Journal of Network and Computer Applications最新文献

{"title":"Enhanced energy efficiency in UAV-assisted Mobile Edge Computing through improved hybrid nature-inspired algorithm for task offloading","authors":"Hengyu Li,&nbsp;Hongjian Li","doi":"10.1016/j.jnca.2025.104290","DOIUrl":"10.1016/j.jnca.2025.104290","url":null,"abstract":"<div><div>With the rise of Mobile Edge Computing (MEC) and the increasing number of User Equipments (UE), traditional MEC systems struggle with high UE density. UAVs can assist in offloading tasks from base stations, but their limited resources make deployment and offloading strategies critical. This paper investigates UAV deployment strategies and task offloading policies in scenarios where UE density varies over time. First, we introduce the Maximum Clique in Weighted Graph (MCWG) algorithm, which is designed to calculate the UAV deployment coordinates within a weighted graph. In the task offloading phase, considering the resource constraints of UAVs, we develop a collaborative computation offloading framework involving UE, UAVs, MEC, and cloud servers. Secondly, we propose an Improved Hybrid Nature-Inspired Optimization (IHNIO) algorithm under delay and energy consumption constraints. This algorithm aims to minimize the average delay and energy consumption of UEs in UAV-assisted Mobile Edge Computing. Simulation results show that our approach significantly enhances energy efficiency compared to baseline solutions, with potential improvements in efficiency reaching up to 11%.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104290"},"PeriodicalIF":8.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-efficient IoT-based tracking and management system for patients with cognitive diseases","authors":"Radwa Ahmed Osman","doi":"10.1016/j.jnca.2025.104289","DOIUrl":"10.1016/j.jnca.2025.104289","url":null,"abstract":"<div><div>The proliferation of Internet of Things (IoT) technologies has created new opportunities for healthcare applications, particularly when it comes to tracking and assisting people with cognitive diseases like autism and Alzheimer’s. In order to meet the unique requirements of individuals with Alzheimer’s and autism, this article suggests a novel energy efficient IoT tracking model that ensures ongoing accurate and reliable monitoring. To develop a complete tracking system, the suggested model combines machine learning algorithms with energy-efficient sensors. Optimizing energy efficiency receives special attention because continuous patient monitoring depends on the device operation under different environmental conditions. This methodology optimizes the energy efficiency of the system using a 1-Deep Convolutional Neural Network (DCNN) and Lagrange optimization algorithms. The primary objective is to ascertain the ideal distance required for sending emergency signal for patients’ wearable IoT devices to travel in the event that their medical problems cause them to become misplaced. The proposed method aims to enhance the overall performance of the communication system by integrating mathematical optimization principles with modern deep learning techniques. This will contribute to the development of more dependable and efficient emergency response mechanisms. This research proposes an energy-efficient IoT tracking methodology that makes a significant contribution to the healthcare technology space. Through addressing the particular difficulties that patients with Autism and Alzheimer’s disease present, the model provides a viable way to improve care, encourage independence, and lessen the workload for carers in an energy-efficient way.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104289"},"PeriodicalIF":8.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-efficient performance optimization in Kubernetes microservices using Generalized Stochastic Petri Net","authors":"Iure Fé ,&nbsp;Tuan Anh Nguyen ,&nbsp;Eunmi Choi ,&nbsp;Dugki Min ,&nbsp;Jae-Woo Lee ,&nbsp;Vandirleya Barbosa ,&nbsp;André Soares ,&nbsp;Paulo A.L. Rego ,&nbsp;Alessandro Mei ,&nbsp;Francisco Airton Silva","doi":"10.1016/j.jnca.2025.104287","DOIUrl":"10.1016/j.jnca.2025.104287","url":null,"abstract":"<div><div>The advantages of microservices system architectures orchestrated by Kubernetes include their capacity to adjust dynamically to meet varying demand, thereby ensuring application performance requirements during high load periods while reducing electrical consumption during low demand periods. However, the proper configuration of autoscaling in microservices systems presents significant challenges due to the myriad of parameters involved, with optimal choices