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Network quality prediction in a designated area using GPS data J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-18 Onur Sahin, Vanlin Sathya
This study introduces a groundbreaking method for predicting network quality in LTE and 5G environments using only GPS data, focusing on pinpointing specific locations within a designated area to determine network quality as either good or poor. By leveraging machine learning algorithms, we have successfully demonstrated that geographical location can be a key indicator of network performance. Our
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Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-14 Yilin Li, Peiying Zhang, Neeraj Kumar, Mohsen Guizani, Jian Wang, Konstantin Igorevich Kostromitin, Yi Wang, Lizhuang Tan
With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service
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CESA: Communication efficient secure aggregation scheme via sparse graph in federated learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-14 Ruijin Wang, Jinbo Wang, Xiong Li, Jinshan Lai, Fengli Zhang, Xikai Pei, Muhammad Khurram Khan
As a distributed learning paradigm, federated learning can be effectively applied to the decentralized system since it can resolve the “data island” problem. However, it is also vulnerable to serious privacy breaches. Although existing secure aggregation technique can address privacy concerns, they also incur significant additional computation and communication costs. To address these challenges, this
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A blockchain transaction mechanism in the delay tolerant network J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-14 Lingling Zi, Xin Cong
Current blockchain systems have high requirements on network connection and data transmission rate, for example, nodes have to receive the latest blocks in time to update the blockchain, nodes have to immediately broadcast the generated block to other nodes for consensus, which restricts the blockchain to run only on real-time connection networks, but the existence of delay tolerant networks poses
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A survey on security issues in IoT operating systems J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-06 Panjun Sun, Yi Wan, Zongda Wu, Zhaoxi Fang
The security issues of the core (operating systems) of the Internet of Things (IoT) are becoming increasingly urgent and prominent, this article conducts a systematic research and summary of the security of the current mainstream IoT operating system. Firstly, based on the architecture and applications functions of IoT devices, this article introduces the concept of operating system security, analyzes
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Privacy-preserving federated learning for proactive maintenance of IoT-empowered multi-location smart city facilities J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-05 Zu-Sheng Tan, Eric W.K. See-To, Kwan-Yeung Lee, Hong-Ning Dai, Man-Leung Wong
The widespread adoption of the Internet of Things (IoT) and deep learning (DL) have facilitated a social paradigm shift towards smart cities, accelerating the rapid construction of smart facilities. However, newly constructed facilities often lack the necessary data to learn any predictive models, preventing them from being truly smart. Additionally, data collected from different facilities is heterogeneous
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An efficient exact method with polynomial time-complexity to achieve [formula omitted]-strong barrier coverage in heterogeneous wireless multimedia sensor networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-05 Nguyen Thi My Binh, Huynh Thi Thanh Binh, Ho Viet Duc Luong, Nguyen Tien Long, Trinh Van Chien
Barrier coverage in Wireless Sensor Networks (WSNs) plays a pivotal role in surveillance and security applications. It serves as a fundamental mechanism for identifying and detecting potential intruders who endeavor to infiltrate a sensor barrier. Achieving -strong barrier coverage is a vital indicator of a WSN’s capability to detect unauthorized intrusions. This paper establishes efficient -strong
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CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-02 Mohamad Wazzeh, Mohamad Arafeh, Hani Sami, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Hadi Otrok
In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) and Federated Learning (FL) have recently emerged as promising technologies for training a decentralized Machine Learning (ML) model. With the increasing use of smartphones and Internet of Things (IoT) devices, these distributed
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Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-02 Mahdi Alkaeed, Adnan Qayyum, Junaid Qadir
The metaverse is a nascent concept that envisions a virtual universe, a collaborative space where individuals can interact, create, and participate in a wide range of activities. Privacy in the metaverse is a critical concern as the concept evolves and immersive virtual experiences become more prevalent. The metaverse privacy problem refers to the challenges and concerns surrounding the privacy of
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Designing transport scheme of 3D naked-eye system J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-08-02 Rong Zheng, Xiaoqin Feng, Fengyuan Ren
3D naked-eye is constructed from multi-stream as a typical representation of stereoscopic video. Its enormous data volume and stringent low-delay transport requirements pose significant challenges for high-quality real-time transport. Through analysis and experimental verification that current streaming media transport frameworks using the server–client or peer-to-peer scheme face difficulties when
