Computer Networks最新文献

{"title":"FMORT: The Meta-Heuristic routing method by integrating index parameters to optimize energy consumption and real execution time using FANET","authors":"Arash GhorbanniaDelavar,&nbsp;Zahra Jormand","doi":"10.1016/j.comnet.2024.110869","DOIUrl":"10.1016/j.comnet.2024.110869","url":null,"abstract":"<div><div>Decreasing energy consumption in Unmanned Aerial Vehicles (UAVs) while simultaneously enhancing their reliability and processing capabilities is considered a fundamental challenge. The routing mechanisms employed in Flying Ad Hoc Networks (FANETs) are more complex compared to those in Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), a challenge addressed by the FMORT method. To tackle these complex routing challenges, clustering techniques that utilize hybrid Meta-heuristic algorithms can be applied. Data analysis within the FMORT framework identified factors influencing service integration, leading to a reduction in redundant request transmissions and overall redundancy in the proposed method. The identification of food sources in the hybrid Meta-heuristic algorithm of the FMORT method is achieved through the integration of the Sparrow and Dragonfly algorithms. These algorithms work simultaneously to increase energy efficiency and increase network lifetime. This strategy optimizes information exchange by selecting an intelligent threshold detector and categorizing inputs, thereby minimizing node mobility. As a result, it improves performance metrics and decreases delivery costs, energy consumption, and delays. In the proposed method, a balanced performance is achieved by comparing existing methods in terms of transmission delay, Packet Delivery Ratio( PDR), throughput, and energy consumption. Simulation results show that the FMORT approach provides effective and stable outcomes in terms of reliability, decreased delays, and improved packet delivery rates. The FMORT framework includes principles for neighbor selection, determining suitable cluster heads, and scoring based on the average Euclidean distance. Additionally, it manages topology access, ensures proper distribution, guarantees data connectivity, and accurately categorizes inputs. By optimizing the sensitivity rate, this method minimizes the average delays and meekly values input data through effective load balancing. Key parameters considered for real time optimization of overall performance include the number of cluster heads during re-clustering, the ratio of request-to-acknowledgment packet transmission, node, and network lifetime, end-to-end delay, and energy consumption. Ultimately, the simulation results show that compared to the MWCRSF algorithm, the average optimization of index parameters,% 0.73 decrease in energy consumption,% 2.23 network lifetime, 1.35 re-cluster construction time and also% 0.11 re-cluster lifetime has increased.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552271","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":"Encrypted-OFDM: A secured wireless waveform","authors":"Hesham Mohammed,&nbsp;Dola Saha","doi":"10.1016/j.comnet.2024.110871","DOIUrl":"10.1016/j.comnet.2024.110871","url":null,"abstract":"<div><div>Wireless communication has been a broadcast system since its inception, which violates security and privacy issues at the physical layer between the intended transmit and receive pairs. Consequently, it is essential to secure the wireless signal such that only the intended receiver can realize the signal properties. In this paper, we propose <span>Encrypted-OFDM</span>, a new waveform, where the signal structure is altered to encrypt the waveform with a shared secret key. We achieve the signal level security by modifying the OFDM signal in time-domain, thus erasing the OFDM properties and obfuscating the signal properties to an eavesdropper. We present a two-stage encryption algorithm to increase the robustness of the transmitted waveform and achieve a high level of secrecy, even when low entropy keys are used. We also introduce a novel channel estimation algorithm by removing the pilots, so that only the intended receiver can estimate the channel correctly. Furthermore, we perform both secrecy and error analysis for the transmitted and received <span>Encrypted-OFDM</span> waveform. Extensive simulation and over-the-air experiments show that the performance of <span>Encrypted-OFDM</span> is comparable to legacy OFDM, and the SNR penalty due to the secured waveform varies between <span><math><mo>≈</mo></math></span> 1–4 dB. In all these scenarios, <span>Encrypted-OFDM</span> remains unrecognized at the eavesdropper.