highly dependent on both the application and the underlying infrastructure. Balancing system performance with energy efficiency is inherently difficult, as enhancements in one area often lead to detriments in the other. In this article, a model is presented to aid in the planning of performance and electrical consumption for microservices architectures orchestrated by Kubernetes, utilizing Pod and cluster autoscaling. The approach is based on the application of Generalized Stochastic Petri Net models. This methodology enabled the creation of a comprehensive model that incorporates various Kubernetes autoscaling configuration elements, the performance characteristics of individual microservices, and the capacity of the underlying infrastructure. The proposed model is capable of computing key metrics, including response time, throughput, discard probability, electrical consumption, average consumption per request, and the Energy-Response time Weighted Product. Sensitivity analysis was employed to identify the elements exerting the greatest impact on the performance and electrical consumption of the architecture, thereby guiding parameter adjustment efforts. It was also determined that the optimal configuration choices are contingent on the expected workload. The results indicate that using configurations with higher autoscaling thresholds during low workloads can reduce electrical consumption by approximately 32% without significantly degrading performance. Conversely, in high arrival rate scenarios, this autoscaling configuration results in a 37% reduction in consumption but at the cost of a 175% increase in response time.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104287"},"PeriodicalIF":8.0,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Joint container orchestrating and request routing for serverless edge computing-based simulation applications","authors":"Yong Peng ,&nbsp;Miao Zhang ,&nbsp;Zhi Zhou ,&nbsp;Hao Huang","doi":"10.1016/j.jnca.2025.104284","DOIUrl":"10.1016/j.jnca.2025.104284","url":null,"abstract":"<div><div>Serverless edge computing dynamically invokes functions based on events, enabling on-demand code execution at the network edge and minimizing infrastructure management overhead. This computing paradigm is naturally suitable for event-driven distributed simulation applications, which involves frequent event interactions and stringent latency constraints. When running on top of geographically dispersed edge clouds, container orchestration and request routing have a significant impact on the performance of serverless edge computing-based simulations. In this paper, we propose an online orchestration framework for cross-edge serverless computing-based-simulations, which aims to minimize the resource cost and carbon emission under performance (i.e., latency) constraint, via jointly optimizing the container retention and requesting routing on-the-fly. This long-term cost minimization problem is difficult since it is NP-hard and involves future uncertain information. To simultaneously address these dual challenges, we carefully combine an online optimization technique with an approximate optimization method in a joint optimization framework. This framework first temporally decomposes the long-term time-coupling problem into a series of one-shot fractional problem via Lyapunov optimization, and then applies randomized dependent scheme to round the fractional solution to a near-optimal integral solution. The resulting online algorithm achieves an outstanding performance, as verified by extensive trace-driven simulations.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104284"},"PeriodicalIF":8.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"OpenDriver: An open-road driver state detection benchmark","authors":"Delong Liu ,&nbsp;Shichao Li ,&nbsp;Tianyi Shi ,&nbsp;Zhu Meng ,&nbsp;Guanyu Chen ,&nbsp;Zhicheng Zhao","doi":"10.1016/j.jnca.2025.104279","DOIUrl":"10.1016/j.jnca.2025.104279","url":null,"abstract":"<div><div>Wearable physiological measurements offer a convenient and feasible method for real-time driver states monitoring. However, there are currently few driver physiological datasets in open-road scenarios, and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. In this paper, a large-scale multi-modal driving benchmark namely OpenDriver is elaborately constructed for driver state detection. Firstly, the OpenDriver encompasses 3278 driving trips, with a signal duration of approximately 4600 h. Two modalities of driving signals are collected: electrocardiogram (ECG) signals and six-axis motion data of the steering wheel from a motion measurement