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DFier: A directed vulnerability verifier for Ethereum smart contracts J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-30 Zeli Wang, Weiqi Dai, Ming Li, Kim-Kwang Raymond Choo, Deqing Zou
Smart contracts are self-executing digital agreements that automatically enforce the terms between parties, playing a crucial role in blockchain systems. However, due to the potential losses of digital assets caused by vulnerabilities, the security issues of Ethereum smart contracts have garnered widespread attention. To address this, researchers have developed various techniques to detect vulnerabilities
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MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-30 Yingya Guo, Mingjie Ding, Weihong Zhou, Bin Lin, Cen Chen, Huan Luo
Hybrid Software Defined Networks (Hybrid SDNs), which combines the robustness of distributed network and the flexibility of centralized network, is now a prevailing network architecture. Previous hybrid SDN Traffic Engineering (TE) solutions search an optimal link weight setting or compute the splitting ratios of traffic leveraging heuristic algorithms. However, these methods cannot react timely to
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Fatriot: Fault-tolerant MEC architecture for mission-critical systems using a SmartNIC J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-29 Taejune Park, Myoungsung You, Jinwoo Kim, Seungsoo Lee
Multi-access edge computing (MEC), deploying cloud infrastructures proximate to end-devices and reducing latency, takes pivotal roles for mission-critical services such as smart grids, self-driving cars, and healthcare. Ensuring fault-tolerance is paramount for mission-critical services, as failures in these services can lead to fatal accidents and blackouts. However, the distributed nature of MEC
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Anomalous state detection in radio access networks: A proof-of-concept J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-26 Michael Frey, Thomas Evans, Angela Folz, Mary Gregg, Jeanne Quimby, Jacob D. Rezac
Modern radio access networks (RANs) are both highly complex and potentially vulnerable to unauthorized security setting changes. A RAN is studied in a proof-of-concept experiment to demonstrate that an unauthorized network state is detectable at layers in the RAN architecture away from the source of the state setting. Specifically, encryption state is set at the packet data convergence protocol (PDCP)
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A bandwidth delay product based modified Veno for high-speed networks: BDP-Veno J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-25 Subhra Priyadarshini Biswal, Sanjeev Patel
In recent years, we have seen a significant enhancement in the performance of standard Transmission Control Protocol (TCP) congestion control algorithms. The number of packet drops and high round-trip time (RTT) are indications of network congestion. Many congestion control mechanisms have been proposed to overcome the challenge of achieving increased throughput and reduced latency. We have reviewed
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CIBORG: CIrcuit-Based and ORiented Graph theory permutation routing protocol for single-hop IoT networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-25 Alain Bertrand Bomgni, Garrik Brel Jagho Mdemaya, Miguel Landry Foko Sindjoung, Mthulisi Velempini, Celine Cabrelle Tchuenko Djoko, Jean Frederic Myoupo
The Internet of Things (IoT) has emerged as a promising paradigm which facilitates the seamless integration of physical devices and digital systems, thereby transforming multiple sectors such as healthcare, transportation, and urban planning. This paradigm is also known as ad-hoc networks. IoT is characterized by several pieces of equipment called objects. These objects have different and limited capacities
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A low-storage synchronization framework for blockchain systems J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-25 Yi-Xiang Wang, Yu-Ling Hsueh
The advent of blockchain technology has brought major changes to traditional centralized storage. Therefore, various fields have begun to study the application and development of blockchain. However, blockchain technology has a serious shortcoming of data bloating. The reason is that blockchain technology achieves decentralization by storing complete blockchain data at each node, incurring a significant
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A lightweight SEL for attack detection in IoT/IIoT networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-25 Sulyman Age Abdulkareem, Chuan Heng Foh, François Carrez, Klaus Moessner
Intrusion detection systems (IDSs) that continuously monitor data flow and take swift action when attacks are identified safeguard networks. Conventional IDS exhibit limitations, such as reduced detection rates and increased computational complexity, attributed to the redundancy and substantial correlation of network data. Ensemble learning (EL) is effective for detecting network attacks. Nonetheless
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HRMF-DRP: A next-generation solution for overcoming provisioning challenges in cloud environments J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-24 Devi D, Godfrey Winster S
The cloud computing infrastructure is a distributed environment and the existing research works have some provisioning problems such as suboptimal resource utilization and high execution time. The Heterogeneity Resource Management Framework for Dynamic Resource Provisioning (HRMF-DRP) is proposed for focusing on task scheduling and workload management. This framework incorporates advanced algorithms
