IEEE Transactions on Mobile Computing最新文献

{"title":"Toward Adaptive IoT Service Balance in Low-Altitude Economy: Multi-UAV-Aided Bi-Objective Wireless Data Collection and Wireless Energy Transfer","authors":"Zeyu Zhao;Yueling Che;Sheng Luo;Kaishun Wu;Victor C. M. Leung","doi":"10.1109/TMC.2026.3664903","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664903","url":null,"abstract":"The rapid development of the low-altitude economy (LAE) has significantly enhanced the service diversity of the Internet of Things (IoT) networks, necessitating efficient coordination among the unmanned aerial vehicles (UAVs). In this work, we utilize multiple UAVs to provide both wireless data collection (WDC) service and wireless energy transfer (WET) service, and divide the IoT devices into the I-devices that only need the WDC service and the E-devices that only need the WET service, respectively, from the multiple UAVs. Due to their conflicting service demands on the UAVs with limited resources, we formulate the bi-objective optimization problem (BOOP) to minimize the age of information (AoI) for the I-devices and the hungry-level of energy (HoE) for the E-devices at the same time, by jointly optimizing all the UAVs’ trajectories and WET decisions over time slots and their WDC decisions over sub-slots. To efficiently solve the complex BOOP, we innovatively transform it into a single-objective optimization problem (SOOP), in which the two conflicting objectives are scalarized via a self-adaptive objective weight. Unlike the conventional approach reliant on fixed and pre-defined objective weights, we optimize the objective weight jointly with other decision variables, enabling automatic adaptation to various network environments without human intervention. However, the proposed SOOP is NP-hard with a large number of decision variables. Accordingly, we propose a new Multi-Agent Adaptive and Hierarchical Deep Reinforcement Learning (MA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>HDRL) framework, which leverages a central controller (CC) to guide the local training of multiple individual UAV agents. In this framework, each UAV agent employs a two-tier hierarchical DRL model: tier-1 optimizes the trajectory and WET policies over the time slots, while tier-2 optimizes the WDC policy across the sub-slots. Meanwhile, the CC trains the global reward preference for all the UAV agents over the training episodes, to adaptively balance the WDC and WET service demands. Finally, extensive simulation results are conducted to demonstrate the outstanding performance of the proposed MA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>HDRL approach as compared to state-of-the-art benchmarks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10773-10787"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519210","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":"Separating or Sharing: Tradeoff Oriented Joint Optimization of Beamforming and Reflective Precoding in Active RIS-Enabled ISAC System","authors":"Zhongyu Ma;Yuxi Gao;Jing Li;Zhirong Liu;Yuankun Tang;Guangjie Han","doi":"10.1109/TMC.2026.3664397","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664397","url":null,"abstract":"Integrated sensing and communication (ISAC) is potentially viewed as a key driver in the ubiquitous service of future wireless systems. However, numerous challenges should be solved before the alluring benefits are enjoyed. Although enhancement of sensing capabilities inevitably leads to a reduction in the communication performance, the loss can be compensated through the emerging reconfigurable intelligent surface (RIS). Specifically, active RIS is more favored because the “multiplicative fading” effect existed in passive RIS is overcome, and the manipulation effectiveness of wireless environments can be further enhanced. To this end, this paper investigates the simultaneous optimization of the transmit beamforming at base station (BS) and the precoding at RIS. To fully explore the potential of the active RIS-enabled ISAC system, two antenna deployment strategies, i.e., separated deployment and shared deployment, are designed to maximize the communication users’ sum-rate under the considered constraints including transmission power, probing power and reflection manipulation, etc. Furthermore, an effective algorithm combining fractional programming and semidefinite relaxation is employed to derive a sub-optimal solution through an alternating optimization framework due to the non-convex characteristic of the initial problem. Finally, the simulation results demonstrate that more superior transmit beampatterns can be achieved in the shared deployment in comparison with the separated deployment, and that the sum-rate’s upper bound is significantly enhanced in the active RIS when comparing to traditional passive RIS.