[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2764":3,"related-2764":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":79,"created_at":99},2764,"An energy-aware federated intelligence framework for sustainable WSN-IoT ecosystems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02291-x","With the rapid development of Internet of Things (IoT), the deployment of Wireless Sensor Networks (WSNs) as vital infrastructure for environmental monitoring, industrial automation, precision agriculture, and smart city applications has increased significantly. Despite this, WSN-IoT ecosystems are still hindered by the issues of limited energy, communication overhead, scalability, preserving privacy, and learning in an intelligent decentralized fashion. To overcome these challenges, this study suggests a federated energy-aware intelligence framework namely EcoSense-AI for sustainable WSN-IoT ecosystems. It combines Energy-Weighted Federated Learning, Adaptive Differential Privacy, Energy-Aware Graph Neural Network (EA-GNN)-based topology modeling, and multi-objective resource optimization, to facilitate secure, efficient, and adaptive edge-cloud collaborative intelligence. The effectiveness of EcoSense-AI was tested with a simulated WSN-IoT network of heterogeneous sensor nodes operating with different residual energy levels, communication ranges and moving network conditions. Distributed environmental sensing data in the device, edge and cloud layer were subjected to experimental analysis. The proposed framework was compared with FedAvg and FedProx for equal training rounds, communication setup and in energy constrained deployment settings. The accuracy, energy efficiency, privacy score, communication cost reduction and network lifetime extension were used as evaluation metrics to measure performance. The experimental results show that the proposed method, EcoSense-AI outperforms the baseline methods with 98.56% accuracy, 93.2% energy efficiency, and 0.97 privacy score, and increased network lifetime by 58.9% and decreased communication cost. The proposed framework for sustainable intelligent sensing and decentralized learning in next-generation WSN-IoT applications is proved to be effective through the results.","随着物联网（IoT）的快速发展，无线传感器网络（WSN）作为环境监测、工业自动化、精准农业和智慧城市应用的重要基础设施，其部署规模显著增长。尽管如此，WSN-IoT生态系统仍然受到能量受限、通信开销、可扩展性、隐私保护以及智能去中心化学习等问题的制约。为克服这些挑战，本研究提出了一种面向可持续WSN-IoT生态系统的联邦能量感知智能框架，即EcoSense-AI。该框架融合了能量加权联邦学习、自适应差分隐私、基于能量感知图神经网络（EA-GNN）的拓扑建模以及多目标资源优化，以实现安全、高效且自适应的边云协同智能。EcoSense-AI的有效性通过一个模拟WSN-IoT网络进行了验证，该网络由具有不同剩余能量水平、通信范围和动态网络条件的异构传感器节点组成。对设备层、边缘层和云层中的分布式环境感知数据进行了实验分析。在相同训练轮次、通信设置和能量受限部署条件下，将所提框架与FedAvg和FedProx进行了对比。采用准确率、能量效率、隐私评分、通信成本降低和网络寿命延长作为评价指标来衡量性能。实验结果表明，所提方法EcoSense-AI优于基线方法，准确率达到98.56%，能量效率为93.2%，隐私评分为0.97，网络寿命延长了58.9%，并降低了通信成本。实验结果证明了该框架在下一代WSN-IoT应用中实现可持续智能感知和去中心化学习的有效性。",null,"Peer-to-Peer Networking and Applications","2026-09-17T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},16,21,18,13,1,"提出面向WSN-IoT的联邦能效智能框架EcoSense-AI，在精度、能效、隐私与网络寿命上均有量化提升，对农业环境监测与精准农业的可持续感知具有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业物联网","边缘计算","联邦学习","数据隐私",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,55,63,68],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":54},19,true,[48],"IoT",[50,51,52,48,53],"物联网","无线传感","internet of things","wireless sensor",[27,28],{"code":56,"score":57,"supported":46,"title":58,"abstract":59,"tags":62},"I1",7,[],[60,61],"精准农业","precision agriculture",[26],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I2",[],[],[29],{"code":69,"score":21,"supported":14,"title":70,"abstract":71,"tags":72},"I6",[],[],[30],[74,75],"农业物联网 数据隐私 智慧农业 联邦学习","农业物联网 