Digital Communications and Networks最新文献

{"title":"Lightweight consensus mechanisms in the Internet of Blockchained Things: Thorough analysis and research directions","authors":"Somia Sahraoui ,&nbsp;Abdelmalik Bachir","doi":"10.1016/j.dcan.2024.12.007","DOIUrl":"10.1016/j.dcan.2024.12.007","url":null,"abstract":"<div><div>The Internet of Things (IoT) has gained substantial attention in both academic research and real-world applications. The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services. However, this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats. Consequently, innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed. Recently, the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions, commonly referred to as the Internet of Blockchained Things (IoBT). Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments. Within this context, consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems. The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential. This paper presents a comprehensive examination of lightweight, constraint-aware consensus algorithms tailored for IoBT. The study categorizes these consensus mechanisms based on their core operations, the security of the block validation process, the incorporation of AI techniques, and the specific applications they are designed to support.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1246-1261"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926716","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":"Proof-of-trusted-work: A lightweight blockchain consensus for decentralized IoT networks","authors":"Pengzhan Jiang ,&nbsp;Long Shi ,&nbsp;Bin Cao ,&nbsp;Taotao Wang ,&nbsp;Baofeng Ji ,&nbsp;Jun Li","doi":"10.1016/j.dcan.2024.10.011","DOIUrl":"10.1016/j.dcan.2024.10.011","url":null,"abstract":"<div><div>Traditional Internet of Things (IoT) architectures that rely on centralized servers for data management and decision-making are vulnerable to security threats and privacy leakage. To address this issue, blockchain has been advocated for decentralized data management in a tamper-resistance, traceable, and transparent manner. However, a major issue that hinders the integration of blockchain and IoT lies in that, it is rather challenging for resource-constrained IoT devices to perform computation-intensive blockchain consensuses such as Proof-of-Work (PoW). Furthermore, the incentive mechanism of PoW pushes lightweight IoT nodes to aggregate their computing power to increase the possibility of successful block generation. Nevertheless, this eventually leads to the formation of computing power alliances, and significantly compromises the decentralization and security of BlockChain-aided IoT (BC-IoT) networks. To cope with these issues, we propose a lightweight consensus protocol for BC-IoT, called Proof-of-Trusted-Work (PoTW). The goal of the proposed consensus is to disincentivize the centralization of computing power and encourage the independent participation of lightweight IoT nodes in blockchain consensus. First, we put forth an on-chain reputation evaluation rule and a reputation chain for PoTW to enable the verifiability and traceability of nodes' reputations based on their contributions of computing power to the blockchain consensus, and we incorporate the multi-level block generation difficulty as a rewards for nodes to accumulate reputations. Second, we model the block generation process of PoTW and analyze the block throughput using the continuous time Markov chain. Additionally, we define and optimize the relative throughput gain to quantify and maximize the capability of PoTW that suppresses the computing power centralization (i.e., centralization suppression). Furthermore, we investigate the impact of the computing power of the computing power alliance and the levels of block generation difficulty on the centralization suppression capability of PoTW. Finally, simulation results demonstrate the consistency of the analytical results in terms of block throughput. In particular, the results show that PoTW effectively reduces the block generation proportion of the computing power alliance compared with PoW, while simultaneously improving that of individual lightweight nodes. This indicates that PoTW is capable of suppressing the centralization of computing power to a certain degree. Moreover, as the levels of block generation difficulty in PoTW increase, its centralization suppression capability strengthens.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1055-1066"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926843","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":"Efficient modulation mode recognition based on joint communication parameter estimation in non-cooperative scenarios","authors":"Xiangdong Huang ,&nbsp;Yimin Wang ,&nbsp;Yanping Li ,&nbsp;Xiaolei Wang","doi":"10.1016/j.dcan.2024.10.016","DOIUrl":"10.1016/j.dcan.2024.10.016","url":null,"abstract":"<div><div>Due to the neglect of the retrieval of communication parameters (including the symbol rate, the symbol timing offset, and the carrier frequency), the existing non-cooperative communication mode recognizers suffer from the generality ability degradation and severe difficulty in distinguishing a large number of modulation modes, etc. To overcome these drawbacks, this paper proposes an efficient communication mode recognizer consisting of communication parameter estimation, the constellation diagram retrieval, and a classification network. In particular, we define a 2-D symbol synchronization metric to retrieve both the symbol rate and the symbol timing offset, whereas a constellation dispersity annealing procedure is devised to correct the carrier frequency accurately. Owing to the accurate estimation of these crucial parameters, high-regularity constellation maps can be retrieved and thus simplify the subsequent classification work. Numerical results show that the proposed communication mode recognizer acquires higher classification accuracy, stronger anti-noise robustness, and higher applicability of distinguishing multiple types, which presents the proposed scheme with vast applicable potentials in non-cooperative scenarios.