Ad Hoc Networks最新文献

{"title":"Utilization of machine learning in future wireless networks for resource optimization: A survey","authors":"Mudassar Liaq ,&nbsp;Sana Sharif ,&nbsp;Sherali Zeadally ,&nbsp;Waleed Ejaz","doi":"10.1016/j.adhoc.2025.103983","DOIUrl":"10.1016/j.adhoc.2025.103983","url":null,"abstract":"<div><div>Future wireless networks will play an essential role as the need for performance and feature availability grows. Most of the traffic in future wireless networks is due to increased Internet of things (IoT) devices, making resource optimization critical. Traditional optimization algorithms have limitations due to their high computational complexity, which restricts their use in modern applications. To address this, machine learning algorithms are now the preferred alternative to traditional optimization algorithms due to their improved runtime complexity. We present a comprehensive survey on the use of machine learning for resource optimization in future wireless networks. The use of machine learning is divided into three categories: (i) comprehensive solutions, where machine learning is the primary component of the solution approach; (ii) partial solutions, where machine learning is used alongside a traditional approach for optimization; and (iii) environment-only solutions, where optimization is performed in a machine-learning environment. We have further classified objective functions (e.g., energy, latency, data rate, etc.) within each category based on the pure objective function, variations on the objective function, and objective function tradeoffs with respect to other objective functions. We present objective functions and constraints used in the literature for optimization problem formulation. We provide an overview of frequently used machine learning algorithms for resource optimization, followed by a detailed survey of machine learning works in the literature in the three aforementioned categories. Finally, we discuss future research directions for utilizing machine learning to optimize resource management in future wireless networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103983"},"PeriodicalIF":4.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Context-aware adaptive Send-on-Delta for traffic saving in sensor networks","authors":"Iago R. Martínez-Sánchez,&nbsp;Joaquín Cuellar-Padilla,&nbsp;Joaquín Olivares,&nbsp;Jose M. Palomares,&nbsp;Fernando León-García","doi":"10.1016/j.adhoc.2025.103967","DOIUrl":"10.1016/j.adhoc.2025.103967","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) are widely deployed for real-time monitoring in domains such as industrial automation, smart buildings, and the Internet of Things (IoT). However, continuous sensor data transmission often results in excessive network traffic and elevated energy consumption, particularly in resource-constrained environments.</div><div>This paper presents a context-aware adaptive data reduction strategy based on the <em>Send-on-Delta</em> (SoD) transmission scheme, enhanced with a dynamic threshold adjustment mechanism driven by Bollinger Bands. A key novelty of the proposed approach lies in its global adaptation strategy: instead of configuring transmission thresholds independently at each node, the system evaluates the collective behavior of the network to dynamically reconfigure the local thresholds in a coordinated manner.</div><div>The method is supported by a formal and algorithmic model that captures the network-wide adaptation process, and is validated through a comprehensive evaluation including: a real-world deployment with ESP32-based wireless sensors operating over a 24-hour period; a scalability study with up to 40 simulated nodes derived from real data traces; and a post-hoc comparison against representative SoD variants, such as Send-on-Area and Predictive SoD. Experimental results show that the proposed strategy achieves over 89% reduction in transmission volume while maintaining bounded error, outperforming traditional approaches in lossless, context-sensitive scenarios. These results confirm the method’s effectiveness, robustness, and scalability for energy-efficient communication in WSNs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103967"},"PeriodicalIF":4.8,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Design and implementation of a layered time slot MAC protocol for high-density single-anchor UWB positioning systems using DS-TWR","authors":"Xiangyang Li ,&nbsp;Yan Tan ,&nbsp;Yuanxiao Dang ,&nbsp;Jianxin Xu ,&nbsp;Senping Tian","doi":"10.1016/j.adhoc.2025.103980","DOIUrl":"10.1016/j.adhoc.2025.103980","url":null,"abstract":"<div><div>Ultra-wideband (UWB) technology, with its centimeter-level ranging accuracy, has become a key enabler for Internet of Things (IoT) applications such as asset tracking, smart warehouses, and proximity-aware systems. However, conventional MAC protocols struggle to support real-time, high-density localization in confined environments due to inefficient channel utilization and limited scalability. This paper proposes a novel MAC protocol featuring a layered time slot mechanism that hierarchically decouples logical (macro) and physical (micro) slot allocations. This design enables efficient scheduling of double-sided two-way ranging (DS-TWR) exchanges through non-contiguous resource reuse, without compromising