels were extracted for model evaluation. For a realistic performance analysis, bias issues, the most critical metrics, and time complexity analyses were considered. The proposed algorithm led to the classification performance above 99% with below 1% false alarm rates of SVM for most datasets. Experimental results showed that PCOA's classification performance is comparable to FSCOA, with approximately five times less time complexity. PCOA-optimized SVM performs similarly to other methods from the literature, such as random forest and deep learning algorithms. In conclusion, this study proposes a fast-optimizing IDS that can be frequently updated to protect various networking setups from ever-changing cyber-attacks using limited-capacity computing devices. Additionally, essential insights into feature selection and classifier optimization for intrusion detection are provided in this study.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-Latency Tradeoffs for Service Placement Based on Reinforcement Learning in Edge Computing","authors":"Bing Tang,&nbsp;Haiyan Li,&nbsp;Wei Xu,&nbsp;Buqing Cao,&nbsp;Qing Yang","doi":"10.1002/cpe.70154","DOIUrl":"https://doi.org/10.1002/cpe.70154","url":null,"abstract":"<div>\u0000 \u0000 <p>Microservice technology, as a flexible application architecture, has gained wide popularity in the field of Internet of Things (IoT). IoT applications are highly sensitive to latency, making it crucial to place microservices on appropriate edge servers in an edge computing environment. Failure to do so can significantly impact service quality and degrade user experience, posing a major challenge. Addressing the aforementioned issues, this paper proposes a multiobjective service deployment strategy for IoT devices based on reinforcement learning. The goal is to minimize service access delay for IoT devices and reduce the average energy consumption of edge servers in the context of mobile edge computing. To achieve this, we first establish a stochastic optimization model using the Markov decision process (MDP) framework to handle service deployment and resource allocation dynamically. This model captures key characteristics such as heterogeneity in edge server capabilities, dynamic geographic information of IoT devices, and uncertainty in microservice requests. To overcome challenges related to dimensionality, slow convergence, and the exploration–exploitation tradeoff in traditional reinforcement learning algorithms, we introduce deep reinforcement learning into the optimization of microservice deployment. Specifically, we propose the use of deep deterministic policy gradient (DDPG) to obtain a near-optimal service deployment strategy without manual instructions. DDPG leverages the depth of the network to guide policy gradients and generate solutions that effectively balance exploration and exploitation. To evaluate the proposed approach, we implement the DPG-MSP (<span><b>DDPG</b></span>-based <span><b>M</b></span>icro<span><b>S</b></span>ervice <span><b>P</b></span>lacement) algorithm using real datasets and synthetic data. Comparative analysis with existing microservice deployment algorithms demonstrates the superiority of DDPG-MSP in terms of performance, robustness, and scalability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Strategy Fusion Binary Zebra Optimization Algorithm for Solving Complex Industrial Process Data Feature Selection and Prediction Models","authors":"Yi-Peng Shang-Guan,&nbsp;Jie-Sheng Wang,&nbsp;Yong-Cheng Sun,&nbsp;Yu-Wei Song,&nbsp;Yu-Liang Qi","doi":"10.1002/cpe.70143","DOIUrl":"https://doi.org/10.1002/cpe.70143","url":null,"abstract":"<div>\u0000 \u0000 <p>In the process of modern industrial production, soft sensing technology is often used to predict the target variables that are difficult to be directly measured by hard instruments. However, the input variables used for prediction are not all closely related to the output. Feature selection (FS) aims to select the features highly related to the target variables and discard the redundant features. How to select the optimal feature subset and reduce the operation cost while ensuring the prediction accuracy becomes a key problem. A multi-strategy fusion binary Zebra optimization algorithm (MFBZOA) was proposed to select the optimal feature subset in a prediction model. Firstly, the Cauchy inverse cumulative distribution function is used to mutate individual positions in the defense stage of the zebra optimization algorithm (ZOA), and then reproductive behavior is introduced into the algorithm to increase the diversity of the solution set and improve the overall quality of the solution. Finally, the Cauchy mutation strategy is introduced to disturb the worst individuals in the zebra population and increase the probability of jumping out of the local optimum. Firstly, the