IEEE Transactions on Mobile Computing最新文献

{"title":"Hybrid Transformer Based Multi-Agent Reinforcement Learning for Multiple Unpiloted Aerial Vehicle Coordination in Air Corridors","authors":"Liangkun Yu;Zhirun Li;Nirwan Ansari;Xiang Sun","doi":"10.1109/TMC.2025.3532204","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532204","url":null,"abstract":"Advanced Air Mobility (AAM) seeks to establish a next-generation air transportation system by leveraging autonomous unpiloted aerial vehicles (UAVs) to transport passengers and cargo between locations previously underserved or unserved by traditional aviation. Achieving AAM at scale requires overcoming significant challenges in airspace management, classification, and traffic control to safely accommodate the increasing volume of UAV operations. This paper presents a comprehensive design for air corridors to facilitate efficient aerial transport and formulates a multi-UAV coordination problem within these corridors. The objective is to enable each UAV to autonomously make control decisions based on local observations gathered from onboard sensors. This decentralized control approach is modeled as a multi-agent partially observable Markov decision process (POMDP), aiming at minimizing UAV travel time while ensuring adherence to corridor boundaries and collision avoidance. To address the complexities posed by varying state dimensions and types, we propose a novel Hybrid Transformer-based Multi-agent Reinforcement Learning (HTransRL) architecture. HTransRL integrates a customized transformer model into an actor-critic network, effectively processing both sequential and non-sequential observed states of varying sizes while capturing their correlations. This enables safe and efficient UAV navigation. Simulation results show that in test environments similar to or simpler than training scenarios, HTransRL achieves a successful arrival rate exceeding 90% in worst-case test scenarios. In test environments more complex than training scenarios, HTransRL demonstrates superior scalability compared to two baseline methods, achieving higher arrival rates and comparable travel times.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5482-5495"},"PeriodicalIF":7.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929726","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":"Causal Analysis and Risk Assessment for Batch Crowdsourcing","authors":"Ke Chao;Shengling Wang;Hongwei Shi;Jianhui Huang;Xiuzhen Cheng","doi":"10.1109/TMC.2025.3532285","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532285","url":null,"abstract":"The way of task posting serves as the main pillar in achieving an efficient crowdsourcing market. Pioneer solutions on task posting can be categorized as retail task posting and batch task posting. Unlike retail task posting, which simply matches the most suitable worker to tasks, batch task posting considers the collaborations not only between workers and tasks but also among tasks, which brings high efficiency, low costs, and satisfactory task completion rates. However, the state of the arts on batch task posting leverage specific attributes to combine tasks as bundles for posting, leading to limited scalability. Hence, we propose a causal analysis framework for batch crowdsourcing to achieve an attribute-independent batch crowdsourcing solution that disentangles multi-factors to uncover the posting merits of tasks bundled at optimal prices, based on which an approximately optimal algorithm is further introduced to form reasonable bundles for posting. Since batch crowdsourcing may incur losses due to short-term profit fluctuation, a risk assessment method is proposed to encourage the requestor to act properly for loss mitigations. Our work explores the causal analysis and risk assessment in batch crowdsourcing for the first time, with the following highlights: 1) <italic>generality</i>. It proposes a composite metric for gauging task bundles which avoids the issue of attribute dependence in the state of the arts, resulting in better universality; 2) <italic>synergy</i>. By collaboratively considering the “value” and “relative position” of variables, our work derives results reflecting causal relationships rather than naive correlations; and 3) <italic>precision</i>. We not only elucidate the probability of risk in batch crowdsourcing but also delineate the rate function governing its probability decay. This allows a requestor to know when and how fast to halt batch task posting.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5312-5323"},"PeriodicalIF":7.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918681","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":"Cost-Effective Edge Data Caching With Failure Tolerance and Popularity Awareness","authors":"Ruikun Luo;Zujia Zhang;Qiang He;Mengxi Xu;Feifei Chen;Xiaohai Dai;Song Wu;Hai Jin","doi":"10.1109/TMC.2025.3531967","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531967","url":null,"abstract":"In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing <italic>edge data caching</i> (EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable <italic>uncertainty-aware edge data caching</i> (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5357-5369"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10847869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918776","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":"Utility-Enhanced Personalized Privacy Preservation in Hierarchical Federated Learning","authors":"Jianan Chen;Honglu Jiang;Qin Hu","doi":"10.1109/TMC.2025.3531919","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531919","url":null,"abstract":"Federated learning (FL) is a distributed learning framework that allows clients to jointly train a model by uploading parameter updates rather than sharing local data. FL deployed on a client-edge-cloud hierarchical architecture, named Hierarchical Federated Learning (HFL), can accelerate model training and accommodate more clients with reduced communication cost via edge aggregation. Unfortunately, HFL suffers from privacy risks since the submitted parameters from clients are vulnerable to privacy attacks. To address this issue, we propose a novel Differential Privacy (DP) definition tailored for HFL, i.e., Group Local Differential Privacy (GLDP). We design the Sampling-Randomizing-Shuffling (SRS) mechanism to implement GLDP in HFL, where the sampling process is employed to achieve a stronger level of privacy protection with less noise added. By combining the randomized response and the shuffling mechanism, our proposed SRS mechanism can achieve client-level personalization within <inline-formula><tex-math>$rho _{k}$</tex-math></inline-formula>-GLDP for privacy preservation while balancing model performance and privacy protection in HFL. Privacy analysis and convergence analysis are conducted to provide theoretical performance guarantees. Experimental results based on real-world datasets verify the effectiveness of SRS.