Computer Networks最新文献

{"title":"Survey of fault management techniques for edge-enabled distributed metaverse applications","authors":"Shahzaib Shaikh,&nbsp;Manar Jammal","doi":"10.1016/j.comnet.2024.110803","DOIUrl":"10.1016/j.comnet.2024.110803","url":null,"abstract":"<div><p>The metaverse, envisioned as a vast, distributed virtual world, relies on edge computing for low-latency data processing. However, ensuring fault tolerance – the system’s ability to handle failures – is critical for a seamless user experience. This paper analyzes existing research on fault tolerance in edge computing over the past six years, specifically focusing on its applicability to the metaverse. We identify common fault types like node failures, communication disruptions, and security issues. The analysis then explores various fault management techniques including proactive monitoring, resource optimization, task scheduling, workload migration, redundancy for service continuity, machine learning for predictive maintenance, and consensus algorithms to guarantee data integrity. While these techniques hold promise, adaptations are necessary to address the metaverse’s real-time interaction requirements and low-latency constraints. This paper analyzes existing research and identifies key areas for improvement, providing valuable research guidelines and insights to pave the way for the development of fault management techniques specifically tailored to the metaverse, ultimately contributing to a robust and secure virtual world.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274084","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":"Securing the internet’s backbone: A blockchain-based and incentive-driven architecture for DNS cache poisoning defense","authors":"Yufan Fu ,&nbsp;Xiaodong Lee ,&nbsp;Jiuqi Wei ,&nbsp;Ying Li ,&nbsp;Botao Peng","doi":"10.1016/j.comnet.2024.110777","DOIUrl":"10.1016/j.comnet.2024.110777","url":null,"abstract":"<div><p>Domain Name System (DNS) is the backbone of the Internet infrastructure, converting human-friendly domain names into machine-processable IP addresses. However, DNS remains vulnerable to various security threats, such as cache poisoning attacks, where malicious attackers inject false information into DNS resolvers’ caches. Although efforts have been made to enhance DNS against such vulnerabilities, existing countermeasures often fall short in one or more areas: they may offer limited resistance to the collusion attack, introduce significant overhead, or require complex implementation that hinders widespread adoption. To address these challenges, this paper introduces TI-DNS+, a trusted and incentivized blockchain-based DNS resolution architecture for cache poisoning defense. TI-DNS+ introduces a <em>Verification Cache</em> exploiting blockchain ledger’s immutable nature to detect and correct forged DNS responses. The architecture also incorporates a multi-resolver <em>Query Vote</em> mechanism, enhancing the ledger’s credibility by validating each record modification through a stake-weighted algorithm. This algorithm selects resolvers as validators based on their stake proportion. To promote well-behaved participation, TI-DNS+ also implements a novel stake-based incentive mechanism that optimizes the generation and distribution of stake rewards. This ensures that incentives align with participants’ contributions, achieving incentive compatibility, fairness, and efficiency. Moreover, TI-DNS+ possesses high practicability as it requires only resolver-side modifications to current DNS. Finally, through comprehensive prototyping and experimental evaluations, the results demonstrate that our solution effectively mitigates DNS cache poisoning. Compared to competitors, our solution improves attack resistance by 1-3 orders of magnitude, while also reducing resolution latency by 5% to 68%.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274080","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":"Task offloading strategies for mobile edge computing: A survey","authors":"Shi Dong ,&nbsp;Junxiao Tang ,&nbsp;Khushnood Abbas ,&nbsp;Ruizhe Hou ,&nbsp;Joarder Kamruzzaman ,&nbsp;Leszek Rutkowski ,&nbsp;Rajkumar Buyya","doi":"10.1016/j.comnet.2024.110791","DOIUrl":"10.1016/j.comnet.2024.110791","url":null,"abstract":"<div><p>With the wide adoption of 5G technology and the rapid development of 6G technology, a variety of new applications have emerged. A multitude of compute-intensive and time-sensitive applications deployed on terminal equipment have placed increased demands on Internet delay and bandwidth. Mobile Edge Computing (MEC) can effectively mitigate the issues of long transmission times, high energy consumption, and data insecurity. Task offloading, as a key technology within MEC, has become a prominent research focus in this field. This paper presents a comprehensive review of the current research progress in MEC task offloading. Firstly, it introduces the fundamental concepts, application scenarios, and related technologies of MEC. Secondly, it categorizes offloading decisions into five aspects: reducing delay, minimizing energy consumption, balancing energy consumption and delay, enabling high-computing offloading, and addressing different application scenarios. It then critically analyzes and compares existing research efforts in these areas.