数据隐私","农业物联网数据隐私智慧农业联邦学习-2764",2,"10.1007\u002Fs12083-026-02291-x",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7169722039",[82,84,87,89],{"name":83,"orcid":9},"G Elumalai",{"name":85,"orcid":86},"Dhanalakshmi Gopal","https:\u002F\u002Forcid.org\u002F0009-0000-4123-176X",{"name":88,"orcid":9},"Jayaprakash Chinnadurai",{"name":90,"orcid":9},"V.V. Teresa",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出EcoSense-AI联邦智能框架，解决WSN-IoT能耗、隐私与通信瓶颈。","能量加权联邦学习、自适应差分隐私、能量感知GNN与多目标优化仿真。","准确率98.56%、能效93.2%、隐私0.97，网络寿命延长58.9%。","智慧农业 \u002F 农业物联网","可将该框架迁移至农田异构传感网，验证真实农业场景下的能效与隐私权衡。","openalex","2026-09-17T23:30:10.020578Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,187,258,351,401,453],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":121,"secondary_topics":122,"classification":129,"search_phrases":167,"slug":170,"view_count":77,"doi":171,"paper":172,"created_at":186},4561,"Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajese.20261004.11","This study presents a system architecture, algorithm, and evaluation protocol for smart agriculture and environmental monitoring. It is a method\u002Farchitecture contribution and does not report field trial results. World food production is still under stress from climate variability, the availability of freshwater resources, and the cost of farm inputs, offering an incentive to transition to data-driven and sensor-based farm management practices. Internet of Things (IoT)-based agricultural monitoring is well developed, but most systems deployed focus on a single parameter (e.g., soil moisture) or the parameter is inferred solely in the cloud, adding latency and fragility to the deployment of systems in rural areas. In this study, we propose an integrated architecture for low-power multiparameter environmental sensing, LoRaWAN\u002FNB-IoT connectivity, edge-based lightweight inference, and privacy-preserving federated learning with cross-farm model aggregation, which is further complemented by a digital twin layer for scenario simulation. The decision layer of the framework can combine data from ground sensors with UAV and satellite images for the multi-scale monitoring of crop health and environmental quality (air, water, and GHG proxies). We contribute the full system design, an algorithmic pipeline, a sensor and model configuration specification, and a pre-registered evaluation protocol intended to guide future field deployment; no field data have yet been collected and clearly labelled non-empirical templates showing the expected reporting format only, rather than measured outcomes. The architecture and protocol are instead grounded in, and cross-checked against, published state-of-the-art results, which report an accuracy range of 82–97% for federated crop-yield prediction and 20–30% water savings from sensor-controlled irrigation.","本研究提出了一套面向智慧农业与环境监测的系统架构、算法与评估协议。这是一项方法\u002F架构层面的贡献，不报告田间试验结果。世界粮食生产仍面临气候变率、淡水资源可得性以及农业投入成本带来的压力，这促使农业管理实践向数据驱动和基于传感器的方向转型。基于物联网（IoT）的农业监测已较为成熟，但大多数已部署系统仅关注单一参数（如土壤水分），或仅在云端推断参数，这增加了农村地区系统部署的延迟和脆弱性。在本研究中，我们提出了一种集成架构，涵盖低功耗多参数环境感知、LoRaWAN\u002FNB-IoT连接、基于边缘的轻量级推理，以及具有跨农场模型聚合的隐私保护联邦学习，并进一步辅以用于情景模拟的数字孪生层。该框架的决策层可将地面传感器数据与无人机和卫星图像相结合，用于作物健康和环境质量（空气、水和温室气体代理指标）的多尺度监测。我们贡献了完整的系统设计、算法流程、传感器与模型配置规范，以及一份预注册的评估协议，旨在指导未来的田间部署；目前尚未收集田间数据，并明确标注了非经验性模板，仅展示预期报告格式，而非实测结果。