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552269","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":"Utility optimization for computation offloading and splitting in time-varying HAP and LEO satellite integrated MEC networks","authors":"Ruidong Zhang,&nbsp;Jiadong Zhang,&nbsp;Xue Wang,&nbsp;Wenxiao Shi","doi":"10.1016/j.comnet.2024.110866","DOIUrl":"10.1016/j.comnet.2024.110866","url":null,"abstract":"<div><div>To provide ubiquitous and low-latency communication and computation services for remote and disaster areas, high altitude platform (HAP) and low earth orbit (LEO) satellite integrated multi-access edge computing (HLS-MEC) networks have emerged as a promising solution. However, most current studies directly assume that the number of connected satellites is fixed and neglect the modeling of the time-varying multi-satellite computing process. Motivated by this, we establish an M/G/K(t) queuing model to illustrate task computation on satellites. To evaluate the efficiency and quality of computation offloading and splitting, we develop a utility model. This model is defined as a difference between a value function that assesses the trade-offs of task offloading, considering latency reductions and energy savings, and a cost function that quantifies expenses related to latency and energy consumption. After formulating the utility maximization problem, we propose the deep reinforcement learning-based offloading and splitting (DBOS) scheme that can overcome the time-varying uncertainties and high dynamics in the HLS-MEC network. Specifically, the DBOS scheme can learn the best computation offloading and splitting policy to maximize the utility by sensing the number of connected satellites, the distance between the HAP and satellites, the available computing resources, and the task arrival rate. Finally, we evaluate and validate the computational complexity and convergence property of the DBOS scheme. Numerical results show that the DBOS scheme outperforms the other three benchmarks and maximizes the utility under time-varying dynamics.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533937","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":"Optimizing deep-space DTN congestion control via deep reinforcement learning","authors":"Lei Yang ,&nbsp;Juan A. Fraire ,&nbsp;Kanglian Zhao ,&nbsp;Ruhai Wang ,&nbsp;Wenfeng Li ,&nbsp;Hong Yang","doi":"10.1016/j.comnet.2024.110865","DOIUrl":"10.1016/j.comnet.2024.110865","url":null,"abstract":"<div><div>This paper introduces an innovative congestion control mechanism for delay/disruption-tolerant networking (DTN) within deep-space communication systems, leveraging the nuanced capabilities of deep reinforcement learning (DRL). This approach significantly departs from traditional methods, addressing the unique challenges of deep-space data transmissions. The proposed DRL-based strategy demonstrates a superior balance of critical factors, including transmission delay, energy efficiency, and data reception integrity. We assess our approach through meticulous simulation and comparison with established benchmark schemes. The findings underscore the mechanism’s enhanced performance metrics, positing it as an appealing solution in the evolving landscape of non-terrestrial networking.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533940","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":"Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’","authors":"Himanshu Verma ,&nbsp;Naveen Chauhan ,&nbsp;Lalit Kumar Awasthi","doi":"10.1016/j.comnet.2024.110862","DOIUrl":"10.1016/j.comnet.2024.110862","url":null,"abstract":"<div><div>The <em>Internet of Healthcare Things</em> (IoHT) is rapidly evolving, providing new opportunities to enhance healthcare delivery. However, the resource limitation of connected medical sensing devices leads to congestion, resulting in reduced network performance, delayed data transmission, and loss of critical medical information, which can have significant consequences in healthcare. To address this issue, this paper proposes an Enhanced Hybrid Congestion Mitigation Strategy (EHCMS) for <em>IPv6 over low-power wireless personal area networks (6LoWPAN)</em> and <em>routing protocol for low-Power and lossy networks (RPL)</em> based patient-centric IoHT (PC-IoHT). The EHCMS combines several techniques, including traffic management, network topology optimization, and load balancing, to enhance network performance and reduce congestion. The proposed framework is a hybrid strategy that utilizes resource- and traffic-control mechanisms to alleviate congestion in the 6LoWPAN-RPL-based patient-centric IoHT network. For the resource-control-based approach, a congestion-aware composite objective function is designed using a few congestion-specific routing metrics and formulated as a multi-attribute decision-making problem solved using Grey relational analysis (GRA). In addition, a non-linear multi-criteria optimization problem-based transmission rate adaptation mechanism is contrived as a traffic-control scheme for congestion mitigation. The effectiveness of the proposed EHCMS is evaluated using simulations on the <em>Cooja</em> simulator in the <em>Contiki-3.0 OS</em> and compared with existing congestion-alleviating strategies. The results demonstrate that the proposed framework can significantly reduce congestion in the IoHT network, perform better than existing works, and improve the quality of service. This research paper contributes to the field of IoHT by proposing an effective congestion mitigation strategy that enhances the reliability and performance of the PC-IoHT network, ultimately improving the quality of healthcare delivery.