[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3163":3,"related-3163":63},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":62},3163,"An NB-IoT-Based Architecture with Spatial-Statistical Analytics for Cross-Domain Air and Water Quality Monitoring in Aquaculture and Aquatic Environments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26185964","The rapid expansion of smart agriculture and precision aquaculture necessitates continuous, high-resolution environmental monitoring to optimize ecosystem stability and prevent catastrophic biomass loss. Conventional Internet of Things (IoT) solutions routinely treat atmospheric and aquatic parameters as isolated domains, neglecting the dynamic physicochemical coupling occurring across the air–water boundary layer. To overcome this domain fragmentation, this study presents an integrated edge-cloud telemetry architecture designed for concurrent, multi-domain environmental monitoring and cross-domain spatial-statistical analysis. The proposed framework employs low-cost, multi-sensor edge nodes integrated with Narrowband IoT (NB-IoT) cellular communication, achieving high signal penetration, energy-efficient operation, and direct base-station connectivity without local gateway dependencies. The system continuously acquires atmospheric parameters (temperature, relative humidity, particulate matter PM1.0\u002FPM2.5\u002FPM10, ozone O3, total volatile organic compounds TVOC, equivalent CO2, Air Quality Index AQI, and Ultraviolet Index UVI) alongside aquatic indicators (water temperature, pH, dissolved oxygen DO, electrical conductivity EC, and turbidity). Telemetry is streamed via Message Queuing Telemetry Transport (MQTT) to a centralized MySQL cloud database, providing real-time Grafana dashboards, spatial Inverse Distance Weighting (IDW) mapping, and automated multi-channel alerting via LINE Notify and email. The architecture was deployed and validated across the Tunghai University aquatic research facility, capturing n = 14,400 synchronized 1-min observations (with an initial raw Packet Delivery Rate of 99.24%). Statistical evaluations accounting for temporal autocorrelation (Neff≈892) and False Discovery Rate correction revealed significant cross-domain associations (padj\u003C0.001), notably an inverse association (r=−0.782, ρ=−0.794, τ=−0.612) between ambient air temperature and aquatic dissolved oxygen physically consistent with Henry’s Law of gas solubility, an inverse association (r=−0.763) between atmospheric humidity and dissolved oxygen, and a positive association (r=+0.789, ρ=+0.812) between humidity and aquatic turbidity. First-order partial correlation analysis (rTa,DO∣Tw=−0.172) confirmed that water temperature serves as the primary thermal mediator of dissolved oxygen depletion. By synergizing low-power NB-IoT telemetry with robust multi-domain analytics, this work provides a scalable, empirical foundation for transitioning from reactive threshold alerting to proactive predictive management in precision aquaculture.","智慧农业与精准水产养殖的快速扩张，亟需持续、高分辨率的环境监测，以优化生态系统稳定性并防止灾难性生物量损失。传统物联网（IoT）方案通常将大气与水体参数视为彼此孤立的领域，忽视了跨气–水边界层发生的动态物理化学耦合。为克服这种领域割裂，本研究提出了一种集成式边缘–云遥测架构，用于并发、多域环境监测与跨域空间统计分析。所提框架采用低成本多传感器边缘节点，并集成窄带物联网（NB-IoT）蜂窝通信，从而实现高信号穿透、节能运行以及无需本地网关依赖的直接基站连接。该系统持续采集大气参数（温度、相对湿度、颗粒物PM1.0\u002FPM2.5\u002FPM10、臭氧O3、总挥发性有机物TVOC、等效CO2、空气质量指数AQI和紫外线指数UVI）以及水体指标（水温、pH、溶解氧DO、电导率EC和浊度）。遥测数据通过消息队列遥测传输（MQTT）流式传输至集中式MySQL云数据库，提供实时Grafana仪表板、空间反距离加权（IDW）制图，以及通过LINE Notify和电子邮件实现的多通道自动告警。该架构已在东海大学水产研究设施中部署并验证，采集了n = 14,400条同步1分钟观测数据（初始原始数据包投递率为99.24%）。考虑时间自相关（Neff≈892）和错误发现率校正的统计评估揭示了显著的跨域关联（padj\u003C0.001），尤其是环境气温与水体溶解氧之间的负相关（r=−0.782，ρ=−0.794，τ=−0.612），这在物理上与亨利气体溶解度定律一致；大气湿度与溶解氧之间的负相关（r=−0.763）；以及湿度与水体浊度之间的正相关（r=+0.789，ρ=+0.812）。一阶偏相关分析（rTa,DO∣Tw=−0.172）证实，水温是溶解氧耗竭的主要热中介因素。