IET Communications最新文献

{"title":"Digital twin assisted multi-task offloading for vehicular edge computing under SAGIN with blockchain","authors":"Qiyong Chen,&nbsp;Chunhai Li,&nbsp;Mingfeng Chen,&nbsp;Maoqiang Wu,&nbsp;Gen Zhang","doi":"10.1049/cmu2.70002","DOIUrl":"https://doi.org/10.1049/cmu2.70002","url":null,"abstract":"<p>To better provide fast computing services, vehicular edge computing can improve the quality of service and quality of experience for intelligent transportation in 6G by reducing task transmission delay. However, vehicular edge networks face network capability limitations and privacy issues in practice. High-speed vehicles and the time-varying environment make them unpredictable. In the meantime, smart vehicles with distinct computation capabilities need to process various tasks with different resource requirements, which will inevitably cause untimely task offloading and massive energy consumption. This paper proposes to use the space-air-ground integrated network with blockchain to enhance the network capability and the privacy protection of vehicular edge networks. The digital twin is taken to better capture the dynamic characteristics of vehicles and the entire environment. The urgency level is introduced to meet the delay requirements of different tasks, while considering the impact of digital twin deviation on task offloading. Moreover, the selection algorithm and the task distribution algorithm based on the improved genetic algorithmare are proposed to obtain the optimal offloading strategy. Simulation results demonstrate that, compared with the existing algorithms, the proposed scheme can maximize the system utility while diminishing the total time for task processing.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 social relationship-aware collaborative D2D secure caching strategy","authors":"Songnong Li,&nbsp;Yongliang Ji,&nbsp;Wenxin Peng,&nbsp;Haoreng Dai,&nbsp;Jinyang Jiang","doi":"10.1049/cmu2.70009","DOIUrl":"https://doi.org/10.1049/cmu2.70009","url":null,"abstract":"<p>In response to the issue of wireless spectrum scarcity and secure transmission caused by frequent requests for short videos from a large number of devices within the network, this article proposes a social relationship-aware collaborative Device-to-Device (D2D) secure caching strategy. By thoroughly analyzing the physical and social characteristics of devices, a collaborative secure caching policy based on user social relationships is designed, aiming to enhance the willingness of end users to share, reduce network traffic loads, and decrease transmission latency. To maximize traffic offloading, users are divided into communities based on their physical-social attributes, grouping users with strong physical-social attributes in the same community to increase their sharing willingness. For users within the community, relay nodes are chosen for users according to the contribution of the cached content to the community, and personalized video content caching placement is further realized based on the video content popularity predicted by the collaborative filtering algorithm to meet user demands. Simulation results suggest that the proposed algorithm can improve traffic offloading and reduce transmission latency.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 novel distinguishing attack on Rocca","authors":"Nan Liu,&nbsp;Chenhui Jin,&nbsp;Junwei Yu,&nbsp;Jie Guan,&nbsp;Ting Cui","doi":"10.1049/cmu2.70008","DOIUrl":"https://doi.org/10.1049/cmu2.70008","url":null,"abstract":"&lt;p&gt;The security of an encryption algorithm often hinges on the indistinguishability between ciphertext and random numbers. In block ciphers, pseudorandomness and super-pseudorandomness are commonly used to depict the indistinguishability between ciphertext and random numbers. Whereas, stream cipher algorithms can be viewed as functions of variables such as the key K, initialization vector IV, and plaintext M. For an ideal stream cipher algorithm, the ciphertext sequences for any two different plaintexts should exhibit good independence across various K-IV pairs. Consequently, the probability of the two ciphertext sequences being equal or having sufficiently long matching segments should be negligible. This study examines a new type of attack that stems from key commitment attacks. By exploiting the independence between the ciphertext sequences, a novel distinguishing attack against the stream cipher is constructed and applied to the encryption of Rocca. Focusing on the authenticated encryption algorithm Rocca, a guess-and-determine method is employed to demonstrate that for any plaintext and two different sets of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;mi&gt;o&lt;/mi&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mi&gt;c&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$( {key,Nonce,AD} )$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, another plaintext can be found with a time complexity &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mn&gt;160&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$O( {{{2}^{160}}} )$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, resulting in identical ciphertext sequences except for the initial &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;4&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mn&gt;256&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$4 times 256$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; bits. Furthermore, it is proved that under chosen plaintext conditions, the time complexity of this distinguishing attack is &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 ","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Green communication for OverGNN enabled heterogeneous ultra-dense networks","authors":"Jun Zeng,&nbsp;Sisi Lin,&nbsp;Yuhan Ai,&nbsp;Guo Wan,&nbsp;Wei Luo,&nbsp;Qimei Chen","doi":"10.1049/cmu2.70005","DOIUrl":"https://doi.org/10.1049/cmu2.70005","url":null,"abstract":"<p>With the development of wireless communications, heterogeneous ultra-dense networks (HUDNs) have emerged to meet the requirements of massive connectivity, high data rate, and low latency in the 5G era. Nevertheless, HUDN usually leads to a high-complexity and non-convex NP-hard energy-efficient resource allocation problem. Therefore, A novel heterogeneous Graph neural network (GNN) with high-dimensional computation structure (namely OverGNN) is proposed for the power allocation problem in this work. Particularly, OverGNN enabled nodes directly interact with high-order neighbours and extract abundant graph topological information, which can facilitate effective feature aggregation among nodes as well as alleviate the over-smoothing problem. Based on this fact, an efficient message passing scheme for user equipments under the same base station is developed to approximate the optimal power allocation strategy for maximizing system energy efficiency. In addition, an unsupervised approach is proposed to train the GNN model that can reduce the cost of dataset collection and enhance the scalability of the proposed method. Numerical results verify the effectiveness of the proposed OverGNN and demonstrate its advantages over the benchmarks.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Real-world UAV recognition based on radio frequency fingerprinting with transformer","authors":"Jia Han,&nbsp;Zhiyong Yu,&nbsp;Jian Yang","doi":"10.1049/cmu2.70004","DOIUrl":"https://doi.org/10.1049/cmu2.70004","url":null,"abstract":"<p>Many unmanned aerial vehicles (UAVs) require the installation of automatic dependent surveillance-broadcast (ADS-B) transponders to facilitate their daily management. However, since ADS-B transponders do not have a good security mechanism, they introduce problems including impersonation, spoofing, and private changing of the registration number, making UAV surveillance inconvenient. Radio frequency fingerprinting (RFF) recognition is carried out by utilizing the fact that different electronic devices in a given transponder will affect the transmitted signals, resulting in the formation of RFF features that are unique to the transponder and difficult to forge. Therefore, in this work, a deep learning architecture is proposed to classify UAVs based on ADS-B signals, and a multi-head self-attention RFF recognition model is constructed using variational mode decomposition (VMD) of the preamble data and a transformer encoder for validation. The model achieves better results in terms of noise, Doppler shifting, and multipath effect interference. This method demonstrates that the transformer architecture of natural language processing, combined with appropriate data preprocessing methods, can also be used for RFF recognition, and provides advantages in accuracy and robustness (67.83% vs. 64.17%).</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"QoE-based dynamic resource allocation for heterogeneous smart distribution grids","authors":"You Li,&nbsp;Yan Huo,&nbsp;Zhongguo Zhou,&nbsp;Qinghe Gao,&nbsp;Tao Jing,&nbsp;Tao Yan,&nbsp;Sisi Xiao","doi":"10.1049/cmu2.70001","DOIUrl":"https://doi.org/10.1049/cmu2.70001","url":null,"abstract":"<p>In the realm of conventional smart distribution grid resource allocation, the prevalent issue resides in its narrow focus on base station capacity, striving to optimize resource allocation for base station communication while disregarding the genuine requirements on the user side. This inadvertently leads to excessive squandering of wireless resources, despite already fulfilling the fundamental service demands of terminal operations. This article, while taking into account the capacity of base stations, introduces an innovative approach by amalgamating the terminal operations concerning data rate, latency, and packet loss rate. Through the construction of a Quality of Experience (QoE) evaluation framework, a scenario is realized within which user experience requirements are ensured by terminals in various practical settings of smart distribution grids, without wireless resources being needlessly dissipated. In this article, the dynamic resource allocation is tackled using Deep Q-Networks (DQN), while the reward function is formulated based on QoE. The simulation results, which track the accumulation of reward values throughout the entire operational process, provide substantial validation for the effectiveness and practicality of the ultimately formulated dynamic resource allocation scheme.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Effective neural coding method based on maximum entropy","authors":"Dongbin He,&nbsp;Aiqun Hu,&nbsp;Kaiwen Sheng","doi":"10.1049/cmu2.70000","DOIUrl":"https://doi.org/10.1049/cmu2.70000","url":null,"abstract":"<p>There are a large number of perceptrons in the new bionic network. To improve the efficiency of data transmission in the bionic network, a maximum entropy neural coding method is proposed. By drawing on the characteristics of human nerve conduction, the authors designed a data transmission model and adopted an adaptive spike firing rate encoding strategy to maximize information entropy, thereby improving encoding efficiency. The simulation experiment results and the applications of the maximum entropy neural coding method to fault detection and seismic detection have validated the effectiveness of the maximum entropy neural coding method. Even if there is certain data distortion, the statistical characteristics of the decoded data and the fault detection performance will not be affected. This research not only proposes novel approaches for efficient data transmission in bionic network, but also identifies possible directions for enhancing data transmission efficiency through the integration of task-oriented semantic communications in future applications.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"AoI-optimal path planning for UAV-assisted data collection with heterogeneous information aging speed","authors":"Chen Xie,&nbsp;Binbin Wu,&nbsp;Zihao Pan,&nbsp;Daoxing Guo,&nbsp;Wenfeng Ma","doi":"10.1049/cmu2.12768","DOIUrl":"https://doi.org/10.1049/cmu2.12768","url":null,"abstract":"<p>This article investigates a problem involving the collection of data from sensor nodes (SNs) with heterogeneous information aging speed (IAS) by an unmanned aerial vehicle (UAV) in a dense obstacle environment. The objective is to minimize the average age of information (AoI) of the SNs through UAV path planning. The problem is challenging due to the tight coupling of obstacle avoidance, information timeliness, and the heterogeneity of SNs. Directly solving this path planning problem is difficult, and the conventional approach involves planning the access sequence without considering obstacle avoidance and then optimizing the UAV trajectory while incorporating safety constraints. However, optimizing the trajectory for safe flight introduces changes in the flight time cost, resulting in the average AoI not reaching its minimum value. To address this, a UAV safe flight network is first established by generating trajectories using a combination of A*-based and successive convex approximation (SCA)-based algorithms. Subsequently, a genetic algorithm (GA)-based method is employed and compared with the time greedy strategy. The numerical results demonstrate that the time greedy strategy, which aligns with intuitive understanding, can achieve a smaller total UAV flight time, while the proposed method effectively minimizes the average AoI of SNs.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.12768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 neural coding method based on feature sensing","authors":"Dongbin He,&nbsp;Aiqun Hu,&nbsp;Kaiwen Sheng","doi":"10.1049/cmu2.12882","DOIUrl":"https://doi.org/10.1049/cmu2.12882","url":null,"abstract":"<p>The novel network contains many sensors, which greatly heightens data transmission burdens. Some networks require the data perceived by sensors for a period to make decisions. Drawing inspiration from the human neural conduction mechanism, a waveform data encoding method called feature sensing neural coding (FSNC) is proposed to enhance network data transmission efficiency. It involves feature decomposition of information and subsequent non-linear encoding of feature coefficients for data transmission. This approach exploits the unique neuronal responses to diverse stimuli and the inherent non-linear characteristics of human neural coding. Finally, taking the speech signal and seismic wave signal as examples, the effectiveness of FSNC is verified by simulating the auditory nerve conduction process with frequency as a feature according to the mechanism of travelling wave motion of the basilar membrane in the cochlea. Moreover, experiments on seismic waveform signals have demonstrated the wide applicability of FSNC. Compared with traditional speech coding schemes, the FSNC bit rate is only 6.4 kbps, which greatly reduces the amount of data transmitted. Not only that, FSNC also has a certain fault tolerance, and parallel transmission can also greatly increase the transmission rate. This research provides new ideas for efficient data transmission over new networks.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.12882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Research on automatic modulation recognition of shortwave signals in few-shot scenarios based on knowledge distillation","authors":"Qi Yao,&nbsp;Jingjing Yang,&nbsp;Ming Huang","doi":"10.1049/cmu2.12881","DOIUrl":"https://doi.org/10.1049/cmu2.12881","url":null,"abstract":"<p>Automatic modulation recognition plays an important role in wireless communication and radio regulation. Existing deep learning-based automatic modulation recognition techniques perform well with large datasets and high computational power but require significant resources for labelled data and complex pre-processing. This paper proposes a multi-information fusion method for few-shot modulation recognition, which involves converting <i>I</i>/<i>Q</i> signals into <i>A</i>/<i>P</i> signals and training with a combination of VGG and LSTM network models. An ensemble knowledge distillation (EKD) approach is employed to streamline the network model, meeting the demands for deploying neural network models on compact devices. Experimental results demonstrate that using only 1% of the shortwave modulation signal dataset as the training set, the proposed model achieves an average classification accuracy of 71.08% under all signal-to-noise ratios, surpassing the currently popular deep learning models. Moreover, two small-scale networks, MobileNetV3 and convolutional neural network are trained, through EKD. Compared to the teacher network, the floating-point operations of the distilled models are reduced by 99.8% and 99.7%, respectively, and the average prediction accuracy only decreases by 16.05% and 8.09%. The lightweight, few-shot networks designed in this study for shortwave modulation signals aim to achieve fast and accurate modulation recognition on compact devices.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.12881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}