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552344","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":"Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective","authors":"Yingzhuo Deng,&nbsp;Zicheng Hu,&nbsp;Weihao Xu,&nbsp;Ningning Han,&nbsp;Haibin Cai","doi":"10.1016/j.comnet.2024.110850","DOIUrl":"10.1016/j.comnet.2024.110850","url":null,"abstract":"<div><div>The growth of global data is increasing exponentially, leading to a greater demand for computing power. To address this requirement, expanding computing power from the cloud to the edge is essential. However, this transformation presents two significant challenges: how to share computing resources more efficiently and how to optimize resource allocation. To tackle these challenges, we propose a three-layer Computing Power Network (CPN) framework that focuses on implementing the collaborative allocation of computing nodes and user tasks. We formulate the resource allocation problem in CPN as a double auction game and use an experience-weighted attraction algorithm that enables participants to adjust bidding strategies based on environmental interactions. We implemented a prototype of our proposed CPN framework and conducted extensive experiments to verify our algorithm’s convergence and evaluate the benefits obtained by buyers (users) and sellers (computing nodes) from the perspective of transaction prices, rewards, and average pricing. The comprehensive experimental results demonstrate the effectiveness of our proposed method. Compared with state-of-the-art pricing strategies, our approach achieves a 20% increase in convergence speed and an 88% increase in overall returns. Furthermore, it also exhibits a 2.5% increase in deal prices and a substantial 83% rise in the income of individual users. These outcomes convincingly prove the superiority of our method in achieving better convergence, improving overall returns, and benefiting both buyers and sellers in the CPN resource auction market.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533936","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":"NetBoost: Towards efficient distillation and service differentiation of network information exposure","authors":"Jie Chen,&nbsp;Jian Luo,&nbsp;Kai Gao","doi":"10.1016/j.comnet.2024.110829","DOIUrl":"10.1016/j.comnet.2024.110829","url":null,"abstract":"<div><div>Exposing network information such as distances between end hosts is useful to improve the quality of experiences for network users. Network providers calculate such information based on private topology and routing data and share it with users through well-established protocols such as Application-Layer Traffic Optimization. However, it is usually not intended to expose the original model, which can face scalability, user heterogeneity, efficacy &amp; efficiency challenges.</div><div>In this paper, we introduce NetBoost, a system that efficiently distills and differentiates ALTO-based network information to address these challenges concurrently. NetBoost significantly reduces the size of exposed network information by orders of magnitude. In particular, by utilizing a gradient boosting algorithm for classification and regression based on IP prefix matching, NetBoost provides high-order information exposure models, allowing network providers to offer differentiated services to clients with privacy-preserving. Our experimental results demonstrate that NetBoost performs effectively in resource-constrained scenarios, surpassing state-of-the-art lossy compression algorithms and achieving greater accuracy than the XGBoost gradient boosting algorithm, while maintaining a comparable compression rate. Furthermore, in simulation experiments conducted using the real-world networking software BIND, NetBoost achieved 6.96% and 5.2% higher accuracy compared to XGBoost under the same number of rules, with NetBoost’s accuracy set at 95% and 90%, respectively. Additionally, NetBoost reduced resolve time by 44.35% and 52.47%, respectively.