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10969-10983"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148515693","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":"Delay-Trajectory-Accuracy Trilemma Optimization for Non-IID Mitigation in AAV-Cooperative Federated Learning IoT","authors":"Qihao Li;Tongzhou Yang;Qiang Ye;Nan Cheng;Fengye Hu","doi":"10.1109/TMC.2026.3656974","DOIUrl":"https://doi.org/10.1109/TMC.2026.3656974","url":null,"abstract":"In this paper, we propose a novel cooperative-enabled communication and learning optimization (CCLO) scheme to resolve the delay-trajectory-accuracy trilemma for non-independent and identically distributed (non-IID) data mitigation in AAV-assisted federated learning (FL) for IoT networks. Specifically, we design a tri-modular framework that synergizes dynamic blocklength allocation, energy-aware trajectory planning, and reliability-driven weight adaptation. We derive a convex reformulation of the error probability minimization problem by exploiting temporal coupling between AAV mobility and blocklength allocation, enabling real-time adaptation to channel dynamics via the golden section algorithm. Then, we develop a successive convex approximation (SCA)-based trajectory optimizer with novel convex relaxations to address non-convex collision avoidance and energy constraints. By introducing auxiliary distance variables, we decouple spatial and spectral resource dependencies, achieving Pareto-optimal paths that balance flight energy and latency. We investigate decentralized weight coordination through a block-coordinate Gauss-Seidel algorithm, dynamically aligning aggregation weights with channel success probabilities and local model divergence metrics to counteract stochastic packet losses. The closed-loop interaction among trajectory planning, blocklength allocation, and weight adaptation together ensures FL convergence while mitigating non-IID data skew and resource constraints. Simulation results demonstrate that the CCLO scheme significantly reduces communication delay and improves model accuracy by 18.9% compared to benchmarks. It also maintains robust convergence under time-varying wireless channels and diverse device distributions, validating its efficacy in harmonizing the trilemma for AAV-cooperative FL-IoT systems.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10094-10111"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518058","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":"3DLoRa: Refining LoRa Link Estimation Using 3D Point Clouds","authors":"Junmei Yao;Shibo Liang;Junda Xu;Guoqing Yang;Ruitao Xie;Haodi Zhang;Kaishun Wu","doi":"10.1109/TMC.2026.3655338","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655338","url":null,"abstract":"LoRa technology plays a vital role in IoT applications by enabling low-power and long-range communication. Since the LoRa signal may transmit hundreds of meters to tens of kilometers under different environments, estimating the path loss of LoRa links can bring great benefits to the network, such as device localization and network planning. Recent years have seen some mechanisms to improve the LoRa link estimation by analyzing the environments based on satellite images. However, they may be inaccurate when we consider the actual network in the three-dimensional (3D) scene. This paper proposes 3DLoRa, the first framework to refine LoRa link estimation through exploiting 3D point clouds. We generate a 3D map by conducting semantic segmentation and voxelization based on the point clouds. We then propose both model-based and learning-based methods to estimate the path loss of a link according to the environments obtained from the 3D map. We test the 3D map generation process based on 3D point clouds in a campus area and show an accuracy of about 97% for environment categorization. We also deploy a LoRa network on the campus and collect data to evaluate the performance of link estimation. Experimental results indicate that 3DLoRa can significantly improve the accuracy of link estimation in various environments compared to previous works. Specifically, the model-based method can reduce the estimation error to about <inline-formula><tex-math>$ text{7 dBm}$</tex-math></inline-formula> with low computational overhead, while the learning-based method can reduce the estimation error to about <inline-formula><tex-math>$ text{2.5 dBm}$</tex-math></inline-formula> with the cost of higher computational overhead. These results can serve as a reference for how to deploy link estimation methods in different network situations.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9629-9643"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518548","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":"ICN-Enabled Internet of Robotic Things Data Routing and Access for Post-Disaster Areas","authors":"Xiaonan Wang;Xinru You","doi":"10.1109/TMC.2026.3664010","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664010","url":null,"abstract":"The Internet of Robotic Things (IoRT) enables real-time data delivery and surveillance in harsh environments. In such monitoring scenarios, users need simultaneous access to diverse real-time data from multiple providers to perform collaborative, integrated analysis. The Information-Centric Networking (ICN) model satisfies this requirement by allowing consumers to efficiently share data from different providers through a single communication process. Based on this observation, we propose an ICN-enabled IoRT data routing and access framework that leverages ICN to enable rapid, simultaneous access to a variety of data. The framework utilizes inter-robot relationships and robot attributes to establish routing paths consisting of backbone robots, enabling unicast-based simultaneous sharing of various data among consumers. The experimental results demonstrate the framework's effectiveness and superiority.