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Blockchain applications in UAV industry: Review, opportunities, and challenges J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-23 Diana Hawashin, Mohamed Nemer, Senay A. Gebreab, Khaled Salah, Raja Jayaraman, Muhammad Khurram Khan, Ernesto Damiani
In recent years, the application of blockchain technology in the Unmanned Aerial Vehicle (UAV) industry has shown promise in making a substantial impact on various aspects of the field. Blockchain can provide key solutions to several challenges related to security, data integrity, and operational efficiency within UAV systems. In this paper, we conduct an in-depth investigation of the transformative
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Exploiting web content semantic features to detect web robots from weblogs J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-22 Rikhi Ram Jagat, Dilip Singh Sisodia, Pradeep Singh
Nowadays, web robots are predominantly used for auto-accessing web content, sharing almost one-third of the total web traffic and often posing threats to various web applications’ security, privacy, and performance. Detecting these robots is essential, and both online and offline methods are employed. One popular offline method is the use of weblog feature-based automated learning. However, this method
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CGSNet: Cross-consistency guiding semi-supervised semantic segmentation network for remote sensing of plateau lake J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-20 Guangchen Chen, Benjie Shi, Yinhui Zhang, Zifen He, Pengcheng Zhang
Analyzing the geographical information for the Plateau Lake region with remote sensing images (RSI) is an emerging technology to monitor the changes of the ecological environment. To alleviate the requirement of abundant labels for supervised RSI segmentation, the Cross-consistency Guiding Semi-supervised Learning (SSL) Semantic Segmentation Network is proposed, and it can perform high-quality multi-category
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Skin lesion classification using modified deep and multi-directional invariant handcrafted features J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-14 Jitesh Pradhan, Ashish Singh, Abhinav Kumar, Muhammad Khurram Khan
Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as
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CL-AP[formula omitted]: A composite learning approach to attack prediction via attack portraying J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-04 Yingze Liu, Yuanbo Guo
The capabilities of accurate prediction of cyberattacks have long been desired as detection methods cannot avoid the damages caused by occurrences of cyberattack. Attack prediction still remains an open issue especially to specify the upcoming steps of an attack with the quickly evolving intelligent techniques at the attackers’ side. This study proposes a composite learning approach (namely CL-AP)
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Attenuating majority attack class bias using hybrid deep learning based IDS framework J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-03 K.G. Raghavendra Narayan, Rakesh Ganesula, Tamminaina Sai Somasekhar, Srijanee Mookherji, Vanga Odelu, Rajendra Prasath, Alavalapati Goutham Reddy
In real-time application domains, like finance, healthcare and defence, delay in service or stealing information may lead to unrecoverable consequences. So, early detection of intrusion is important to prevent security breaches. In recent days, anomaly-based intrusion detection using Hybrid Deep Learning approaches are becoming more popular. The most used benchmark datasets in the literature are NSL-KDD
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Uncovering phishing attacks using principles of persuasion analysis J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-03 Lázaro Bustio-Martínez, Vitali Herrera-Semenets, Juan Luis García-Mendoza, Miguel Ángel Álvarez-Carmona, Jorge Ángel González-Ordiano, Luis Zúñiga-Morales, J. Emilio Quiróz-Ibarra, Pedro Antonio Santander-Molina, Jan van den Berg
With the rising of Internet in early ’90s, many fraudulent activities have migrated from physical to digital: one of them is phishing. Phishing is a deceptive practice focused on exploiting the human factor, which is the most vulnerable aspect of any security process. In this scam, social engineering techniques are extensively utilized, specifically focusing on the principles of persuasion, to deceive
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On designing a profitable system model to harmonize the tripartite dissension in content delivery applications J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-02 Libin Yang, Wei Lou
The popularity of commercial content delivery applications has led to dissension among three embroiled parties: Content Service Providers (CSPs), Internet Service Providers (ISPs), and End Users (EUs). This dissension is not only a technical problem but an economic problem. To harmonize this dissension, this paper takes live streaming as a typical content delivery application. It proposes a profitable
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Satellite synergy: Navigating resource allocation and energy efficiency in IoT networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-02 Muhammad Abdullah, Humayun Zubair Khan, Umair Fakhar, Ahmad Naeem Akhtar, Shuja Ansari
Satellite-assisted internet of things (IoT) networks have emerged as a beacon of promise, offering global coverage and uninterrupted connectivity. However, the challenges of resource allocation and task offloading in such networks are intricate due to the unique characteristics of satellite communication systems. This research’s findings enrich the landscape of energy-efficient and dependable satellite-assisted