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5264-5279"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918775","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":"Towards Resilience 5G-V2N: Efficient and Privacy-Preserving Authentication Protocol for Multi-Service Access and Handover","authors":"Ye Bi;Chunfu Jia","doi":"10.1109/TMC.2025.3532120","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532120","url":null,"abstract":"The booming 5G cellular networks sparked tremendous interest in supporting more sophisticated critical use cases through vehicle-to-network (V2N) communications. However, the inherent technical vulnerabilities and densification of 5G raise new security and efficiency challenges. The existing secondary authentication fails to support multi-service access. The random access process lacks authentication of the gNB, possibly leading to fake base station attacks (FBS). Moreover, related research extends key forward/backward secrecy (KF/BS) to require that it also applies to gNBs, thus invalidating most existing schemes. This paper introduces a comprehensive security framework for 5G-V2N that seamlessly integrates with existing standardized architecture to provide privacy-preserving mutual authentication and key agreement for the full service cycle. Specifically, we propose new secondary authentication involving gNBs and support single request access to multi-services. Second, incorporating the service migration idea, we design the g2g (gNB-to-gNB) channel establishment phase to promote secure context share. Finally, the proposed efficient handover phase achieves the security properties of enhanced KF/BS, known randomness secrecy and privacy-preserving, and avoids FBS. We verify the proposed protocol using three different formal techniques: provably secure, BAN-logic, and AVISPA tool. Extensive experimental results and comparison show that our scheme excels in computational and communication efficiencies, and detecting malicious events.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5446-5463"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929816","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":"DeExp: Revealing Model Vulnerabilities for Spatio-Temporal Mobile Traffic Forecasting With Explainable AI","authors":"Serly Moghadas Gholian;Claudio Fiandrino;Narseo Vallina-Rodríguez;Marco Fiore;Joerg Widmer","doi":"10.1109/TMC.2025.3531544","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531544","url":null,"abstract":"The ability to perform mobile traffic forecasting effectively with Deep Neural Networks (DNN) is instrumental to optimize resource management in 5G and beyond generation mobile networks. However, despite their capabilities, these Deep Neural Networks (DNN)s often act as complex opaque-boxes with decisions that are difficult to interpret. Even worse, they have proven vulnerable to adversarial attacks which undermine their applicability in production networks. Unfortunately, although existing state-of-the-art EXplainable Artificial Intelligence (XAI) techniques are often demonstrated in computer vision and Natural Language Processing (NLP), they may not fully address the unique challenges posed by spatio-temporal time-series forecasting models. To address these challenges, we introduce <sc>DeExp</small> in this paper, a tool that flexibly builds upon legacy EXplainable Artificial Intelligence (XAI) techniques to synthesize compact explanations by making it possible to understand which Base Stations (BSs) are more influential for forecasting from a spatio-temporal perspective. Armed with such knowledge, we run state-of-the-art Adversarial Machine Learning (AML) techniques on those BSs to measure the accuracy degradation of the predictors under adversarial attacks. Our comprehensive evaluation uses real-world mobile traffic datasets and demonstrates that legacy XAI techniques spot different types of vulnerabilities. While Gradient-weighted Class Activation Mapping (GC) is suitable to spot BSs sensitive to moderate/low traffic injection, LayeR-wise backPropagation (LRP) is suitable to identify BSs sensitive to high traffic injection. Under moderate adversarial attacks, the prediction error of the BSs identified as vulnerable can increase by more than 250%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5245-5263"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918646","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":"Rethinking the Effect of Sparse Data Completion on Sparse Mobile Crowdsensing Tasks","authors":"Yuanbo Xu;Jiawei Liu;En Wang;Bo Yang;Dongming Luan;Yongjian Yang;Jing Deng","doi":"10.1109/TMC.2025.3531362","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531362","url":null,"abstract":"<monospace>M</monospace>obile <monospace>c</monospace>rowd<monospace>s</monospace>ensing (<monospace>MCS</monospace>) is a powerful technique that enables a variety of urban tasks, including temperature monitoring, location-based services, and urban path recommendations. However, these tasks often face the challenge of sparse and incomplete sensing data, undermining their effectiveness and reliability. <monospace>S</monospace>parse <monospace>d</monospace>ata <monospace>c</monospace>ompletion (<monospace>SDC</monospace>) methods have been developed to infer missing or unobserved data by leveraging spatio-temporal correlations to tackle this issue. This forms the core concept of the <monospace>s</monospace>parse <monospace>m</monospace>obile <monospace>c</monospace>rowd<monospace>s</monospace>ensing problem (<monospace>SMCS</monospace>), which aims to improve the performance of downstream tasks through inferred data. Despite the potential benefits, most existing SMCS methods fail to consider the trade-off between the cost of SDC and the benefits for downstream tasks. These methods often treat SDC and downstream tasks as independent modules, resulting in suboptimal outcomes. In this paper, we investigate the impact of SDC on the SMCS paradigm, both qualitatively and quantitatively. We establish the upper bound of performance achievable when applying SDC in SMCS under different levels of sensing data sparsity. Based on these studies and findings, we propose a practical and flexible framework called <monospace>SDC-EVA</monospace>, <monospace>S</monospace>ensing <monospace>D</monospace>ata <monospace>C</monospace>ompletion <monospace>EVA</monospace>luation framework. This framework allows for applying different SDC methods in SMCS, considering factors such as computing complexity, storage space, and associated costs. Our proposed framework allows researchers to assess the necessity and feasibility of integrating SDC into SMCS systems before designing and deploying them in real-world scenarios. This assessment can be tailored to specific data sparsity and contextual information. To validate the effectiveness of our proposed evaluation framework, we conduct experiments in various real-world scenarios involving different combinations of SDC and downstream tasks. The results demonstrate the superiority of our framework in improving the performance of SMCS. By presenting these findings, we aim to contribute to developing SMCS techniques and provide valuable insights for researchers and practitioners.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5094-5105"},"PeriodicalIF":7.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918797","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":"Enabling Ultralow-Latency Services With Ubiquitous Mobility by Means of a Compact Network Architecture","authors":"Guiliang Cai;Qiang Wu;Ran Wang;Lianyi Zhi;Xiaoming Fu;Hongke Zhang","doi":"10.1109/TMC.2025.3526971","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526971","url":null,"abstract":"With the rapid development of emerging services such as cellular vehicle-to-everything and immersive video service, network connections have further evolved from tangible physical connections to intangible virtual connections such as content, services, and computing resources, and the application scenarios have become more abundant. The mobile ultra-service, which is characterized by ultra-low latency, ultra-high reliability, and ubiquitous mobility, is becoming one of the most representative traffic types. However, the existing mobile network architecture has not evolved sufficiently to meet the specific requirements of these mobile ultra-services, the mobility anchors introduce unnecessary node and link latency, leaving space for further optimization. A compact network architecture (ComArch) is proposed in this paper for ultralow-latency services with ubiquitous mobility. ComArch is designed with a mapping control plane and a generalized forwarding plane to collaboratively implement packet forwarding in mobile scenarios. The generalized forwarding plane handles packet forwarding, while the mapping control plane manages terminals’ identifier and locator mapping entries. The node latency introduced by mobility anchors is eliminated, and an efficient routing scheme is proposed to find the optimal mandatory nodes in the forwarding path, thereby reducing unnecessary link latency. Experimental results show that ComArch can effectively reduce end-to-end delay while saving resources.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"4858-4873"},"PeriodicalIF":7.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925323","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":"nCTX: A Neural Network-Powered Lossless Compressive Transmission Using Shared Information","authors":"Wooseung Nam;Sungyong Lee;Joohyun Lee;Kyunghan Lee","doi":"10.1109/TMC.2025.3530950","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530950","url":null,"abstract":"In this work, we explore the possibility of a new delivery method for lossless data, namely compressive transmission. It aims at minimizing the transmission data volume at runtime by exploiting the tailored information shared between the sender and the receiver. There are two approaches to leverage shared information for compression: 1) using a DNN-based codec as a proxy for shared information and 2) applying redundancy elimination using deduplication. However, these approaches have not been studied in depth to utilize the trade-off between the compression rate and the amount of shared information. Compared to these approaches, compressive transmission is unique as it fully leverages the abundance of information available on both sides, which is chosen and placed purposely. To bring the concept to reality, we propose nCTX, a neural network-powered Compressive Transmission System that adaptively exploits a generative model and matching blocks. nCTX extracts the optimal semantic data from the input data, exploiting shared information to closely imitate the original and compensate it with the offset (i.e., difference). Extensive evaluations in mobile platforms confirm that nCTX reduces the transmission volume significantly by 25.8% and 23.3% compared to FLIF and RC, the state-of-the-art image codecs, respectively, in comparable or shorter computation times.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5386-5399"},"PeriodicalIF":7.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929725","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":"Intelligent End-to-End Deterministic Scheduling Across Converged Networks","authors":"Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen","doi":"10.1109/TMC.2025.3530486","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530486","url":null,"abstract":"Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2504-2518"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563922","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}