proposed improved ZOA is combined with the Zebra optimization algorithm, Golden sine algorithm, Whale optimization algorithm, Frilled lizard optimization, Human evolution optimization algorithm, Coatis optimization algorithm, and Goose optimization algorithm to perform CEC2022 function optimization simulation experiments to verify its effectiveness. Then, MFBZOA and the above comparison algorithms are used as search strategies respectively, combined with the wrapper FS method driven by a multi-layer perceptron to solve the FS problem of four industrial process data and build the corresponding prediction model. Then, the optimal feature subset selected by each algorithm is used in the prediction experiment. The simulation results show that MFBZOA can effectively select the optimal feature subset, improve the global search ability and local search ability, and maintain good prediction accuracy and generalization performance.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Access Control Model Against Attribute Guessing Attacks for Data Sharing in Cloud Computing Environment","authors":"Qikun Zhang,&nbsp;Jinbo Feng,&nbsp;Ruifang Wang,&nbsp;Yongjiao Li,&nbsp;Junling Yuan,&nbsp;Yu-an Tan","doi":"10.1002/cpe.70140","DOIUrl":"https://doi.org/10.1002/cpe.70140","url":null,"abstract":"<div>\u0000 \u0000 <p>Data sharing is a fundamental component that facilitates collaboration and interoperability among entities in cloud computing environments. It enables cross-domain access, concurrent task execution, and parallel multi-task processing. However, challenges such as privacy breaches, vulnerabilities of sensitive data, and inflexible access control mechanisms are prevalent in data access scenarios. Many existing attribute-based searchable encryption (ABSE) schemes suffer from issues like keyword leakage, limited query methods, and susceptibility to attribute guessing attacks. To address these challenges, this paper proposes an attribute-based access control scheme designed to mitigate keyword-guessing attacks in cloud environments. The proposed scheme has several advantages: (1) Enhanced Privacy Protection: By employing attribute-based encryption (ABE), the scheme ensures user personal information and ciphertext attribute values remain protected during authentication through hidden attribute authentication techniques. (2) Resistance to Keyword-Guessing Attacks: The scheme utilizes an anti-guessing attribute encryption algorithm, ensuring that attribute keywords and access policies remain secure against guessing attacks during transmission. (3) A flexible ciphertext attribute search and matching algorithm enhances access control security and supports fine-grained access control. This approach achieves precise, adaptable, and stealthy access control while strengthening privacy protection. It also accommodates diverse search requirements and ensures robust fine-grained access control. Security analysis confirms its strong security. Performance analysis shows that it outperforms existing schemes.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defect Detection of Prefabricated Building Components With Integrated YOLOv5s-GhostNet","authors":"Xuchao Liu,&nbsp;Jiayang Li","doi":"10.1002/cpe.70145","DOIUrl":"https://doi.org/10.1002/cpe.70145","url":null,"abstract":"<div>\u0000 \u0000 <p>The defect detection of prefabricated building components during production and use is the key to ensuring building quality. Traditional manual methods are inefficient and prone to missed detections, while existing deep learning models face challenges such as insufficient detection accuracy, high computational resource consumption, and poor real-time performance in complex backgrounds and small target scenes. To address this issue, this study uses YOLOv5 as the basic object detection algorithm, introduces an attention mechanism, replaces the loss function, and improves the Adam optimizer to obtain YOLOv5s. To reduce the size of the YOLOv5s, GhostNet is introduced as a feature extraction network to construct the YOLOv5s-GhostNet detection model for defect detection of prefabricated building components. In actual defect detection of building components, the YOLOv5s-GhostNet researched and designed only had 12 defect classification errors, and the classification accuracy was improved to 99.6%. The proposed model had an AUC area of 0.90 for defect detection in building components and a detection speed of 42FPS. In visual analysis, the confidence coefficient of the proposed model for detecting “crack” defects was 99%. YOLOv5s-GhostNet has extremely high accuracy and efficiency in detecting defects in prefabricated building components and has broad application prospects. In order to verify the performance of the YOLOv5s GhostNet model proposed in this article, traditional YOLOv5, VGG-16, ResNet, and HRNet were selected as benchmark models for comparison. The experimental results show that the proposed model outperforms these benchmark models in terms of detection accuracy, detection speed, and model complexity, further verifying its effectiveness and superiority in practical applications. The application of this model not only promotes the further development of deep learning technology in architecture but also provides a reference for defect detection in other fields.