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Privacy-preserving generation and publication of synthetic trajectory microdata: A comprehensive survey J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-07-01 Jong Wook Kim, Beakcheol Jang
The generation of trajectory data has increased dramatically with the advent and widespread use of GPS-enabled devices. This rich source of data provides invaluable insights for various applications such as traffic optimization, urban planning, crowd management, and public safety. However, the increasing demand for the publication and sharing of trajectory data for big data analytics raises significant
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A Contextual Multi-Armed Bandit approach for NDN forwarding J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-29 Yakoub Mordjana, Badis Djamaa, Mustapha Reda Senouci, Aymen Herzallah
Named Data Networking (NDN) is a promising Internet architecture that aims to supersede the current IP-based architecture and shift the host-centric model to a data-centric one. Within NDN, forwarding Interest packets remains a significant challenge and has attracted considerable recent research attention. The momentum behind machine learning techniques, especially reinforcement learning, is steadily
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JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-28 Ala Altaweel, Sidra Aslam, Ibrahim Kamel
This paper first shows that Prophet, Spray and Wait, Epidemic, and First Contact routing protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the Jamhole attack. In Jamhole attack, an attacker, Eve, compromises two different locations in OMNs by (i) jamming the GPS signal of victim nodes in these locations and (ii) by establishing a pair-wise hidden wormhole tunnel among these locations
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Multi-UAV aided energy-aware transmissions in mmWave communication network: Action-branching QMIX network J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-26 Quang Tuan Do, Thien Duc Hua, Anh-Tien Tran, Dongwook Won, Geeranuch Woraphonbenjakul, Wonjong Noh, Sungrae Cho
Advancements in drone technology and high-frequency millimeter-wave communications are transforming unmanned-aerial-vehicles (UAV)-aided networks, expanding their potential across diverse applications. Despite the advantages of broad frequency bandwidth and enhanced line of sight connectivity in the UAV-aided millimeter-wave networks, it is challenging to provide high network performance because of
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Leveraging application permissions and network traffic attributes for Android ransomware detection J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-26 Sekione Reward Jeremiah, Haotian Chen, Stefanos Gritzalis, Jong Hyuk Park
The increase in ransomware threats targeting Android devices necessitates the development of advanced techniques to strengthen the effectiveness of detection and prevention methods. Existing studies use Machine Learning (ML) techniques to detect and classify ransomware attacks, however, the ransomware landscape's rapid evolution hinders the effectiveness of these approaches. Moreover, the potential
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SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-25 Zainab H. Ali, Nora El-Rashidy, Mostafa A. Elhosseini, Sarah M. Ayyad
Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks
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Towards zero-energy: Navigating the future with 6G in Cellular Internet of Things J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-25 Muhammad Tahir Abbas, Karl-Johan Grinnemo, Guillaume Ferré, Philippe Laurent, Stefan Alfredsson, Mohammad Rajiullah, Johan Eklund
The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight
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Detecting DDoS based on attention mechanism for Software-Defined Networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-25 Namkyung Yoon, Hwangnam Kim
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Quick service during DDoS attacks in the container-based cloud environment J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-25 Anmol Kumar, Mayank Agarwal
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Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-25 Xu Zhao, Yichuan Wu, Tianhao Zhao, Feiyu Wang, Maozhen Li
Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users’ quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during
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Virtual reality traffic prioritization for Wi-Fi quality of service improvement using machine learning classification techniques J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-24 Seyedeh Soheila Shaabanzadeh, Marc Carrascosa-Zamacois, Juan Sánchez-González, Costas Michaelides, Boris Bellalta
The increase in the demand for eXtended Reality (XR)/Virtual Reality (VR) services in the recent years, poses a great challenge for Wi-Fi networks to maintain the strict latency requirements. In VR over Wi-Fi, latency is a significant issue. In fact, VR users expect instantaneous responses to their interactions, and any noticeable delay can disrupt user experience. Such disruptions can cause motion
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Constrained routing in multi-partite graph to solve VNF placement and chaining problem J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-24 Mohand Yazid Saidi, Issam Abdeldjalil Ikhelef, Shuopeng Li, Ken Chen