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10200-10210"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518844","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":"BSGAKA-IoD: Blockchain-Enabled Scalable Group Authentication and Key Agreement Scheme for the Dynamic Internet of Drones","authors":"Keke Huang;Huidan Hu;Changlu Lin","doi":"10.1109/TMC.2026.3664384","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664384","url":null,"abstract":"With the advancing application of Unmanned Aerial Vehicles (UAVs, also known as Drones), UAV swarms are deployed in various complex mission scenarios to collaborate on particular assignments. However, UAV group communication encounters security and privacy challenges due to the open and insecure communication environment. Moreover, many existing UAV group authentication and key agreement (GAKA) schemes provide limited support for dynamic membership and fail to enforce mandatory participation of the leader UAV in hierarchical architectures. To address these limitations, we propose a blockchain-enabled scalable GAKA scheme tailored for dynamic IoD, named BSGAKA-IoD. The scheme achieves UAV threshold GAKA through an enhanced designated participant <inline-formula><tex-math>$(ID, t, n)^{*}$</tex-math></inline-formula>-secret sharing scheme, which cryptographically enforces leader participation and enables threshold-based GAKA among the cluster leader and member UAVs. The scheme supports efficient group authentication, secure group key establishment, and dynamic joining/leaving without requiring any secure channel, while achieving <inline-formula><tex-math>$O(n)$</tex-math></inline-formula> computational and communication complexity. We conduct comprehensive security analysis and develop two blockchain prototypes: a Solidity smart contract implementation validated in Remix, and a Hyperledger Fabric implementation benchmarked using Hyperledger Caliper under two network configurations. The results confirm that BSGAKA-IoD provides strong security guarantees, maintains linear scalability, and is feasible for deployment in realistic consortium-chain IoD settings.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10225-10241"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519029","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":"vSoC: Efficient and Debug-Friendly Virtual System-on-Chip for Mobile Emulation","authors":"Zijie Zhou;Jiaxing Qiu;Zhenhua Li;Feng Qian;Yunhao Liu;Bo Xiao;Hongwei Hu","doi":"10.1109/TMC.2026.3667012","DOIUrl":"https://doi.org/10.1109/TMC.2026.3667012","url":null,"abstract":"Emerging heavy-load mobile apps like UHD video and AR/VR access diverse high-throughput hardware devices, e.g., video codecs and cameras. However, today’s mobile emulators exhibit poor performance when emulating these devices. We pinpoint the major reason to be the discrepancy between the guest’s (system-on-chip) and host’s (PC or cloud server) memory architectures for these devices, which makes the shared virtual memory (SVM) architecture of mobile emulators highly inefficient. To address this, we introduce vSoC, the first virtual mobile SoC featuring a <italic>unified</i> SVM framework that enables efficient and secure data sharing among virtual devices, as well as an <italic>intelligent</i> prefetch engine that effectively eliminates the vast majority of coherence maintenance overhead. While vSoC addresses runtime performance issues, app developers face a severe debugging challenge due to the inability of traditional tools to capture complete system states. Therefore, we devise an adaptive VM (virtual machine) snapshot-based approach that dynamically selects the optimal resource loading strategy to make vSoC debug-friendly. Compared to state-of-the-art emulators, vSoC brings 1.8–9.0× frame rates, 35%-62% lower motion-to-photon latency, and 6.5–14.4× bug reproduction rates for heavy-load apps. It is applicable to a variety of scenarios like end-user high-performance emulation and cloud/web-based rendering.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10242-10259"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519030","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":"Preamble Signaling in WLAN: Vulnerabilities, Attacks, and Countermeasures","authors":"Zhengguang Zhang;Marwan Krunz","doi":"10.1109/TMC.2026.3660297","DOIUrl":"https://doi.org/10.1109/TMC.2026.3660297","url":null,"abstract":"The preamble of a Wi-Fi frame contains multiple Signal (SIG) fields that are crucial for robust communications. In this paper, we first use measurements and security analysis to identify the vulnerabilities of the SIG fields in terms of confidentiality, predictability, and integrity. We then introduce a SIG tampering attack (SIGTAM), in which the attacker exploits these vulnerabilities to craft and transmit a signal that tampers with legitimate SIG fields. The attack signal is designed such that the tampered signal passes error detection, including even-parity and cyclic redundancy check (CRC), hence deceiving the receiver. The tampered SIG fields lead to frame discard or decoding errors at the receiver, and disrupt channel access of neighboring devices. We also develop strategies that make SIGTAM robust against channel impairments and synchronization errors. SIGTAM operates stealthily, targeting fewer than 20% of the subcarriers and lasting for only 4 <inline-formula><tex-math>$mu$</tex-math></inline-formula>s. We conduct extensive simulations and over-the-air hardware experiments on IEEE 802.11a/ax devices, and demonstrate that SIGTAM achieves nearly 100% packet drop and error rates. Compared to a smart jamming on selected subcarriers of SIG fields, SIGTAM consumes <inline-formula><tex-math>$40times$</tex-math></inline-formula> less energy to cause 100% packet drop, yet prevents channel access by legitimate devices for over <inline-formula><tex-math>$10times$</tex-math></inline-formula> longer time. Beyond denial-of-service (DoS), SIGTAM has broader impacts, including throughput degradation, increased latency, channel underutilization, unfair channel access, and reduced spatial reuse. To mitigate the threat of SIGTAM, we propose and evaluate a defense scheme that detects the attack, identifies affected subcarriers, and recovers legitimate SIG fields from their equalized frequency-domain symbols.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9962-9979"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148515692","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":"Privacy-Preserving Yet Vulnerable: Data Poisoning Attacks Against Differential Privacy Sparse Mobile Crowdsensing System","authors":"Chengxin Li;Yujie Gu;Pengpeng Qiao;Shengli Pan;Kouichi Sakurai;Zhetao Li","doi":"10.1109/TMC.2026.3655365","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655365","url":null,"abstract":"Sparse Mobile CrowdSensing (SMCS) reduces sensing costs while preserving data quality, shedding a new paradigm on data collection. However, the reliance on user-contributed data raises significant privacy concerns. While Local Differential Privacy (LDP) offers strong theoretical guarantees for protecting user privacy, the noise it introduces not only degrades inference accuracy but also creates opportunities for new security threats, in particular data poisoning attacks. In this work, we study the vulnerability of differentially private SMCS systems under data poisoning. We propose a novel attack framework formulated as a bi-level optimization problem with spatiotemporal constraints. Specifically, the lower-level problem performs robust data inference using differentially private matrix factorization with a mixture of Gaussians, enabling accurate reconstruction from noisy and incomplete data. The upper-level problem, from the attacker’s perspective, aims to maximize inference error while maintaining stealth, which is achieved by enforcing temporal and spatial similarity between poisoned and normal data. To solve the bi-level problem, we develop an alternating algorithm that combines projected gradient ascent with the Expectation-Maximization procedure to obtain the optimal poisoning strategy. Extensive experiments on real-world datasets show that attackers can effectively degrade the system’s inference performance while concealing their attack behavior. Additionally, we observed a positive correlation between attack intensity and spatiotemporal constraint loss, enabling attackers to adapt their strategies to specific deployment requirements. These findings highlight the structural vulnerability of differential privacy SMCS systems to data poisoning attacks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9594-9611"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518217","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":"Toward Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models","authors":"Xinyu Huang;Leming Shen;Zijing Ma;Yuanqing Zheng","doi":"10.1109/TMC.2026.3654976","DOIUrl":"https://doi.org/10.1109/TMC.2026.3654976","url":null,"abstract":"Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about the potential privacy leaks to the remote servers. To address this issue, we develop <i>HomeLLaMA</i>, an on-device assistant for privacy-preserving and personalized smart home serving with a tailored small language model (SLM). <i>HomeLLaMA</i> learns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed, <i>HomeLLaMA</i> facilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user interaction while protecting their privacy, we develop <i>PrivShield</i> to offer an optional, privacy-preserving LLM-based smart home service for users who are unsatisfied with local responses and are willing to send less-sensitive queries to remote servers. For evaluation, we develop a comprehensive benchmark, <i>DevFinder</i>, to assess service quality. Extensive experiments and user studies (<inline-formula><tex-math>$M=100$</tex-math></inline-formula>) demonstrate that <i>HomeLLaMA</i> can provide personalized services while significantly enhancing user privacy","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9505-9520"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518404","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}