通过将低功耗NB-IoT遥测与稳健的多域分析相结合，该",null,"Sensors","2026-09-20T00:00:00Z","论文",10,false,72,{"impact":13,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,13,9,1,"NB-IoT边缘云架构实现空气-水质跨域同步监测，1.44万条实测数据与统计结论扎实，对精准水产养殖有参考价值，但属单点验证性研究，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","物联网","水产养殖","水质监测","NB-IoT",[32,33],"NB-IoT 水产养殖 水质监测","东海大学 空气质量 溶解氧","NB-IoT水产养殖水质监测-3163",0,"10.3390\u002Fs26185964",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213982361",[40,43,46,49,52],{"name":41,"orcid":42},"Tsai-Chen Yang","https:\u002F\u002Forcid.org\u002F0009-0007-2271-6690",{"name":44,"orcid":45},"Yin-Tzu Huang","https:\u002F\u002Forcid.org\u002F0000-0001-8679-091X",{"name":47,"orcid":48},"Yu‐Fang Chung","https:\u002F\u002Forcid.org\u002F0000-0002-7373-7201",{"name":50,"orcid":51},"Tzer‐Shyong Chen","https:\u002F\u002Forcid.org\u002F0000-0001-8915-5057",{"name":53,"orcid":54},"Chao‐Tung Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9579-4426",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"提出NB-IoT边缘云架构，同步监测空气与水质并做跨域空间统计分析。","低成本多传感器NB-IoT节点、MQTT+MySQL云、IDW空间映射与偏相关分","气温与溶解氧显著负相关，水温是溶解氧消耗的主要热中介。","智慧农业 \u002F 农业物联网","可探索跨域耦合预警模型，将气水界面关联用于养殖缺氧风险提前预测。","openalex","2026-09-22T23:30:12.820712Z",{"total":64,"page":21,"page_size":64,"items":65},6,[66,98,138,166,204,245],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":9,"content":9,"source_name":71,"source_url":9,"published_at":72,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":74,"sources":80,"tags":82,"search_phrases":85,"slug":88,"view_count":35,"doi":9,"paper":89,"created_at":97},1504,"Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring","https:\u002F\u002Fwww.journaljsrr.com\u002Findex.php\u002FJSRR\u002Farticle\u002Fview\u002F4484","研究构建了集成物联网传感器、无人机、深度学习与 Web\u002F移动应用的农业 AI 系统，用于作物与水产健康监测。数据集包括 7 万张图像与 3 个月的物联网传感器数据，按 80:10:10 划分训练\u002F验证\u002F测试。模型结果：YOLOv8 害虫识别准确率 92.1%，CNN 病害识别准确率 93.4%，虾类识别 88.4%；整体系统准确率 97%，传感器预测 RMSE 为 1.2。结论指出 AI、物联网与无人机技术可有效检测生物与非生物胁迫，对可持续农业系统形成支撑。原文标题：Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring。","Journal of Scientific Research and Reports 32(9) 468-478","2026-09-02T00:00:00Z",69,{"impact":18,"substance":75,"depth":76,"authority":77,"freshness":78,"relevant":21,"comment":79},20,17,12,2,"研究集成物联网、无人机与深度学习，实现作物和水产健康监测，数据详实，模型准确率高，对智慧农业有实质参考价值。",[81],{"name":71,"url":69},[26,83,84,27,28],"无人机","农业人工智能",[86,87],"农业人工智能 智慧农业 水产养殖 无人机","农业人工智能 智慧农业","农业人工智能智慧农业水产养殖无人机-1504",{"doi":9,"openalex_id":9,"authors":90,"venue":9,"cited_by_count":35,"oa_url":9,"card":91,"direction":59,"ingested_from":96},[],{"tldr":92,"method":93,"finding":94,"direction":59,"opportunity":95},"构建集成物联网、无人机与深度学习的农业AI系统，实现作物与水产健康监测。","集成物联网传感器、无人机、YOLOv8与CNN，使用7万图像及3个月传感器数据训","系统准确率97%，害虫识别92.1%，病害93.4%，虾类88.4%，传感器预测RMSE1.2。","可探索多源数据融合与边缘计算，提升实时监测精度，并扩展至更多水产种类与复杂环境。","agent","2026-09-03T00:06:44.772165Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":112,"tags":114,"search_phrases":118,"slug":121,"view_count":35,"doi":122,"paper":123,"created_at":137},3158,"Internet of things-based smart irrigation system using soil moisture and weather data","https:\u002F\u002Fdoi.org\u002F10.12928\u002Ftelkomnika.v24i5.27889","Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.","农业中的水资源浪费仍然是一项重大挑战，因为灌溉实践往往依赖固定时间表，而非实际田间条件。本研究提出了一种基于物联网（IoT）的智能灌溉系统，旨在通过实时监测和自动灌溉控制来提高用水效率。该系统将电容式土壤湿度传感器与从在线应用程序编程接口（API）获取的天气信息相结合，同时所有数据处理均在Raspberry Pi边缘设备上本地完成。系统采用基于规则的决策机制，将土壤状况分为干燥、适宜和湿润三类，并根据土壤湿度水平和降雨预报确定适当的灌溉措施。该系统使用低成本和易于获取的组件实现，并在受控条件下进行了测试，土壤湿度水平范围约为0%至85%以上。实验结果表明，系统能够一致地分类临界干燥、适宜和临界湿润状况，从而在不同情景下实现适当的灌溉响应。此外，电子邮件通知仅在临界状况下生成，而在适宜湿度水平下未触发任何警报，表明系统运行稳定可靠。所提出的系统为支持高效灌溉管理和可持续农业实践提供了一种实用且具有成本效益的解决方案。","TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z",58,{"impact":108,"substance":109,"depth":110,"authority":77,"freshness":108,"relevant":21,"comment":111},8,16,14,"低成本物联网智能灌溉系统，方法清晰、结论可靠，对节水农业有实用参考价值，但属常规技术验证类论文，影响范围有限。",[113],{"name":104,"url":101},[26,27,115,116,117],"智能灌溉","土壤墒情","节水农业",[119,120],"IoT 智能灌溉 土壤湿度","Raspberry Pi 边缘计算 灌溉","IoT智能灌溉土壤湿度-3158","10.12928\u002Ftelkomnika.v24i5.27889",{"doi":122,"openalex_id":124,"authors":125,"venue":104,"cited_by_count":35,"oa_url":101,"card":132,"direction":59,"ingested_from":61},"W7213907872",[126,128,130],{"name":127,"orcid":9},"Zakarie Abdi Mohamud",{"name":129,"orcid":9},"Rozeha Binti A. Rashid",{"name":131,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":133,"method":134,"finding":135,"direction":59,"opportunity":136},"基于物联网与土壤湿度及天气数据，实现低成本自动灌溉决策系统。","电容式土壤湿度传感器、天气API、Raspberry