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1080-1090"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926844","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":"Accurate and efficient elephant-flow classification based on co-trained models in evolved software-defined networks","authors":"Ling Xia Liao ,&nbsp;Changqing Zhao ,&nbsp;Jian Wang ,&nbsp;Roy Xiaorong Lai ,&nbsp;Steve Drew","doi":"10.1016/j.dcan.2024.10.017","DOIUrl":"10.1016/j.dcan.2024.10.017","url":null,"abstract":"<div><div>Accurate early classification of elephant flows (elephants) is important for network management and resource optimization. Elephant models, mainly based on the byte count of flows, can always achieve high accuracy, but not in a time-efficient manner. The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks (SDNs) to achieve a better resource efficiency. This paper addresses this situation by combining co-training and Reinforcement Learning (RL) to enable a closed-loop classification approach that divides the entire classification process into episodes, each involving two elephant models. One predicts elephants and is retrained by a selection of flows automatically labeled online by the other. RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase. Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%, and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1091-1102"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925895","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-saving control strategy for ultra-dense network base stations based on multi-agent reinforcement learning","authors":"Yan Zhen ,&nbsp;Litianyi Tao ,&nbsp;Dapeng Wu ,&nbsp;Tong Tang ,&nbsp;Ruyan Wang","doi":"10.1016/j.dcan.2024.10.015","DOIUrl":"10.1016/j.dcan.2024.10.015","url":null,"abstract":"<div><div>Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network (UDN) and focuses on solving the resulting challenge of increased energy consumption. A base station control algorithm based on Multi-Agent Proximity Policy Optimization (MAPPO) is designed. In the constructed 5G UDN model, each base station is considered as an agent, and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance. To reduce the extra power consumption due to frequent sleep mode switching of base stations, a sleep mode switching decision algorithm is proposed. The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent's action strategy. Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1007-1017"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926835","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":"A deep-learning-based MAC for integrating channel access, rate adaptation, and channel switch","authors":"Jiantao Xin ,&nbsp;Wei Xu ,&nbsp;Bin Cao ,&nbsp;Taotao Wang ,&nbsp;Shengli Zhang","doi":"10.1016/j.dcan.2024.10.010","DOIUrl":"10.1016/j.dcan.2024.10.010","url":null,"abstract":"<div><div>With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation, and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switching into DL-MAC, enhancing its functionality from single-channel to multi-channel operations. Specifically, the DL-MAC protocol incorporates a Deep Neural Network (DNN) for channel selection and a Recurrent Neural Network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC. Experimental results demonstrate that DL-MAC exhibits significantly superior performance compared to traditional algorithms in both single and multi-channel environments, and also outperforms single-function designs. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overheads within the evaluation range.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1042-1054"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926841","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":"Cross-chain mapping blockchain: Scalable data management in massive IoT networks","authors":"Wenjian Hu ,&nbsp;Yao Yu ,&nbsp;Xin Hao ,&nbsp;Phee Lep Yeoh ,&nbsp;Lei Guo ,&nbsp;Yonghui Li","doi":"10.1016/j.dcan.2024.11.001","DOIUrl":"10.1016/j.dcan.2024.11.001","url":null,"abstract":"<div><div>We propose a Cross-Chain Mapping Blockchain (CCMB) for scalable data management in massive Internet of Things (IoT) networks. Specifically, CCMB aims to improve the scalability of securely storing, tracing, and transmitting IoT behavior and reputation data based on our proposed cross-mapped Behavior Chain (BChain) and Reputation Chain (RChain). To improve off-chain IoT data storage scalability, we show that our lightweight CCMB architecture efficiently utilizes available fog-cloud resources. The scalability of on-chain IoT data tracing is enhanced using our Mapping Smart Contract (MSC) and cross-chain mapping design to perform rapid Reputation-to-Behavior (R2B) traceability queries between BChain and RChain blocks. To maximize off-chain to on-chain throughput, we optimize the CCMB block settings and producers based on a general Poisson Point Process (PPP) network model. The constrained optimization problem is formulated as a Markov Decision Process (MDP), and solved using a dual-network Deep Reinforcement Learning (DRL) algorithm. Simulation results validate CCMB's scalability advantages in storage, traceability, and throughput. In specific massive IoT scenarios, CCMB can reduce the storage footprint by 50% and traceability query time by 90%, while improving system throughput by 55% compared to existing benchmarks.