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GSR-C2N: Graph Feature Extracted Spar-Raven Optimized CNN Based Crypto Mining Framework","authors":"Mohd Anas Wajid,&nbsp;Shaharyar Alam Ansari,&nbsp;Mohammad Luqman,&nbsp;Mohammad Khubeb Siddiqui,&nbsp;Mohammad Saif Wajid","doi":"10.1002/cpe.70146","DOIUrl":"https://doi.org/10.1002/cpe.70146","url":null,"abstract":"<div>\u0000 \u0000 <p>The primary objective of this research is to increase the security of computer systems and networks by developing robust tools and techniques. The issue of crypto mining has become increasingly important in the field of cyber security due to the rapid increase in cryptocurrency usage. The main challenge in crypto mining relies on the extraction of the most relevant features and finding the optimal values. To concentrate more on these challenges, the Graph Feature Extracted Spar-Raven Optimized Convolutional Neural Network based Crypto mining framework (GSR-C2N) is proposed that enables the prompt detection and effective mitigation of crypto mining. By doing so, the research aims to address the potential adverse impacts caused, including performance slowdowns, heightened energy usage, and financial losses incurred by both individuals and organizations. The transaction of each block is monitored and controlled by the Block transaction information controller that safeguards security and accuracy. Specifically, the Spar-Raven optimization hybridizes the unique characteristics, including the memory and intelligence characteristics of the Raven with the Spar's keen awareness of predators, to find the global best solution and adaptively fine-tune the hyper-parameters of the GSR-C2N classifier. The performance of the model is analyzed using the crypto-mining-malware dataset, where the accuracy, sensitivity, and specificity of the proposed GSR-C2N were 96.848%, 96.388%, and 97.505% for K-Fold 10, and achieved 96.413%, 96.388%, and 96.633% for Training percentage 80%. Moreover, the proposed approach exhibits better performance and offers rapid processing speed, scalability, adaptability, and seamless deployment across diverse networks, making the GSR-C2N model efficient for performing in a real-time environment.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HDAMM: Hierarchical Geographical Data Aggregation Method Using Mobile Sink in Wireless Sensor Networks","authors":"Maryam Naghibi,&nbsp;Hamid Barati,&nbsp;Ali Barati","doi":"10.1002/cpe.70115","DOIUrl":"https://doi.org/10.1002/cpe.70115","url":null,"abstract":"<div>\u0000 \u0000 <p>In wireless sensor networks (WSNs), nodes typically operate with limited energy supplies, making efficient data gathering essential for prolonging network lifespan. One effective approach to reduce energy consumption is clustering. However, using a fixed sink to collect data can lead to energy depletion in specific nodes, causing bottlenecks. A mobile sink, on the other hand, can address this issue by enhancing network performance and reducing energy load on individual nodes. This paper introduces a hierarchical cluster-based data aggregation method that employs fuzzy logic alongside a mobile sink to improve energy efficiency. The strategy has two main stages: clustering and data aggregation. In the clustering stage, the process is split into two steps: identifying cluster heads and organizing clusters. A fuzzy inference system assesses each node's potential as a cluster head based on factors such as remaining energy, node connectivity, and centrality. The nodes with the highest scores are selected as primary cluster heads, while those with slightly lower scores serve as backup cluster heads. Clusters are then formed around these chosen heads. In the data aggregation phase, cluster heads gather data from cluster members and forward it either to a mobile sink or directly to the base station (BS). Cluster heads located within a specified range (distance ≤ <i>r</i>) of the BS send data directly, while others route data via the mobile sink. This technique enhances data transmission efficiency and optimizes energy consumption, contributing to overall network improvement. The HDAMM approach demonstrated considerable advancements over earlier methods in terms of energy efficiency, delay reduction, packet delivery rate, and network longevity.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}