unit (IMU), which are recorded from 81 bus drivers and their vehicles. Secondly, three challenging tasks are carefully designed, and they are ECG signal quality assessment, individual biometric identification based on ECG signals, and physiological signal analysis in complex driving environments, respectively. Moreover, the corresponding baseline models and evaluation metrics are proposed to demonstrate the rationality and completeness of the dataset and tasks. First, in the quality assessment task, a noisy augmentation strategy is introduced to achieve realistic noise simulation, and then a larger-scale ECG signal dataset is generated. Second, an end-to-end contrastive learning framework is employed to effectively identify individual biometric. Finally, a comprehensive analysis of drivers’ Heart Rate Variability (HRV) features under different driving conditions gives multiple heuristic analytical conclusions. The OpenDriver benchmark and dataset will be publicly available at <span><span>https://github.com/bdne/OpenDriver</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"242 ","pages":"Article 104279"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks","authors":"Sushma S.A. ,&nbsp;Madhunisha E. ,&nbsp;Sourav Kanti Addya ,&nbsp;Saifur Rahman ,&nbsp;Shantanu Pal ,&nbsp;Chandan Karmakar","doi":"10.1016/j.jnca.2025.104278","DOIUrl":"10.1016/j.jnca.2025.104278","url":null,"abstract":"<div><div>Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"242 ","pages":"Article 104278"},"PeriodicalIF":7.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"NTP-INT: Network traffic prediction-driven in-band network telemetry for high-load switches","authors":"Penghui Zhang ,&nbsp;Hua Zhang ,&nbsp;Yuqi Dai ,&nbsp;Cheng Zeng ,&nbsp;Jingyu Wang ,&nbsp;Jianxin Liao","doi":"10.1016/j.jnca.2025.104265","DOIUrl":"10.1016/j.jnca.2025.104265","url":null,"abstract":"<div><div>Due to its real-time visibility, In-band network telemetry (INT) is of great significance for network management. Nevertheless, with the rapid growth of network devices and services, targeted access to detailed network information in dynamic environments has become increasingly essential. This paper proposes an intelligent network telemetry system called NTP-INT to obtain more fine-grained network information on high-load switches. Specifically, NTP-INT consists of three modules: the network traffic prediction module, the topology pruning module, and the probe path planning module. Firstly, the network traffic prediction module adopts a Multi-Temporal Graph Neural Network (MTGNN) to predict future network traffic and identify high-load switches. Then, we design the topology pruning algorithm to generate a subnetwork covering all high-load switches to reduce the complexity of probe path planning. Finally, the probe path planning module uses an attention-mechanism-based Deep Reinforcement Learning (DRL) model to plan efficient probe paths in the subnetwork. Experimental results demonstrate that NTP-INT achieves more accurate telemetry on high-load switches while reducing control overhead by 50%. Additionally, the topology pruning strategy shortens training time by over 40%.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"242 ","pages":"Article 104265"},"PeriodicalIF":7.7,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Intelligent congestion control in 5G URLLC Software-Defined Networks using adaptive resource management via Reinforced Dueling Deep Q-Networks","authors":"Vitawat Sittakul ,&nbsp;Iacovos Ioannou ,&nbsp;Prabagarane Nagaradjane ,&nbsp;Vasos Vassiliou","doi":"10.1016/j.jnca.2025.104276","DOIUrl":"10.1016/j.jnca.2025.104276","url":null,"abstract":"<div><div>Centralized control of Software Defined Networking (SDN) yields efficient management of network resources and offers a global perspective. However, centralized controllers have many performance and scalability issues, particularly given the rapid expansion of 5G connectivity. The latest demands on the transport network come from areas such as increasing RAN and mobile broadband service capacity, new 5G-enabled services and the dynamic deployment flexibility of the 5G Radio Access Network (RAN) split architecture, with its tight transport characteristics. These characteristics are particularly evident in the fronthaul segment of RAN, where latency and synchronization requirements pose significant challenges. Enhanced automation capabilities in the operations and management domain represent a key requirement to meet these challenges. Traditional machine learning (ML) techniques, which concentrate the training data and carry out sequential model learning over