Network Functions Virtualization (NFV) and Software-Defined Networks (SDN) empower IT professionals and service providers to strategically deploy Virtual Network Functions (VNFs), resulting in enhanced services and security while minimizing costs. Network services are dynamically provided through the deployment of Service Function Chains (SFCs), which involve selecting and interconnecting physical
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Metarouting with automatic tunneling in multilayer networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-24 Noureddine Mouhoub, Maria Moloney, Damien Magoni
Metarouting allows for the modeling of routing protocols using an algebraic structure called routing algebra. Routing protocols requiring design or validation can easily be modeled using this approach. To date, however, existing research on routing algebras has mainly focused on applying this approach to routing protocols that are generally used in networks which have a single addressing and forwarding
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A robust PID and RLS controller for TCP/AQM system J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-24 Junyong Tang, Hui Li, Jiankang Zhang, Kangqian Guan, Qiqi Shan, Xiangyang Liang
Transmission Control Protocol (TCP) controlling congestion by peer-to-peer is challenging to handle communication with numerous concurrent TCP flows and heavy traffic loads. Therefore, TCP requires the active queue management (AQM) to assist in avoiding buffer bloat in intermediate devices. Thus, Some AQM controllers, such as Red, Codel, and Pie, have been proposed to control congestion better. However
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GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-24 Mingjie Ding, Yingya Guo, Zebo Huang, Bin Lin, Huan Luo
Routing optimization, as a significant part of Traffic Engineering (TE), plays an important role in balancing network traffic and improving quality of service. With the application of Machine Learning (ML) in various fields, many neural network-based routing optimization solutions have been proposed. However, most existing ML-based methods need to retrain the model when confronted with a network unseen
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An agnostic and secure interoperability protocol for seamless asset movement J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 El-hacen Diallo, Mohameden Dieye, Omar Dib, Pierre Valiorgue
As blockchain technology continues to evolve, it has fostered an extensive ecosystem of applications and platforms. This dynamic landscape is characterized by a myriad of innovative solutions, ranging from decentralized finance and supply chain management to digital identity and voting systems, each contributing to the ongoing advancement and adoption of blockchain technology across various sectors
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Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Riya Goyal, Abhinav Tomar
With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in
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RECAR: Robust and efficient collision-avoiding routing for 3D underwater named data networking J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Yue Li, Yingjian Liu, Haoyu Yin, Zhongwen Guo, Yu Wang
Internet of Underwater Things (IoUT) has important application prospects in fields of both scientific research and commercial business. As a future network architecture, Named Data Networking (NDN) is starting to be applied to IoUT. Although Underwater Named Data Networking (UNDN) has unique advantages in dealing with bandwidth usage, multi-path forwarding, and node mobility, there is still no specific
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Broadcast/multicast delivery integration in B5G/6G environments J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Orlando Landrove, Rufino Cabrera, Eneko Iradier, Erick Jimenez, Pablo Angueira, Jon Montalban
This paper describes the design of a Broadcast Core Network (BCN). The BCN is intended to enable the integration of existing terrestrial broadcast systems into a 5G/6G ecosystem. The existing multimedia content distribution architecture in current broadcast networks and the capabilities of 5G access and core networks (5GC) are analyzed to dissect their limitations. We show, inter alia, how the lack
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Multi-agent reinforcement learning for privacy-aware distributed CNN in heterogeneous IoT surveillance systems J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani
Although Deep Neural Networks (DNN) have become the backbone technology of several Internet of Things (IoT) applications, their execution in resource-constrained devices remains challenging. To cater for these challenges, collaborative deep inference conducted by IoT devices was introduced. However, the prevalence of DNN computation suffers from severe privacy problems, e.g. data-reverse and model
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AI for AI-based intrusion detection as a service: Reinforcement learning to configure models, tasks, and capacities J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Ying-Dar Lin, Hao-Xuan Huang, Didik Sudyana, Yuan-Cheng Lai
Intrusion Detection Systems (IDS) increasingly leverage machine learning (ML) to enhance the detection of zero-day attacks. As operational complexities increase, enterprises are turning to Intrusion Detection as a Service (IDaS), requiring advanced solutions for efficient ML model selection and resource allocation. Existing research often focuses primarily on accuracy and computational efficiency,
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The universal federator: A third-party authentication solution to federated cloud, edge, and fog J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Asad Ali, Ying-Dar Lin, Jian Liu, Chin-Tser Huang