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241822","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":"Lifetime maximization of IoT-enabled smart grid applications using error control strategies","authors":"Nazli Tekin ,&nbsp;Bilge Kagan Dedeturk ,&nbsp;Vehbi Cagri Gungor","doi":"10.1016/j.comnet.2024.110778","DOIUrl":"10.1016/j.comnet.2024.110778","url":null,"abstract":"<div><p>Recently, with the advancement of Internet of Things (IoT) technology, IoT-enabled Smart Grid (SG) applications have gained tremendous popularity. Ensuring reliable communication in IoT-based SG applications is challenging due to the harsh channel environment often encountered in the power grid. Error Control (EC) techniques have emerged as a promising solution to enhance reliability. Nevertheless, ensuring network reliability requires a substantial amount of energy consumption. In this paper, we formulate a Mixed Integer Programming (MIP) model which considers the energy dissipation of EC techniques to maximize IoT network lifetime while ensuring the desired level of IoT network reliability. We develop meta-heuristic approaches such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) to address the high computation complexity of large-scale IoT networks. Performance evaluations indicate that the EC-Node strategy, where each IoT node employs the most energy-efficient EC technique, yields a minimum of 8.9% extended lifetimes compared to the EC-Net strategies, where all IoT nodes employ the same EC method for a communication. Moreover, the PSO algorithm reduces the computational time by 77% while exhibiting a 2.69% network lifetime decrease compared to the optimal solution.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241814","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 path planning and power allocation of a cellular-connected UAV using apprenticeship learning via deep inverse reinforcement learning","authors":"Alireza Shamsoshoara ,&nbsp;Fatemeh Lotfi ,&nbsp;Sajad Mousavi ,&nbsp;Fatemeh Afghah ,&nbsp;İsmail Güvenç","doi":"10.1016/j.comnet.2024.110789","DOIUrl":"10.1016/j.comnet.2024.110789","url":null,"abstract":"<div><p>This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV’s goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize interference to the ground user equipment (UEs) connected to neighboring cellular base stations (BSs), considering both the shortest path and limitations on flight resources. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389128624006212/pdfft?md5=dc7b3d1acee33e2f5feab69fccae53be&pid=1-s2.0-S1389128624006212-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232950","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":"Data signals for deep learning applications in Terahertz communications","authors":"Duschia Bodet ,&nbsp;Jacob Hall ,&nbsp;Ahmad Masihi ,&nbsp;Ngwe Thawdar ,&nbsp;Tommaso Melodia ,&nbsp;Francesco Restuccia ,&nbsp;Josep M. Jornet","doi":"10.1016/j.comnet.2024.110800","DOIUrl":"10.1016/j.comnet.2024.110800","url":null,"abstract":"<div><p>The Terahertz (THz) band (0.1–10 THz) is projected to enable broadband wireless communications of the future, and many envision deep learning as a solution to improve the performance of THz communication systems and networks. However, there are few available datasets of true THz signals that could enable testing and training of deep learning algorithms for the research community. In this paper, we provide an extensive dataset of 120,000 data frames for the research community. All signals were transmitted at 165 GHz but with varying bandwidths (5 GHz, 10 GHz, and 20 GHz), modulations (4PSK, 8PSK, 16QAM, and 64QAM), and transmit amplitudes (75 mV and 600 mV), resulting in twenty-four distinct bandwidth-modulation-power combinations each with 5,000 unique captures. The signals were captured after down conversion at an intermediate frequency of 10 GHz. This dataset enables the research community to experimentally explore solutions relating to ultrabroadband deep and machine learning applications.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389128624006327/pdfft?md5=c4870e9a435477344bfb00ccf315d922&pid=1-s2.0-S1389128624006327-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232951","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":"A fast malware detection model based on heterogeneous graph similarity search","authors":"Tun Li,&nbsp;Peng Shou,&nbsp;Xin Wan,&nbsp;Qian Li,&nbsp;Rong Wang,&nbsp;Chaolong Jia,&nbsp;Yunpeng Xiao","doi":"10.1016/j.comnet.2024.110799","DOIUrl":"10.1016/j.comnet.2024.110799","url":null,"abstract":"<div><p>The Android operating system has long been vulnerable to malicious software. Existing malware detection methods often fail to identify ever-evolving malware and are slow in detection. To address this, we propose a new model for rapid Android malware detection, which constructs various Android entities and relationships into a heterogeneous graph. Firstly, to address the semantic fusion problem in high-order heterogeneous graphs that arises with the increase in the depth of the heterogeneous graph model, we introduce adaptive weights during node aggregation to absorb the local semantics of nodes. This allows more attention to be paid to the feature information of the node itself during the semantic aggregation stage, thereby avoiding semantic confusion. Secondly, to mitigate the high time costs associated with detecting unknown applications, we employ an incremental