ranging precision. A dynamic superframe structure is introduced, prioritizing deterministic ranging in the Contention-Free Period (CFP) and supporting retries in the Contention Access Period (CAP), and accommodating heterogeneous update rates across tags. Theoretical analysis and simulations demonstrate substantial improvements in tag capacity, bandwidth efficiency, and ranging throughput compared to standard guaranteed time slot (GTS)-based protocols. Furthermore, a hardware implementation using DecaWave DWM1000 modules validates the protocol’s feasibility, confirming effective multi-tag localization under real-world conditions. Beyond positioning, this layered time slot mechanism can also be applied to other domains besides positioning where several transmissions and receptions are necessary for one transaction.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103980"},"PeriodicalIF":4.8,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual-layer UAV-assisted mobile edge computing system for disaster rescue: Coordinated optimization of coverage, obstacle-avoidance path planning and task offloading","authors":"Weiyu Gu ,&nbsp;Tuanfa Qin ,&nbsp;Dan Chen ,&nbsp;Shixuan Xian ,&nbsp;Xiao Jiang ,&nbsp;Wenhao Guo ,&nbsp;Yongle Hu","doi":"10.1016/j.adhoc.2025.103981","DOIUrl":"10.1016/j.adhoc.2025.103981","url":null,"abstract":"<div><div>This study addresses critical challenges in urban disaster rescue operations, such as fires, including communication failures, complex environments, and information scarcity. We propose a novel Dual-layer UAV-assisted Mobile Edge Computing (DUAMEC) system, leveraging an air–space–ground collaborative communication framework and intelligent task scheduling to overcome traditional limitations like information blind spots, decision-making delays, and inefficient response. DUAMEC innovatively combines a high-altitude upper-layer UAV (U-UAV) for wide-area coverage and a low-altitude down-layer UAV (D-UAV) for task processing, achieving strong coverage, low latency, and high energy efficiency. The core innovations of the DUAMEC system are manifested in the following aspects: First, we propose a grid-based adaptive multi-stage greedy optimization algorithm for optimal UAV deployment, dynamically generating multi-level candidate grids and employing adaptive step-size contraction. An uncovered-point compensation mechanism ensures continuous area coverage. Second, we design a Multi-Agent TD3 with Hindsight Priority Experience Replay (MATD3-HP) algorithm, utilizing a multi-dimensional state space and compound reward mechanism to optimize resource allocation, path planning, and task offloading in dynamic obstacle environments. Experimental results demonstrate that compared to conventional single-layer UAV-MEC systems and fixed path planning schemes, the DUAMEC system achieves an 66.78% reduction in system overhead while maintaining 98% user coverage. Simultaneously, it sustains stable performance with low task processing latency and energy consumption even in scenarios with dense user distribution and highly dynamic obstacles, thereby providing an efficient and reliable intelligent solution for urban disaster rescue operations.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103981"},"PeriodicalIF":4.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Using Reinforcement Learning and Game Theory for Determining Cooperative Nodes in Multi-hop Wireless Networks","authors":"Fahimeh Rashidjafari ,&nbsp;Nahideh Derakhshanfard ,&nbsp;Behrouz Shahrokhzadeh ,&nbsp;Ali Ghaffari","doi":"10.1016/j.adhoc.2025.103969","DOIUrl":"10.1016/j.adhoc.2025.103969","url":null,"abstract":"<div><div>Multi-hop wireless networks, such as ad hoc and IoT networks, contain relay nodes offering packet relay between sources and destinations. Ensuring energy efficiency, minimizing delay, and maintaining reliable routing remain supreme challenges, especially when node cooperation is essential in guaranteeing network survivability. Unlike traditional approaches that assess cooperation globally and concerning the origin, this work proposes a new approach that detects cooperative nodes on a per-destination basis using a hybrid framework based on reinforcement learning and game theory. In the proposed framework, every node is an independent agent that learns optimal routing policies through ongoing interactions. Reinforcement learning allows dynamic adaptation, while game-theoretic modeling achieves incentive compatibility through payment for cooperative behavior and punishment for selfishness. Simulation results demonstrate that the algorithm presented outperforms benchmark algorithms including HChOA, QoS-RSIA, RL-based, and DQN-RSS. The suggested approach attains a 20% lessening in energy consumption, a 37.5% reduction in average delay, a 10.6% betterment in packet delivery ratio, a 14.1% improvement in cooperation ratio, and a 17.5% improvement in network lifetime. These enhancements validate the efficacy of the suggested approach in improving routing performance, reducing energy consumption, and guaranteeing reliability in dynamic multi-hop wireless networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103969"},"PeriodicalIF":4.