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1125-1140"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926845","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":"Generalized spatial modulation detector assisted by reconfigurable intelligent surface based on deep learning","authors":"Chiya Zhang ,&nbsp;Qinggeng Huang ,&nbsp;Chunlong He ,&nbsp;Gaojie Chen ,&nbsp;Xingquan Li","doi":"10.1016/j.dcan.2024.11.015","DOIUrl":"10.1016/j.dcan.2024.11.015","url":null,"abstract":"<div><div>Reconfigurable Intelligent Surface (RIS) is regarded as a cutting-edge technology for the development of future wireless communication networks with improved frequency efficiency and reduced energy consumption. This paper proposes an architecture by combining RIS with Generalized Spatial Modulation (GSM) and then presents a Multi-Residual Deep Neural Network (MR-DNN) scheme, where the active antennas and their transmitted constellation symbols are detected by sub-DNNs in the detection block. Simulation results demonstrate that the proposed MR-DNN detection algorithm performs considerably better than the traditional Zero-Forcing (ZF) and the Minimum Mean Squared Error (MMSE) detection algorithms in terms of Bit Error Rate (BER). Moreover, the MR-DNN detection algorithm has less time complexity than the traditional detection algorithms.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1173-1180"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926849","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":"Interest-aware joint caching, computing, and communication optimization for mobile VR delivery in MEC networks","authors":"Baojie Fu ,&nbsp;Tong Tang ,&nbsp;Dapeng Wu ,&nbsp;Ruyan Wang","doi":"10.1016/j.dcan.2024.10.018","DOIUrl":"10.1016/j.dcan.2024.10.018","url":null,"abstract":"<div><div>In the upcoming B5G/6G era, Virtual Reality (VR) over wireless has become a typical application, which is an inevitable trend in the development of video. However, in immersive and interactive VR experiences, VR services typically exhibit high delay, while simultaneously posing challenges for the energy consumption of local devices. To address these issues, this paper aims to improve the performance of VR service in the edge-terminal cooperative system. Specifically, we formulate a joint Caching, Computing, and Communication (3C) VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices. To design the optimal VR service policy, the optimization problem is decoupled into three independent subproblems to be solved separately. To improve the caching efficiency within the network, a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior. Based on this, a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users. Then, the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy. With alternative optimization, an optimal policy for joint 3C based on user interest can be finally obtained. Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness. Our code is available at <span><span>https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1103-1113"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925843","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":"A knowledge graph-based reinforcement learning approach for cooperative caching in MEC-enabled heterogeneous networks","authors":"Dan Wang,&nbsp;Yalu Bai,&nbsp;Bin Song","doi":"10.1016/j.dcan.2024.12.006","DOIUrl":"10.1016/j.dcan.2024.12.006","url":null,"abstract":"<div><div>Existing wireless networks are flooded with video data transmissions, and the demand for high-speed and low-latency video services continues to surge. This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes. Recently, Multi-access Edge Computing (MEC)-enabled heterogeneous networks, which leverage edge caches for proximity delivery, have emerged as a promising solution to all of these problems. Designing an effective edge caching scheme is critical to its success, however, in the face of limited resources. We propose a novel Knowledge Graph (KG)-based Dueling Deep Q-Network (KG-DDQN) for cooperative caching in MEC-enabled heterogeneous networks. The KG-DDQN scheme leverages a KG to uncover video relations, providing valuable insights into user preferences for the caching scheme. Specifically, the KG guides the selection of related videos as caching candidates (i.e., actions in the DDQN), thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN. Extensive simulation results validate the convergence effectiveness of the KG-DDQN, and it also outperforms baselines regarding cache hit rate and service delay.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 4","pages":"Pages 1237-1245"},"PeriodicalIF":7.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926715","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}