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533941","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 efficient hierarchical attribute-based encryption scheme with cross-domain data sharing","authors":"Guangcan Yang ,&nbsp;Peixuan Li ,&nbsp;Yang Xin ,&nbsp;Yunhua He ,&nbsp;Chao Wang ,&nbsp;Xiubo Chen","doi":"10.1016/j.comnet.2024.110863","DOIUrl":"10.1016/j.comnet.2024.110863","url":null,"abstract":"<div><div>With the rapid advancement of data sharing technology, an increasing amount of data is being stored on cloud servers. To enable fine-grained access control over the data stored on cloud servers, the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology has been widely adopted. Recognizing that shared data and files often possess a hierarchical structure, hierarchical CP-ABE technology has been proposed recently. However, most existing schemes are restricted to single-domain data access, which limits their flexibility and universal applicability in practical applications. To address this limitation, an access control scheme based on hierarchical CP-ABE, named CDS-CP-ABE, is proposed to facilitate secure and efficient cross-domain data sharing. The scheme is capable of not only realizing fine-grained hierarchical access control within a single domain but also enabling cross-domain data sharing. Security analysis confirms that our scheme effectively resists chosen-plaintext attack. Furthermore, empirical results indicate that the time consumption associated with our scheme is lower compared to other existing schemes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533942","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":"An Internet of Vehicles road traffic data sharing scheme based on signcryption and editable blockchain","authors":"Xueyan Liu,&nbsp;Xin Xiong,&nbsp;Jia Wang,&nbsp;Yujiao Qi","doi":"10.1016/j.comnet.2024.110864","DOIUrl":"10.1016/j.comnet.2024.110864","url":null,"abstract":"<div><div>Aiming at the problems of low real-time update efficiency of traffic data and privacy leakage of vehicle users in the Internet of Vehicles (IoV) data sharing, an IoV road traffic data sharing scheme based on signcryption and editable blockchain is proposed. Road-Side Unit (RSU) implements proxy signcryption for sharing data based on the authorization of the vehicle, ensuring the reliability of data and reducing the frequent interaction between vehicles and authorized map companies when sharing data. The editable blockchain is implemented by the chameleon hash function, and no new blocks need to be generated when updating data, thereby reducing the storage overhead caused by updating data. Combined with editable blockchain and fog computing server, the storage, sharing and update of traffic data are better realized. Security analysis shows that the proposed scheme satisfies the confidentiality, integrity, verifiability and unforgeability. Simulation results show that the proposed scheme has less communication overhead and computing overhead.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533938","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":"SecureEI: Proactive intellectual property protection of AI models for edge intelligence","authors":"Peihao Li ,&nbsp;Jie Huang ,&nbsp;Shuaishuai Zhang ,&nbsp;Chunyang Qi","doi":"10.1016/j.comnet.2024.110825","DOIUrl":"10.1016/j.comnet.2024.110825","url":null,"abstract":"<div><div>Deploying AI models on edge computing platforms enhances real-time performance, reduces network dependency, and ensures data privacy on terminal devices. However, these advantages come with increased risks of model leakage and misuse due to the vulnerability of edge environments to physical and cyber attacks compared to cloud-based solutions. To mitigate these risks, we propose SecureEI, a proactive intellectual property protection method for AI models that leverages model splitting and data poisoning techniques. SecureEI divides the model into two components: DeviceNet, which processes input data into protected license data, and EdgeNet, which operates on the license data to perform the intended tasks. This method ensures that only the transformed license data yields high model accuracy, while original data remains unrecognizable, even under fine-tuning attacks. We further employ targeted training strategies and weight adjustments to enhance the model’s resistance to potential attacks that aim to restore its recognition capabilities for original data. Evaluations on MNIST, Cifar10, and FaceScrub datasets demonstrate that SecureEI not only maintains high model accuracy on license data but also significantly bolsters defense against fine-tuning attacks, outperforming existing state-of-the-art techniques in safeguarding AI intellectual property on edge platforms.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533934","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}