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Time-dependent distributed collaboration and incentive mechanism for Mobile Crowdsensing","authors":"Junjie Yan ,&nbsp;Wenli Wang ,&nbsp;Haohao Yuan ,&nbsp;Jingxian Liu ,&nbsp;Junyi Deng","doi":"10.1016/j.adhoc.2025.103979","DOIUrl":"10.1016/j.adhoc.2025.103979","url":null,"abstract":"<div><div>In Mobile Crowd Sensing (MCS), the increasing complexity of sensing tasks and the rising demand for data quality have rendered traditional single-participant sensing paradigms inadequate. Collaborative sensing involving multiple participants has emerged as a crucial approach to enhance sensing efficiency and accuracy. However, centralized collaboration strategies often impose significant computational and processing burdens on the platform, while neglecting participants’ actual capabilities and willingness to cooperate. Moreover, existing research rarely addresses the sensing time redundancy that arises when multiple participants collaborate on the same task. Additionally, most studies assume participants have long, continuous time slots available for sensing, which does not align with real-world scenarios where participants’ available time is often fragmented. To address these challenges, this paper proposes a Time-dependent Mobile Crowdsensing Distributed Group Collaboration System (TMDCS). First, we construct a task selection model that accounts for participants’ sensing capabilities and allocates different suitable tasks across their multiple fragmented time slots. We also develop a task collaboration incentive model aimed at encouraging greater participation and ensuring high-quality sensing data. Second, a distributed task optimization mechanism is designed to improve overall social welfare. This mechanism selects leaders and forms collaborative groups based on coalition game theory. Finally, a reverse auction scheme is applied to select the optimal coalition for each task and determine incentive distribution. Experimental results demonstrate that the proposed TMDCS outperforms baseline methods, achieving average improvements of 49.7% in social welfare, 37.5% in task coverage, and 25.3% in task quality.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103979"},"PeriodicalIF":4.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"QoS-aware Network Slicing and Resource Management for Internet of Vehicles in 5G networks","authors":"Wafa Hamdi,&nbsp;Orhan Dağdeviren,&nbsp;Hasan Bulut","doi":"10.1016/j.adhoc.2025.103976","DOIUrl":"10.1016/j.adhoc.2025.103976","url":null,"abstract":"<div><div>The development of Internet of Vehicles (IoV) technologies brings with it numerous challenges, such as heterogeneous Quality of Service (QoS) in 5G and beyond (B5G). These challenges go hand in hand with spectrum scarcity, one of the biggest challenges of future wireless technologies. This will be exacerbated especially in the era of Vehicle-to-Everything (V2X) communication due to the limitation of radio resources such as channels, bandwidth, and power. This challenge is further intensified by the increasing number of vehicles. Therefore, spectrum sharing initiatives have a significant impact on traffic safety and efficiency. In response to these challenges and to meet the diverse QoS requirements of vehicular applications within 5G/B5G, new paradigms such as New Radio Vehicle-to-Everything (NR-V2X) and Network Slicing (NS) have emerged as important solutions. Network Slicing efficiently divides the physical network into slices tailored to Ultra-Reliable Low Latency Communications (URLLC), Enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC). In this paper, we investigate the IoV slicing problem with QoS support, focusing on partitioning the physical network into three different slices: URLLC, eMBB and mMTC. To ensure seamless communication in vehicular networks, our mixed method approach effectively incorporates handover mechanisms, emergency traffic prioritization, based services, and road network-specific parameters. In addition, we propose two resource allocation algorithms that enable efficient allocation of resources to vehicles and comply with the standardization principles of the 3rd Generation Partnership Project (3GPP). These algorithms aim to prioritize emergency traffic during incidents while maintaining an acceptable QoS for non-safety services in a resource-constrained environment, thus improving Key Performance Indicators (KPIs). Detailed simulations show the effectiveness of the proposed algorithms, and confirm their ability to improve the performance of emergency services in terms of end-to-end delay while ensuring acceptable reliability and throughput. Comparative evaluations further highlight the superiority of the proposed NS method over other existing approaches.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103976"},"PeriodicalIF":4.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Adaptive guard band and power control for resource allocation in mobile and fixed mission-critical IoUT networks","authors":"Walid K. Hasan,&nbsp;Iftekhar Ahmad,&nbsp;Quoc Viet Phung,&nbsp;Daryoush Habibi","doi":"10.1016/j.adhoc.2025.103978","DOIUrl":"10.1016/j.adhoc.2025.103978","url":null,"abstract":"<div><div>The Internet of Underwater Things (IoUT) is transforming underwater communication by enabling essential mission-critical applications such as precise navigation, emergency response coordination, diver safety, robust security and surveillance systems, and real-time environmental monitoring. However, Underwater Acoustic Communication (UAC), which serves as the primary communication medium for IoUT, experiences substantial challenges, including limited bandwidth availability, severe signal attenuation and Doppler-induced frequency shifts, especially pronounced in mobile underwater environments. These challenges degrade throughput and increase latency, making it difficult to meet the strict delay and reliability demands of mission-critical IoUT applications. Without adaptive solutions, real-time underwater communication remains unreliable and inefficient. This paper introduces an Adaptive Guard band and Power control resource allocation scheme for mission critical applications (AGP-MCA), specifically designed to improve underwater communication. The AGP-MCA framework optimizes the acoustic spectrum based on the criticality of IoUT applications. AGP-MCA dynamically adjusts guard bands to effectively mitigate Doppler caused by mobile nodes and strategically manages transmission power to reduce power consumption significantly, and handles non-critical data through buffering. We formulate a comprehensive mathematical optimization model and employ a Whale Optimization Algorithm (WOA)-based meta heuristic approach to achieve near-optimal solutions while ensuring minimal computational complexity. Extensive simulations demonstrate that AGP-MCA enhances throughput, reduces both end-to-end delay and power consumption, and consistently outperforms existing protocols and configurations without adaptive guard bands. Further, it offers a robust and power-efficient solution for real-time mission-critical IoUT applications.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103978"},"PeriodicalIF":4.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multi-UAV path planning for connectivity-based sweep coverage","authors":"Survi Kumari ,&nbsp;Seshan Srirangarajan","doi":"10.1016/j.adhoc.2025.103966","DOIUrl":"10.1016/j.adhoc.2025.103966","url":null,"abstract":"<div><div>Quality of service in wireless surveillance networks relies on two key factors: area coverage and connectivity. In this work, we investigate the spatial coverage and connectivity of a wireless surveillance network comprising of unmanned aerial vehicles (UAVs) used for surveying an area of interest. In many such applications, the data collected by the UAVs must be relayed, perhaps through multiple hops, to the base station in real-time. The goal of the network, in such applications, is to improve area coverage of the network while ensuring the UAVs are connected to the base station/sink throughout their flight. To achieve this, we propose two mixed-integer linear programming (MILP)-based formulations to plan the path of a set of UAVs. In the first formulation, we maximize the area covered by the UAVs while ensuring that they maintain their connectivity to the base station. In the second formulation, we minimize the number of movements required by the UAVs to achieve a desired coverage level while maintaining connectivity to the base station. We present extensive performance evaluation of the proposed algorithms and their comparison with path-planning approaches that do not consider connectivity constraints. We explore some of the trade-offs involved in meeting the connectivity requirement.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103966"},"PeriodicalIF":4.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Interplay of ML and blockchain for secure Internet of Military Vehicles communication underlying 5G","authors":"Maulik Sojitra ,&nbsp;Nilesh Kumar Jadav ,&nbsp;Rajesh Gupta ,&nbsp;Usha Patel ,&nbsp;Janam Patel ,&nbsp;Sudeep Tanwar ,&nbsp;Giovanni Pau ,&nbsp;Fayez Alqahtani ,&nbsp;Amr Tolba","doi":"10.1016/j.adhoc.2025.103968","DOIUrl":"10.1016/j.adhoc.2025.103968","url":null,"abstract":"<div><div>Internet of Things (IoT) networks have rapidly transformed various sectors, including modern warfare, where Internet of Military Vehicles (IoMVs) enable remote connection, monitoring, and data sharing. However, IoMV sensors lack inherent security measures to combat threats such as DDoS, jamming, and spoofing. Traditional security solutions relying on AI face challenges such as inefficient feature selection, lack of transparency, and susceptibility to data tampering. In this paper, we propose an AI and Blockchain based secure data exchange architecture for battlefield IoMV networks. Our approach employs an Explainable Artificial Intelligence (XAI) technique for optimal feature selection and uses five different Machine Learning algorithms to classify malicious and non-malicious data. Notably, the XGBoost model achieves an accuracy of 98.8%. Non-malicious data is securely forwarded to a blockchain network, where a smart contract validates its legitimacy, and stored off-chain using the Inter-Planetary File System (IPFS) to enhance scalability and reduce storage costs. Additionally, leveraging low latency 5G communication ensures rapid and reliable data transmission. This integration of AI for real-time threat detection, blockchain for tamper-proof storage, and 5G for enhanced communication significantly improves battlefield operations by enabling secure and efficient decision-making.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103968"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}