a sizable data set, are the main emphasis of current wireless network learning approaches. However, using a huge dataset for training is inefficient since it takes a lot of time and does not use resources or energy efficiently. Hence, this work focuses on Reinforced Dueling Deep Q-Network (RDDQN), a revolutionary approach to network slicing design for load prediction and resource management in data-driven workflows. Moreover, it can reduce congestion by adopting an Ultimatum queuing game theory-based scheduling mechanism in the controller. The proposed RDDQN achieves an average throughput of 579.34 kbps, an execution time of 12.57 s, goodput fairness of 94.56%, and delay fairness of 10.37 s across various parameters.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"242 ","pages":"Article 104276"},"PeriodicalIF":7.7,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"LASeR: Lightweight and secure remote user authentication protocol for Internet of Drones","authors":"Ilyes Ahmim ,&nbsp;Feriel Bouakkaz ,&nbsp;Abderrezak Rachedi ,&nbsp;Nassira Ghoualmi-Zine","doi":"10.1016/j.jnca.2025.104275","DOIUrl":"10.1016/j.jnca.2025.104275","url":null,"abstract":"<div><div>The Internet of Drones has emerged as a new paradigm in academia and industry due to its clear advantages in multiple domains, including the military, smart cities, smart agriculture, and more recently, during the COVID-19 pandemic. Remote users are increasingly eager to access real-time information collected by drones in specific areas. However, the wireless communication used for information exchange between remote users and drones is vulnerable to various security challenges due to its open nature. Moreover, drones are constrained by limited energy and resources, which hinders the effective use of traditional cryptographic methods that involve high computational and communication costs. To address these challenges in IoD (Internet of Drones) environments, we propose a novel lightweight authenticated key agreement protocol, named LASeR, which ensures secure exchanges between users and drones. LASeR relies exclusively on Elliptic Curve Cryptography (ECC), bit-wise XOR operation, and one-way hash functions, thereby achieving a lightweight design. Our protocol not only verifies the authenticity of the user but also establishes a session key between the user and drone for encrypted communications. Security evaluations demonstrate that LASeR is resilient against various security attacks and meets essential security requirements, notably forward and backward secrecy. Furthermore, we show that LASeR imposes significantly lower computational and communication costs compared to other relevant protocols.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"242 ","pages":"Article 104275"},"PeriodicalIF":7.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Network traffic feature representation with contrastive learning for traffic engineering in hybrid software defined networks","authors":"Weihong Zhou ,&nbsp;Ruiyu Yang ,&nbsp;Yingya Guo ,&nbsp;Huan Luo","doi":"10.1016/j.jnca.2025.104270","DOIUrl":"10.1016/j.jnca.2025.104270","url":null,"abstract":"<div><div>Traffic Engineering (TE) promotes the performance of hybrid Software Defined Networks (hybrid SDN) through optimizing traffic route selection. To handle dynamic network traffic, existing machine learning-based TE methods in hybrid SDNs focus on leveraging Reinforcement Learning (RL) to learn the mapping between the dynamic traffic demands and the traffic splitting ratios. However, with the huge network state space incurred by the dynamic network traffic and increasing network scale, it is hard for the RL-agent to learn and converge to the optimal mapping between traffic demands and traffic splitting ratios, thus the network performance suffers a degradation in dynamic network environment. To tackle this issue, we innovatively propose a TE approach that combines Contrastive learning (CL) and RL. Specifically, to reduce huge state space, we design to learn the mapping between network traffic features and routing policy rather than learning the mapping between traffic demand and routing policy. To well capture the features of traffic demands, we leverage CL to train a feature encoder for representing network traffic. We conduct extensive experiments on real network topologies datasets and the experimental results demonstrate that our proposed algorithm provides significant network performance improvements over state-of-arts.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"242 ","pages":"Article 104270"},"PeriodicalIF":7.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}