该架构和协议以已发表的最先进成果为依据并与之交叉验证，这些成果报告联邦作物产量预测的准确率范围为82–97%，传感器控制灌溉可节水20–30%。","American Journal of Environmental Science and Engineering","2026-10-09T00:00:00Z",66,{"impact":113,"substance":17,"depth":114,"authority":20,"freshness":115,"relevant":21,"comment":116},12,17,8,"提出边缘AI+IoT+联邦学习的抗气候风险农业监测架构与评估协议，方法设计完整但尚无田间实测数据，属有参考价值的预研性论文。",[118],{"name":109,"url":106},[26,27,28,120,29],"数字孪生",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[123],{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":130},{"candidates":131,"agricultural_evidence":46},[132,138,142,149,156,163],{"code":56,"score":45,"supported":46,"title":133,"abstract":135,"tags":137},[134],"smart agriculture",[26,120,134,136],"digital twin",[120,26],{"code":32,"score":45,"supported":46,"title":139,"abstract":140,"tags":141},[48],[50,52,48],[27,28],{"code":124,"score":143,"supported":46,"title":144,"abstract":145,"tags":148},9,[],[146,147,125],"灌溉","水资源",[],{"code":150,"score":101,"supported":46,"title":151,"abstract":152,"tags":155},"H2",[],[153,154],"土壤","soil",[],{"code":157,"score":158,"supported":46,"title":159,"abstract":160,"tags":162},"B7",3,[],[161],"温室",[],{"code":64,"score":21,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[29],[168,169],"LoRaWAN NB-IoT 农业监测","联邦学习 作物产量预测","LoRaWANNB-IoT农业监测-4561","10.11648\u002Fj.ajese.20261004.11",{"doi":171,"openalex_id":173,"authors":174,"venue":109,"cited_by_count":91,"oa_url":106,"card":181,"direction":96,"ingested_from":98},"W7221105407",[175,178],{"name":176,"orcid":177},"Zarin Progga","https:\u002F\u002Forcid.org\u002F0009-0004-2311-1210",{"name":179,"orcid":180},"Mohammad Ali","https:\u002F\u002Forcid.org\u002F0009-0009-3725-8535",{"tldr":182,"method":183,"finding":184,"direction":96,"opportunity":185},"提出边缘AI、物联网与联邦学习集成的智慧农业环境监测架构与评估协议，未开展实地试验。","LoRaWAN\u002FNB-IoT多参数传感、边缘轻量推理、跨农场联邦学习、数字孪生仿","架构基于已发表结果交叉验证，联邦产量预测精度82-97%，传感器灌溉节水20-30%。","该框架仅停留在设计阶段，未来可开展真实农田部署，验证边缘联邦学习在异构农场环境下的精度与通信开销。","2026-10-10T23:30:07.789940Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":195,"is_selected":14,"score":196,"score_detail":197,"sources":200,"tags":204,"primary_topic":207,"secondary_topics":208,"classification":210,"search_phrases":236,"slug":239,"view_count":21,"doi":9,"paper":240,"created_at":257},4671,"A COMPREHENSIVE REVIEW ON IOT AND EDGE AI-BASED PRECISION AGRICULTURE SYSTEM FOR WATER OPTIMIZATION","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F316","Water scarcity and inefficient irrigation practices remain major challenges in modern agriculture, leading to reduced crop yield, resource wastage, and increased production costs. The proposed project, “IoT and Edge AI-Based Precision Agriculture System for Water Optimisation,” aims to develop an intelligent, real-time irrigation management system that optimises water usage through data-driven decision-making at the edge. The system integrates Internet of Things (IoT) sensors such as soil moisture, temperature, humidity, and light intensity sensors to continuously monitor field conditions. The collected data is processed locally using an Edge AI module, enabling real-time analysis and predictive irrigation control without relying entirely on cloud connectivity. By deploying machine learning algorithms at the edge, the system can determine optimal watering schedules based on soil conditions, crop requirements, and environmental factors, thereby minimising water wastage and ensuring efficient irrigation. A cloud platform is incorporated for long-term data storage, visualisation, and remote monitoring via a web or mobile dashboard. This hybrid architecture enhances scalability, reduces latency, and improves reliability in rural areas with limited internet connectivity. Automated irrigation control using smart valves or pumps ensures precise water delivery tailored to specific crop zones.","水资源短缺和灌溉效率低下仍是现代农业面临的主要挑战，导致作物减产、资源浪费和生产成本增加。本项目“基于物联网与边缘人工智能的水资源优化精准农业系统”旨在开发一种智能实时灌溉管理系统，通过边缘端的数据驱动决策来优化水资源利用。该系统集成了物联网（IoT）传感器，如土壤湿度、温度、湿度和光照强度传感器，以持续监测田间状况。采集的数据通过边缘人工智能模块在本地进行处理，实现实时分析和预测性灌溉控制，而无需完全依赖云连接。通过在边缘端部署机器学习算法，系统能够根据土壤条件、作物需求和环境因素确定最佳灌溉计划，从而最大限度地减少水资源浪费并确保高效灌溉。系统还包含一个云平台，用于长期数据存储、可视化以及通过网页或移动端仪表盘进行远程监控。这种混合架构增强了可扩展性，降低了延迟，并提高了互联网连接有限的农村地区的可靠性。利用智能阀门或水泵的自动灌溉控制可确保针对特定作物区域进行精准供水。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,53,{"impact":113,"substance":198,"depth":20,"authority":101,"freshness":115,"relevant":21,"comment":199},14,"综述性论文，系统梳理IoT与边缘AI在水肥优化中的应用，方法框架清晰但缺乏原创数据与实证，属细分领域参考性内容。",[201,202],{"name":193,"url":190},{"name":193,"url":203},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[26,205,27,28,206],"农业人工智能","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[209],{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},{"status":39,"locked":14,"version":40,"method":41,"evidence":211},{"candidates":212,"agricultural_evidence":46},[213,217,222,226,232],{"code":32,"score":45,"supported":46,"title":214,"abstract":215,"tags":216},[48],[50,52,48],[27,28],{"code":56,"score":218,"supported":46,"title":219,"abstract":220,"tags":221},15,[61],[60,61],[26],{"code":124,"score":13,"supported":46,"title":223,"abstract":224,"tags":225},[],[146,147,125],[206],{"code":64,"score":57,"supported":46,"title":227,"abstract":228,"tags":231},[],[229,230],"机器学习","machine learning",[205],{"code":150,"score":101,"supported":46,"title":233,"abstract":234,"tags":235},[],[153,154],[],[237,238],"IoT 边缘AI 精准灌溉","智慧灌溉 土壤墒情 传感器","IoT边缘AI精准灌溉-4671",{"doi":9,"openalex_id":241,"authors":242,"venue":193,"cited_by_count":91,"oa_url":190,"card":252,"direction":96,"ingested_from":98},"W7221411136",[243,245,247,249],{"name":244,"orcid":9},"Om Gaikwad",{"name":246,"orcid":9},"Shriraj Chavan",{"name":248,"orcid":9},"Krushnadev Gaikwad",{"name":250,"orcid":251},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":253,"method":254,"finding":255,"direction":96,"opportunity":256},"综述基于物联网与边缘AI的精准农业系统，实现实时灌溉优化与节水。","集成土壤湿度等IoT传感器与边缘AI，本地处理数据并云端存储可视化。","边缘AI可实时预测灌溉需求，减少水浪费并适应网络受限农村环境。","可探索边缘AI模型轻量化与多源数据融合，提升复杂农田环境下的灌溉决策精度。","2026-10-11T23:30:16.298334Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":269,"tags":271,"primary_topic":273,"secondary_topics":274,"classification":279,"search_phrases":313,"slug":316,"view_count":91,"doi":317,"paper":318,"created_at":350},4489,"Artificial Intelligence and Internet of Things in Smart Agriculture: A Bibliometric and Thematic Review of Data Infrastructures and Sustainability Implications","https:\u002F\u002Fdoi.org\u002F10.54175\u002Fhsustain5040036","Artificial intelligence (AI) is reshaping smart agriculture through prediction, automation, resource optimization, and data-driven decision-making. However, its effective and sustainable deployment depends on reliable Internet of Things (IoT) data infrastructures supporting sensing, connectivity, data acquisition, transmission, and distributed processing. This review maps research at the intersection of AI, IoT, and smart agriculture and examines its data-infrastructure requirements and sustainability implications. Drawing on 441 Scopus-indexed, peer-reviewed journal articles published between 2017 and 2025, the study combines bibliometric performance analysis, science mapping, and content-based thematic interpretation to identify publication trends, intellectual foundations, influential research fronts, emerging themes, and deployment challenges. The bibliometric analyses characterize AI–IoT research in smart agriculture, while IoT data infrastructure provides the principal analytical lens, and sustainability and responsible deployment are examined through interpretive synthesis. The findings reveal rapid publication growth and prominent themes involving connected sensing, predictive analytics, edge intelligence, remote sensing, smart irrigation, cybersecurity, blockchain, and edge–cloud processing. Persistent barriers include limited interoperability, inconsistent data quality, energy requirements, scalability constraints, cybersecurity and governance risks, affordability, and unequal adoption in resource-constrained contexts. The review concludes that sustainable AI-enabled agriculture requires reliable, secure, interoperable, energy-efficient, inclusive, and context-appropriate digital infrastructures.","人工智能（AI）正通过预测、自动化、资源优化和数据驱动决策重塑智慧农业。然而，其有效且可持续的部署依赖于可靠的物联网（IoT）数据基础设施，以支持感知、连接、数据采集、传输和分布式处理。本综述梳理了人工智能、物联网与智慧农业交叉领域的研究，并考察其数据基础设施需求及可持续性影响。基于2017年至2025年间发表的441篇Scopus索引同行评审期刊论文，本研究结合文献计量绩效分析、科学知识图谱和基于内容的主题解读，以识别发文趋势、知识基础、有影响力的研究前沿、新兴主题及部署挑战。文献计量分析刻画了智慧农业中人工智能—物联网研究的特征，物联网数据基础设施则作为主要分析视角，可持续性与负责任部署则通过解释性综合加以考察。研究发现发文量快速增长，突出主题涉及互联感知、预测分析、边缘智能、遥感、智能灌溉、网络安全、区块链及边云处理。持续存在的障碍包括互操作性有限、数据质量不一致、能源需求、可扩展性约束、网络安全与治理风险、可负担性以及在资源受限情境中的不均衡采用。本综述得出结论：可持续的人工智能赋能农业需要可靠、安全、可互操作、节能、包容且情境适宜的数字基础设施。","Highlights of Sustainability",79,{"impact":19,"substance":267,"depth":19,"authority":20,"freshness":115,"relevant":21,"comment":268},22,"基于441篇文献的AI-IoT智慧农业文献计量综述，系统梳理数据基础设施与可持续部署瓶颈，学术参考价值较高。",[270],{"name":264,"url":261},[26,205,27,28,272],"数据基础设施",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[275],{"code":64,"slug":276,"name":277,"path":278,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":280},{"candidates":281,"agricultural_evidence":46},[282,286,290,297,302,306],{"code":32,"score":45,"supported":46,"title":283,"abstract":284,"tags":285},[52],[50,52,48],[27,28],{"code":56,"score":218,"supported":46,"title":287,"abstract":288,"tags":289},[134],[26,134],[26],{"code":291,"score":198,"supported":46,"title":292,"abstract":294,"tags":296},"I7",[293],"bibliometric",[295,293],"文献计量",[],{"code":64,"score":113,"supported":46,"title":298,"abstract":300,"tags":301},[299],"artificial intelligence",[299],[205],{"code":124,"score":101,"supported":46,"title":303,"abstract":304,"tags":305},[],[146,125],[],{"code":307,"score":101,"supported":46,"title":308,"abstract":309,"tags":312},"I3",[],[310,311],"遥感","remote sensing",[],[314,315],"AI IoT 智慧农业 文献计量","农业物联网 数据基础设施","AIIoT智慧农业文献计量-4489","10.54175\u002Fhsustain5040036",{"doi":317,"openalex_id":319,"authors":320,"venue":264,"cited_by_count":91,"oa_url":344,"card":345,"direction":96,"ingested_from":98},"W7220924310",[321,324,327,329,332,335,338,341],{"name":322,"orcid":323},"Altyeb Ali Abaker Omer","https:\u002F\u002Forcid.org\u002F0000-0001-9420-9910",{"name":325,"orcid":326},"Yajie Dong","https:\u002F\u002Forcid.org\u002F0009-0001-2843-6135",{"name":328,"orcid":9},"Ibrahim Issa Mohamed Issa",{"name":330,"orcid":331},"Yousif Yahia Ahmed Abuker","https:\u002F\u002Forcid.org\u002F0009-0002-8111-9283",{"name":333,"orcid":334},"Mohamed Ibrahim Abdallh Babeker","https:\u002F\u002Forcid.org\u002F0009-0008-0904-9501",{"name":336,"orcid":337},"Alfadil Aboud Alfadil Hassan","https:\u002F\u002Forcid.org\u002F0009-0005-0656-1903",{"name":339,"orcid":340},"Zhiguo Shan","https:\u002F\u002Forcid.org\u002F0000-0002-6165-8258",{"name":342,"orcid":343},"Yinxue Shi","https:\u002F\u002Forcid.org\u002F0009-0002-1465-8666","https:\u002F\u002Fwww.hos.pub\u002Farticles\u002Fhsustain5040036\u002Fpdf",{"tldr":346,"method":347,"finding":348,"direction":96,"opportunity":349},"综述2017-2025年441篇文献，梳理AI与IoT在智慧农业中的数据基础设施与可持续性。","Scopus文献计量、科学映射与主题内容分析，以IoT数据基础设施为分析视角。","研究快速增长，但互操作性、数据质量、能耗、安全与公平采用仍是主要障碍。","可针对资源受限小农户场景，研究低能耗、可互操作且包容的农业IoT数据基础设施与治理机制。","2026-10-09T23:30:07.811940Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":9,"source_name":357,"source_url":354,"published_at":358,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":362,"tags":364,"primary_topic":366,"secondary_topics":367,"classification":368,"search_phrases":385,"slug":388,"view_count":158,"doi":389,"paper":390,"created_at":400},4352,"Hybrid Butterworth Filter and Autoencoder for Time-Series Anomaly Detection in Agricultural IoT Sensors","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23186427","Internet of Things (IoT) sensors deployed in agricultural environments frequently suffer from noise interference caused by environmental fluctuations, which degrades the performance of deep-learning-based anomaly detection. This work presents a hybrid pipeline combining a Butterworth low-pass filter and an autoencoder to detect anomalies from sensor time-series data. The Butterworth filter suppresses high-frequency environmental noise before data enters the autoencoder, improving the quality of input features. The autoencoder reconstructs normal sensor signals and identifies anomalous samples by measuring reconstruction error. We validate the proposed method on the NAB real-world sensor dataset and conduct ablation experiments to analyse the contribution of the filtering module. Experimental results show that the pre-filtering step effectively reduces false positives and improves detection accuracy compared with using a raw-data autoencoder baseline. This hybrid approach is lightweight and suitable for edge agricultural IoT devices with limited computational resources.","部署于农业环境中的物联网（IoT）传感器常受环境波动引起的噪声干扰，导致基于深度学习的异常检测性能下降。本文提出了一种将巴特沃斯低通滤波器与自编码器相结合的混合流程，用于从传感器时间序列数据中检测异常。巴特沃斯滤波器在数据进入自编码器之前抑制高频环境噪声，从而提高输入特征的质量。自编码器重建正常传感器信号，并通过测量重建误差来识别异常样本。我们在NAB真实传感器数据集上验证了所提方法，并进行消融实验以分析滤波模块的贡献。实验结果表明，与使用原始数据的自编码器基线相比，预滤波步骤有效减少了假阳性并提高了检测精度。该混合方法轻量，适用于计算资源有限的边缘农业物联网设备。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-06T00:00:00Z",72,{"impact":113,"substance":18,"depth":114,"authority":20,"freshness":143,"relevant":21,"comment":361},"面向农业IoT传感器时序数据的滤波+自编码器轻量异常检测方法，方法新颖、有消融实验与NAB数据集验证，适合边缘设备，但属细分技术进展，公共影响有限。",[363],{"name":357,"url":354},[26,205,27,28,365],"异常检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":369},{"candidates":370,"agricultural_evidence":46},[371,375,381],{"code":32,"score":45,"supported":46,"title":372,"abstract":373,"tags":374},[48],[50,52,48],[27,28],{"code":64,"score":376,"supported":46,"title":377,"abstract":378,"tags":380},4,[],[379],"深度学习",[205],{"code":56,"score":21,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[26],[386,387],"农业物联网 传感器 异常检测","Butterworth 自编码器 时序数据","农业物联网传感器异常检测-4352","10.5281\u002Fzenodo.23186427",{"doi":389,"openalex_id":391,"authors":392,"venue":357,"cited_by_count":91,"oa_url":354,"card":395,"direction":96,"ingested_from":98},"W7220674657",[393],{"name":394,"orcid":9},"Sicheng Su",{"tldr":396,"method":397,"finding":398,"direction":96,"opportunity":399},"提出巴特沃斯低通滤波与自编码器混合方法，检测农业物联网传感器时序异常。","巴特沃斯滤波预处理加自编码器重构误差，在NAB真实数据集上做消融实验。","预滤波有效降低误报并提升检测精度，方法轻量适合边缘农业物联网设备。","可探索自适应滤波参数与轻量自编码器在边缘端多传感器联合异常检测中的泛化能力。","2026-10-07T23:30:14.174878Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":357,"source_url":404,"published_at":407,"category":12,"cover_url":9,"hotness":195,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":415,"primary_topic":418,"secondary_topics":419,"classification":421,"search_phrases":437,"slug":440,"view_count":77,"doi":441,"paper":442,"created_at":452},2932,"Wireless Sensor Network Intrusion Detection: A Review of Statistical, Machine Learning, and Federated Methods","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22823847","Wireless Sensor Networks have become essential infrastructure across environmental monitoring, healthcare, smart agriculture, industrial automation, and smart city applications. However, their distributed, resource-constrained, and physically accessible design creates serious security risks. This paper surveys cybersecurity challenges and intrusion detection in WSNs, tracing the field from statistical methods to machine learning, deep learning, explainable AI, and federated learning. Our analysis shows that while signature-based and anomaly-based detection provide foundational security, they face significant limitations in dynamic, resource-limited WSN environments. Machine learning and deep learning achieve strong results, Random Forest reaching 98–99.5% accuracy and deep learning architectures exceeding 99% in some studies, but these figures depend heavily on dataset, preprocessing, and evaluation choices. Explainable AI techniques such as SHAP and LIME address the \"black-box\" problem by enabling transparent security decisions, while federated learning enables privacy-preserving, communication-efficient collaborative detection. Significant gaps remain: integrated frameworks that detect both data theft and eavesdropping are lacking, passive attacks receive limited attention, developing-region contexts are under-addressed, and energy-aware adaptive security remains aspirational. We identify future directions including lightweight deep models, energy-aware adaptive security, hybrid architectures, and robust, poisoning-resilient federated mechanisms.","无线传感器网络（Wireless Sensor Networks, WSNs）已成为环境监测、医疗健康、智慧农业、工业自动化和智慧城市应用中的关键基础设施。然而，其分布式、资源受限且物理可访问的设计带来了严重的安全风险。本文综述了无线传感器网络中的网络安全挑战与入侵检测，梳理了该领域从统计方法到机器学习、深度学习、可解释人工智能（Explainable AI）和联邦学习（Federated Learning）的发展脉络。我们的分析表明，尽管基于签名和基于异常的检测提供了基础性安全保障，但它们在动态且资源受限的无线传感器网络环境中面临显著局限。机器学习和深度学习方法取得了良好效果，随机森林（Random Forest）达到98–99.5%的准确率，深度学习架构在部分研究中超过99%，但这些数据在很大程度上取决于数据集、预处理和评估方式的选择。SHAP和LIME等可解释人工智能技术通过实现透明的安全决策，缓解了“黑箱”问题，而联邦学习则支持隐私保护且通信高效的协作检测。目前仍存在显著空白：缺乏同时检测数据窃取和窃听的集成框架，被动攻击受到的关注有限，发展中地区的应用场景研究不足，能量感知的自适应安全仍停留在设想阶段。我们指出了未来研究方向，包括轻量级深度模型、能量感知自适应安全、混合架构以及鲁棒且抗投毒的联邦机制。","2026-09-18T00:00:00Z",61,{"impact":115,"substance":19,"depth":17,"authority":13,"freshness":143,"relevant":21,"comment":410},"综述系统梳理WSN入侵检测从统计方法到联邦学习的演进，对农业物联网安全有参考价值，但属通用技术综述、非农业专属突破。",[412,413],{"name":357,"url":404},{"name":357,"url":414},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22823848",[26,27,29,416,417],"无线传感器网络","入侵检测",{"code":64,"slug":276,"name":277,"path":278,"parent_code":36},[420],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":422},{"candidates":423,"agricultural_evidence":46},[424,429,433],{"code":64,"score":19,"supported":46,"title":425,"abstract":426,"tags":428},[230],[379,229,230,427],"deep learning",[29],{"code":32,"score":17,"supported":46,"title":430,"abstract":431,"tags":432},[53],[51,53],[27,416],{"code":56,"score":57,"supported":46,"title":434,"abstract":435,"tags":436},[],[26,134],[26],[438,439],"无线传感器网络 入侵检测 联邦学习","无线传感器网络 农业物联网 入侵检测 智慧农业","无线传感器网络入侵检测联邦学习-2932","10.5281\u002Fzenodo.22823847",{"doi":441,"openalex_id":443,"authors":444,"venue":357,"cited_by_count":91,"oa_url":404,"card":447,"direction":96,"ingested_from":98},"W7213580003",[445],{"name":446,"orcid":9},"Jennifer Kyari-Ayele, Joseph Mom M, Iorkyase Ephraim T",{"tldr":448,"method":449,"finding":450,"direction":96,"opportunity":451},"综述无线传感器网络入侵检测从统计到机器学习、深度学习、可解释AI与联邦学习的方法演进与挑战。","文献综述，对比统计、机器学习、深度学习、可解释AI和联邦学习方法及性能。","机器学习检测精度高但依赖数据集，缺乏同时检测数据窃取与窃听的集成框架，被动攻击研究不足。","面向农业WSN的轻量级、能量感知自适应安全与抗投毒联邦检测框架尚属空白。","2026-09-19T23:30:11.466192Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":9,"source_name":459,"source_url":456,"published_at":460,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":471,"classification":472,"search_phrases":487,"slug":490,"view_count":376,"doi":491,"paper":492,"created_at":510},2649,"An Embedded Multi-Sensor IoT Platform for Agricultural Monitoring Using Meshtastic-Based LoRa Communication and Cloud Microservices","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26185839","The paper presents a low-cost agricultural monitoring platform based on embedded IoT sensing, LoRa mesh communication, and an Edge–Fog–Cloud architecture designed for real-time environmental monitoring in smart agriculture applications. The proposed system integrates distributed sensor nodes built around the Heltec Mesh Node T114 platform, combining an nRF52840 microcontroller with an SX1262 LoRa transceiver to support low-power, long-range communication in agricultural environments. Environmental data acquisition is performed using integrated BME280 temperature, humidity, and pressure sensors, capacitive soil moisture sensors, and TEMT6000 light sensors. A custom firmware architecture extending the Meshtastic framework was developed to support sensor integration, telemetry generation, packet forwarding, and energy-aware scheduling within the LoRa mesh network. The platform combines embedded telemetry modules, intermediate routing mechanisms, and cloud-based microservices responsible for data aggregation and processing. Experimental results showed stable radio-link conditions in the tested node-to-router configuration, with RSSI values around −55 dBm and SNR values of approximately 6–7 dB. Channel utilization varied between approximately 4% and 12%, while transmission airtime utilization remained below 5%. These results support the feasibility of the proposed sensing, communication, and data-processing architecture under the tested configuration. The architecture is designed to accommodate additional sensing and routing nodes, while multi-hop performance, long-term energy consumption, and network scalability remain to be evaluated in larger deployments.","本文提出了一种基于嵌入式物联网传感、LoRa网状通信以及边缘—雾—云架构的低成本农业监测平台，专为智慧农业应用中的实时环境监测而设计。所提出的系统集成了围绕Heltec Mesh Node T114平台构建的分布式传感器节点，该平台将nRF52840微控制器与SX1262 LoRa收发器相结合，以支持农业环境中的低功耗、远距离通信。环境数据采集通过集成的BME280温度、湿度和气压传感器、电容式土壤水分传感器以及TEMT6000光照传感器完成。开发了一种扩展Meshtastic框架的定制固件架构，以支持LoRa网状网络内的传感器集成、遥测生成、数据包转发和能量感知调度。该平台结合了嵌入式遥测模块、中间路由机制以及负责数据聚合与处理的云端微服务。实验结果表明，在测试的节点到路由器配置中，无线电链路条件稳定，RSSI值约为−55 dBm，SNR值约为6–7 dB。信道利用率在约4%至12%之间变化，而传输空中时间利用率保持在5%以下。这些结果支持了所提出的传感、通信和数据处理架构在测试配置下的可行性。该架构设计可容纳额外的传感和路由节点，而多跳性能、长期能耗和网络可扩展性仍有待于在更大规模部署中评估。","Sensors","2026-09-15T00:00:00Z",71,{"impact":113,"substance":463,"depth":114,"authority":20,"freshness":143,"relevant":21,"comment":464},20,"基于Meshtastic与LoRa组网的农业多传感器监测平台，含实测链路数据与云微服务架构，技术方案具体但尚属小规模验证，适合作为智慧农业物联网技术参考。",[466],{"name":459,"url":456},[26,27,28,468,469],"环境监测","LoRa",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":473},{"candidates":474,"agricultural_evidence":46},[475,479,483],{"code":32,"score":17,"supported":46,"title":476,"abstract":477,"tags":478},[48],[50,48],[27,28],{"code":56,"score":57,"supported":46,"title":480,"abstract":481,"tags":482},[],[26,134],[26],{"code":150,"score":101,"supported":46,"title":484,"abstract":485,"tags":486},[],[153,154],[],[488,489],"农业物联网 智慧农业 环境监测 边缘计算","农业物联网 智慧农业","农业物联网智慧农业环境监测边缘计算-2649","10.3390\u002Fs26185839",{"doi":491,"openalex_id":493,"authors":494,"venue":459,"cited_by_count":91,"oa_url":456,"card":505,"direction":96,"ingested_from":98},"W7213352397",[495,497,500,503],{"name":496,"orcid":9},"Cătălin Negulescu",{"name":498,"orcid":499},"Theodor Borangiu","https:\u002F\u002Forcid.org\u002F0000-0002-4801-1951",{"name":501,"orcid":502},"Silviu Răileanu","https:\u002F\u002Forcid.org\u002F0000-0003-2301-4854",{"name":504,"orcid":9},"Victor-Valentin Anghel",{"tldr":506,"method":507,"finding":508,"direction":96,"opportunity":509},"提出基于Meshtastic LoRa网状网络与云微服务的低成本农业多传感器监测平台。","Heltec T114节点、BME280\u002F土壤湿度\u002F光照传感器、LoRa mes","测试链路RSSI约-55dBm、SNR 6-7dB，信道占用4%-12%，验证架构可行性。","多跳性能、长期能耗与大规模网络扩展性尚未评估，可作后续研究切入点。","2026-09-16T23:30:12.438822Z"]