Cloud, Edge, and Fog computing provide computational services to different end users. A federation among these computing paradigms is beneficial, as it enhances the capability, capacity, coverage, and services of cloud, edge, and fog. An authentication method is needed to realize such a federation among cloud, edge, and fog so that a user belonging to one of these computing paradigms can use the services
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Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-22 Weitao Yang, Li Pan, Shijun Liu
With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease
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Troubleshooting solution for traffic congestion control J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-21 Van Tong, Sami Souihi, Hai Anh Tran, Abdelhamid Mellouk
The Internet has existed since the 1970s as a means of data exchange between network devices in small networks. In the early stage, there was a small number of devices, but today there is an ever-increasing number of devices, leading to congestion in the network. Therefore, congestion control has attracted so much attention in the academic community and the industry for the past 30 years. Recently
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MCTE-RPL: A multi-context trust-based efficient RPL for IoT J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-21 Javad Mohajerani, Mokhtar Mohammadi Ghanatghestani, Malihe Hashemipour
The Internet of things (IoT) is highly exposed to various attacks due to its sensitive applications, but it is very vulnerable in dealing with these attacks. So, various studies have been introduced to improve IoT security. Most of methods have focused on improving the security of the RPL protocol (Low-Power and Lossy Networks routing protocol) based on the development of trust models. However, most
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Seraph: Towards secure and efficient multi-controller authentication with [formula omitted]-threshold signature in multi-domain SDWAN J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-21 Wendi Feng, Ke Liu, Shuo Sun, Bo Cheng, Wei Zhang
The multi-controller scheme is widely adopted in Software-Defined Wide Area Networks (SDWANs), where a WAN is segmented into multiple domains, each controlled by one controller. These controllers communicate with each other in-band, necessitating authentication before exchanging control messages. However, relying solely on identification of a single node for authentication exposes the network to spoofing
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Advanced optimization-based weighted features for ensemble deep learning smart occupancy detection network for road traffic parking J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-20 B. Padmavathi, Vanaja Selvaraj
In day-to-day activities, the advanced technology like Internet of Things (IoT) emerges to improve the lifestyle of people. In metropolitan cities, real-time parking is the long-lasting problem that we face in our daily life activities. Urban parking regulation gained more attention because of its capability to diminish energy consumption, congested traffic, and manifestation. The parking space detection
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Hybrid kitchen safety guarding with stove fire recognition based on the Internet of Things J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-20 Lien-Wu Chen, Hsing-Fu Tseng, Chun-Yu Cho, Ming-Fong Tsai
In this paper, we design a hybrid kitchen safety guarding framework using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). According to the relevant literature we studied, this is the first framework for kitchen safety guarding that provides the following features: (1) the deep learning based model integrating densely
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CDT: Cross-interface Data Transfer scheme for bandwidth-efficient LoRa communications in energy harvesting multi-hop wireless networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-20 Hua Qin, Ni Li, Tao Wang, Gelan Yang, Yang Peng
With the capability of generating sustainable energy by exploiting the ambient environment (e.g., light, heat, vibrations, etc.), the energy harvesting (EH) technology is increasingly used on low-power smart objects, forming self-powered green Internet of Things (IoTs). Despite targeting different application domains, many of these green IoT systems adopt the distributed network paradigm by forming
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Synthetic and privacy-preserving traffic trace generation using generative AI models for training Network Intrusion Detection Systems J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-20 Giuseppe Aceto, Fabio Giampaolo, Ciro Guida, Stefano Izzo, Antonio Pescapè, Francesco Piccialli, Edoardo Prezioso
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Recent endeavors in machine learning-powered intrusion detection systems for the Internet of Things J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-20 D. Manivannan
The significant advancements in sensors and other resource-constrained devices, capable of collecting data and communicating wirelessly, are poised to revolutionize numerous industries through the Internet of Things (IoT). Sectors such as healthcare, energy, education, transportation, manufacturing, military, and agriculture stand to benefit. IoT is expected to play a crucial role in implementing both
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A multi-UAV assisted task offloading and path optimization for mobile edge computing via multi-agent deep reinforcement learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-06-20 Tao Ju, Linjuan Li, Shuai Liu, Yu Zhang
To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi-agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge