Journal of Information and Intelligence最新文献

{"title":"Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication, sensing and computing","authors":"Xinrui Gu,&nbsp;Shengfeng Wang,&nbsp;Zhiqing Wei,&nbsp;Zhiyong Feng","doi":"10.1016/j.jiixd.2024.02.002","DOIUrl":"10.1016/j.jiixd.2024.02.002","url":null,"abstract":"<div><p>The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 4","pages":"Pages 325-338"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000076/pdfft?md5=a20a6663048dfba2bb125f3763e3db1b&pid=1-s2.0-S2949715924000076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks","authors":"Zhenyu Li ,&nbsp;Yuchuan Fu ,&nbsp;Mengqiu Tian ,&nbsp;Changle Li","doi":"10.1016/j.jiixd.2024.02.006","DOIUrl":"https://doi.org/10.1016/j.jiixd.2024.02.006","url":null,"abstract":"<div><p>The combination of integrated sensing and communication (ISAC) with mobile edge computing (MEC) enhances the overall safety and efficiency for vehicle to everything (V2X) system. However, existing works have not considered the potential impacts on base station (BS) sensing performance when users offload their computational tasks via uplink. This could leave insufficient resources allocated to the sensing tasks, resulting in low sensing performance. To address this issue, we propose a cooperative power, bandwidth and computation resource allocation (RA) scheme in this paper, maximizing the overall utility of Cramér-Rao bound (CRB) for sensing accuracy, computation latency for processing sensing information, and communication and computation latency for computational tasks. To solve the RA problem, a twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to explore and obtain the effective solution of the RA problem. Furthermore, we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks, as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations. Simulation demonstrates that compared to other benchmark methods, TD3 achieves an average utility improvement of 97.11% and 27.90% in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 4","pages":"Pages 339-354"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000118/pdfft?md5=7da67833638c1d345742ffcf41afbff6&pid=1-s2.0-S2949715924000118-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A statistical sensing method by utilizing Wi-Fi CSI subcarriers: Empirical study and performance enhancement","authors":"Tao Deng ,&nbsp;Bowen Zheng ,&nbsp;Rui Du ,&nbsp;Fan Liu ,&nbsp;Tony Xiao Han","doi":"10.1016/j.jiixd.2024.05.002","DOIUrl":"10.1016/j.jiixd.2024.05.002","url":null,"abstract":"<div><p>In modern Wi-Fi systems, channel state information (CSI) serves as a foundational support for various sensing applications. Currently, existing CSI-based techniques exhibit limitations in terms of environmental adaptability. As such, optimizing the utilization of subcarrier CSI stands as a critical avenue for enhancing sensing performance. Within the OFDM communication framework, this work derives sensing outcomes for both detection and estimation by harnessing the CSI from every individual measured subcarrier, subsequently consolidating these outcomes. When contrasted against results derived from CSI based on specific extraction protocols or those obtained through weighted summation, the methodology introduced in this study offers substantial improvements in CSI-based detection and estimation performance. This approach not only underscores the significance but also serves as a robust exemplar for the comprehensive application of CSI.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 4","pages":"Pages 365-374"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000374/pdfft?md5=90504ae06b478f8e48c78218ff4dd240&pid=1-s2.0-S2949715924000374-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based fall detection using commodity Wi-Fi","authors":"Tingwei Chen ,&nbsp;Xiaoyang Li ,&nbsp;Hang Li ,&nbsp;Guangxu Zhu","doi":"10.1016/j.jiixd.2024.04.001","DOIUrl":"10.1016/j.jiixd.2024.04.001","url":null,"abstract":"<div><p>As the phenomenon of an aging population gradually becomes common worldwide, the pressure on the elderly has seen a notable increase. To address this challenge, fall detection systems are important in ensuring the safety of the elderly population, particularly those living alone. Wi-Fi sensing, as a privacy-preserving method of perception, can be deployed indoors for detecting human activities such as falls, based on the reflective properties of electromagnetic waves. Signals generated by transmitters experience reflections from various objects within indoor environments, leading to distinct propagation paths. These signals eventually aggregate at the receiver, incorporating details about the objects’ orientation and their activity states. In this study, within practical experimental environments, we collect dataset and utilize deep learning method to classify the falling events.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 4","pages":"Pages 355-364"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000283/pdfft?md5=f31939e6bf88241fc2bd69185c959aa9&pid=1-s2.0-S2949715924000283-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140775565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing","authors":"Ruijin Sun ,&nbsp;Yao Wen ,&nbsp;Nan Cheng ,&nbsp;Wei Wang ,&nbsp;Rong Chai ,&nbsp;Yilong Hui","doi":"10.1016/j.jiixd.2024.02.005","DOIUrl":"10.1016/j.jiixd.2024.02.005","url":null,"abstract":"<div><p>Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). With this paradigm, vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading. By optimizing the computation mode and network resources, in this paper, we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task. Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. To tackle this challenge, we propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks. Specifically, borrowing the iterative structure of the AM algorithm, also referred to as structural knowledge, the proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning to learn an adaptive optimizer for updating variables in each sub-problem, instead of the handcrafted counterpart in the AM algorithm. Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 4","pages":"Pages 302-324"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000106/pdfft?md5=40b4034f42d124042f5327bc76eb93ca&pid=1-s2.0-S2949715924000106-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140433037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification","authors":"Yu Liu ,&nbsp;Caihong Mu ,&nbsp;Shanjiao Jiang ,&nbsp;Yi Liu","doi":"10.1016/j.jiixd.2024.06.001","DOIUrl":"10.1016/j.jiixd.2024.06.001","url":null,"abstract":"<div><div>Although the deep-learning method has achieved great success for hyperspectral image (HSI) classification, the few-shot HSI classification deserves sufficient study because it is difficult and expensive to acquire labeled samples. In fact, the meta-learning methods can improve the performance for few-shot HSI classification effectively. However, most of the existing meta-learning methods for HSI classification are supervised, which still heavily rely on the labeled data for meta-training. Moreover, there are many cross-scene classification tasks in the real world, and domain adaptation of unsupervised meta-learning has been ignored for HSI classification so far. To address the above issues, this paper proposes an unsupervised meta-learning method with domain adaptation based on a multi-task reconstruction-classification network (MRCN) for few-shot HSI classification. MRCN does not need any labeled data for meta-training, where the pseudo labels are generated by multiple spectral random sampling and data augmentation. The meta-training of MRCN jointly learns a shared encoding representation for two tasks and domains. On the one hand, we design an encoder-classifier to learn the classification task on the source-domain data. On the other hand, we devise an encoder-decoder to learn the reconstruction task on the target-domain data. The experimental results on four HSI datasets demonstrate that MRCN preforms better than several state-of-the-art methods with only two to five labeled samples per class. To the best of our knowledge, the proposed method is the first unsupervised meta-learning method that considers the domain adaptation for few-shot HSI classification.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 2","pages":"Pages 103-112"},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PGCF: Perception graph collaborative filtering for recommendation","authors":"Caihong Mu ,&nbsp;Keyang Zhang ,&nbsp;Jiashen Luo ,&nbsp;Yi Liu","doi":"10.1016/j.jiixd.2024.05.003","DOIUrl":"10.1016/j.jiixd.2024.05.003","url":null,"abstract":"<div><div>Extensive studies have fully proved the effectiveness of collaborative filtering (CF) recommendation models based on graph convolutional networks (GCNs). As an advanced interaction encoder, however, GCN-based CF models do not differentiate neighboring nodes, which will lead to suboptimal recommendation performance. In addition, most GCN-based CF studies pay insufficient attention to the loss function and they simply select the Bayesian personalized ranking (BPR) loss function to train the model. However, we believe that the loss function is as important as the interaction encoder and deserves more attentions. To address the above issues, we propose a novel GCN-based CF model, named perception graph collaborative filtering (PGCF). Specifically, for the interaction encoder, we design a neighborhood-perception GCN to enhance the aggregation of interest-related information of the target node during the information aggregation process, while weakening the propagation of noise and irrelevant information to help the model learn better embedding representation. For the loss function, we design a margin-perception Bayesian personalized ranking (MBPR) loss function, which introduces a self-perception margin, requiring the predicted score of the user-positive sample to be greater than that of the user-negative sample, and also greater than the sum of the predicted score of the user-negative sample and the margin. The experimental results on five benchmark datasets show that PGCF is significantly superior to multiple existing CF models.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 6","pages":"Pages 525-534"},"PeriodicalIF":0.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental full-duplex amplify-and-forward relay scheme for OFDM with power gain control","authors":"","doi":"10.1016/j.jiixd.2024.05.001","DOIUrl":"10.1016/j.jiixd.2024.05.001","url":null,"abstract":"<div><p>The fundamental challenges for full-duplex (FD) relay networks are the self-interference cancellation (SIC) and the cooperative transmission design at the relay. This paper presents a practical amplify-and-forward (AF) FD one-way relay scheme for orthogonal frequency division multiplexing (OFDM) transmission with multi-domain SIC. It is found that the residual self-interference (SI) signals at the relay can be regarded as an equivalent multipath model. The effects of the residual SI at the relay are incorporated into the equivalent end-to-end channel model, and the inter-block interference can be removed at the destination by using cyclic prefix (CP) protection. Based on the equivalent multipath model, we present a solution for optimizing the amplification factor on the performance of signal-to-interference-plus-noise ratio (SINR) when the equivalent multipath length is longer than the CP. Furthermore, a practical one way FD relay network with 3 nodes is built and measured. The simulation and measured results verify the superior performance of the proposed scheme.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 5","pages":"Pages 375-387"},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000362/pdfft?md5=fe196ceb9fd9ca78d469646703de4d63&pid=1-s2.0-S2949715924000362-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight and efficient raw data collection scheme for IoT systems","authors":"Yixuan Huang ,&nbsp;Yining Liu ,&nbsp;Jingcheng Song ,&nbsp;Weizhi Meng","doi":"10.1016/j.jiixd.2024.03.004","DOIUrl":"10.1016/j.jiixd.2024.03.004","url":null,"abstract":"<div><p>With the prevalence of Internet of Things (IoT) devices, data collection has the potential to improve people's lives and create a significant value. However, it also exposes sensitive information, which leads to privacy risks. An approach called N-source anonymity has been used for privacy preservation in raw data collection, but most of the existing schemes do not have a balanced efficiency and robustness. In this work, a lightweight and efficient raw data collection scheme is proposed. The proposed scheme can not only collect data from the original users but also protect their privacy. Besides, the proposed scheme can resist user poisoning attacks, and the use of the reward method can motivate users to actively provide data. Analysis and simulation indicate that the proposed scheme is safe against poison attacks. Additionally, the proposed scheme has better performance in terms of computation and communication overhead compared to existing methods. High efficiency and appropriate incentive mechanisms indicate that the scheme is practical for IoT systems.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 3","pages":"Pages 209-223"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000271/pdfft?md5=51591f30faf1b37d53c6c14d9cec3ea7&pid=1-s2.0-S2949715924000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification","authors":"Aamir Ali ,&nbsp;Caihong Mu ,&nbsp;Zeyu Zhang ,&nbsp;Jian Zhu ,&nbsp;Yi Liu","doi":"10.1016/j.jiixd.2024.03.002","DOIUrl":"10.1016/j.jiixd.2024.03.002","url":null,"abstract":"<div><p>In the field of hyperspectral image (HSI) classification in remote sensing, the combination of spectral and spatial features has gained considerable attention. In addition, the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs, capable of capturing a large amount of intrinsic information. However, some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales, leading to low classification results, and dense-connection based methods enhance the feature propagation at the cost of high model complexity. This paper presents a two-branch multiscale spectral-spatial feature extraction network (TBMSSN) for HSI classification. We design the multiscale spectral feature extraction (MSEFE) and multiscale spatial feature extraction (MSAFE) modules to improve the feature representation, and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial features at multiscale. Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness, alleviate the vanishing-gradient problem and strengthen the feature propagation. To evaluate the effectiveness of the proposed method, the experimental results were carried out on bench mark HSI datasets, demonstrating that TBMSSN obtained higher classification accuracy compared with several state-of-the-art methods.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 3","pages":"Pages 224-235"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000167/pdfft?md5=d4d6ccaef4c80b7e55681a18aea7102b&pid=1-s2.0-S2949715924000167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140280893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}