similarity search model. This model quickly measures the similarity between unknown applications and those within the sample, aggregating the weights of nodes based on similarity scores and semantic attention coefficients, thereby enabling rapid detection. Lastly, considering the high time and space complexity of calculating node similarity scores on large graphs, we design a <em>NeuSim</em> model based on an encoder–decoder structure. The encoder module embeds each path instance as a vector, while the decoder converts the vector into a scalar similarity score, significantly reducing the complexity of the calculation. Experiments demonstrate that this model can not only rapidly detect malware but also capture high-level semantic relationships of application software in complex malware networks by hierarchically aggregating information from neighbors and meta-paths of different orders. Moreover, this model achieved an AUC of 0.9356 and an F1 score of 0.9355, surpassing existing malware detection algorithms. Particularly in the detection of unknown application software, the <em>NeuSim</em> model can double the detection speed, with an average detection time of 105 ms.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241820","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":"Truthful mechanism for joint resource allocation and task offloading in mobile edge computing","authors":"Xi Liu ,&nbsp;Jun Liu ,&nbsp;Weidong Li","doi":"10.1016/j.comnet.2024.110796","DOIUrl":"10.1016/j.comnet.2024.110796","url":null,"abstract":"<div><p>In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-time data processing and analysis by offloading to the MEC servers. The objective is maximizing social welfare in an auction-based model. However, the distances between mobile devices and access points lead to differences in energy consumption. Unfortunately, existing works have not considered both maximizing social welfare and minimizing energy consumption. Motivated by this, we address the problem of joint resource allocation and task offloading in MEC, with heterogeneous MEC servers providing multiple types of resources for mobile devices (MDs) to perform tasks remotely. We split the problem into two sub-problems: winner determination and offloading decision. The first sub-problem determines winners granted the ability to offload tasks to maximize social welfare. The second sub-problem determines how to offload tasks among the MEC servers to minimize energy consumption. In the winner determination problem, we propose a truthful algorithm that drives the system into equilibrium. We then show the approximate ratios for single and multiple MEC servers. In the offloading decision problem, we propose an approximation algorithm. We then show it is a polynomial-time approximation scheme for a single MEC server. Experiment results show that our proposed mechanism finds high-quality solutions in changing mobile environments.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228492","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":"An Adversarial Machine Learning Based Approach for Privacy Preserving Face Recognition in Distributed Smart City Surveillance","authors":"Farah Wahida ,&nbsp;M.A.P. Chamikara ,&nbsp;Ibrahim Khalil ,&nbsp;Mohammed Atiquzzaman","doi":"10.1016/j.comnet.2024.110798","DOIUrl":"10.1016/j.comnet.2024.110798","url":null,"abstract":"<div><p>Smart cities rely heavily on surveillance cameras for urban management and security. However, the extensive use of these cameras also raises significant concerns regarding data privacy. Unauthorized access to facial data captured by these cameras and the potential for misuse of this data poses serious threats to individuals’ privacy. Current privacy preservation solutions often compromise data usability with noise application-based approaches and vulnerable centralized data handling settings. To address these privacy challenges, we propose a novel approach that combines Adversarial Machine Learning (AML) with Federated Learning (FL). Our approach involves the use of a noise generator that perturbs surveillance data right from the source before they leave the surveillance cameras. By exclusively training the Federated Learning model on these perturbed samples, we ensure that sensitive biometric features are not shared with centralized servers. Instead, such data remains on local devices (e.g., cameras), thereby ensuring that data privacy is maintained. We performed a thorough real-world evaluation of the proposed method and achieved an accuracy of around 99.95% in standard machine learning settings. In distributed settings, we achieved an accuracy of around 96.24% using federated learning, demonstrating the practicality and effectiveness of the proposed solution.<span><span>¹</span></span></p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389128624006303/pdfft?md5=da5fe96757f1e618798967bd74657413&pid=1-s2.0-S1389128624006303-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241836","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":"Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system","authors":"Jianxin Liu ,&nbsp;Zhiguo Xu ,&nbsp;Rui Fan ,&nbsp;Zhigang Wen","doi":"10.1016/j.comnet.2024.110756","DOIUrl":"10.1016/j.comnet.2024.110756","url":null,"abstract":"<div><div>With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327849","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}