Pi边缘计算与规则决策。","系统能准确分类干、适宜、湿状态，仅在临界条件触发灌溉与邮件通知。","可引入机器学习预测土壤湿度动态，优化规则阈值并扩展至多作物多区域验证。","2026-09-22T23:30:10.956765Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":9,"content":9,"source_name":143,"source_url":141,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":145,"sources":147,"tags":149,"search_phrases":152,"slug":155,"view_count":35,"doi":156,"paper":157,"created_at":165},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture","2026-09-22T00:00:00Z",{"impact":77,"substance":18,"depth":109,"authority":19,"freshness":13,"relevant":21,"comment":146},"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[148],{"name":143,"url":141},[26,84,150,27,151],"产量预测","玉米",[153,154],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":156,"openalex_id":158,"authors":159,"venue":143,"cited_by_count":35,"oa_url":141,"card":9,"direction":59,"ingested_from":61},"W7213978365",[160,163],{"name":161,"orcid":162},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":164,"orcid":9},"Yasinta Nzogera","2026-09-22T23:30:10.178097Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":174,"sources":177,"tags":179,"search_phrases":181,"slug":184,"view_count":35,"doi":185,"paper":186,"created_at":203},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",{"impact":77,"substance":175,"depth":76,"authority":19,"freshness":20,"relevant":21,"comment":176},21,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[178],{"name":172,"url":169},[26,84,180,27,115],"机器学习",[182,183],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":185,"openalex_id":187,"authors":188,"venue":172,"cited_by_count":35,"oa_url":169,"card":198,"direction":59,"ingested_from":61},"W7213546497",[189,192,195],{"name":190,"orcid":191},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":193,"orcid":194},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":196,"orcid":197},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":199,"method":200,"finding":201,"direction":59,"opportunity":202},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":211,"score_detail":212,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":35,"doi":223,"paper":224,"created_at":244},2913,"A quantum-inspired multi-objective learning framework for real-time sustainable aquaculture water quality prediction","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827991","Management of sustainable aquaculture necessitates accurate prediction of water quality parameters, since environmental variability is an important factor in determining aquatic productivity and ecological stability. Traditional machine learning systems are usually limited by inefficient parameter optimization, feature overlap, and limited adaptability to non-linear and time-varying environmental trends. To address these issues, a new concept is proposed, which is called Quantum-Inspired Aquaculture Optimization Network (Q-AQUAOptNet). To improve the process of feature selection, hyperparameter optimization, and model stability before temporal prediction, the proposed architecture combines sustainability index modeling and quantum-inspired multi-objective evolutionary optimization. The optimization approach balances exploration and exploitation, enhancing the learning ability of the prediction model. Implementation of the framework was done through Python-based simulation tools in preprocessing, optimization, and performance evaluation. The created system had a Root Mean Square Error of 0.24, a Mean Absolute Error of 0.21, and an R 2 of 0.97, which means that it has effective predictive power and a great ability to explain the variance. Integrating evolutionary optimization and temporal learning improves generalization performance and reduces prediction uncertainty. The findings demonstrate that Q-AQUAOptNet provides effective predictive performance within a simulation-based framework and shows potential as a sustainability-oriented intelligent water-quality monitoring and decision support system for aquaculture applications.","可持续水产养殖管理需要准确预测水质参数，因为环境变异性是决定水生生产力和生态稳定性的重要因素。传统机器学习系统通常受限于参数优化效率低、特征重叠以及对非线性和时变环境趋势的适应能力有限。为解决这些问题，提出了一种新概念，称为量子启发水产养殖优化网络（Quantum-Inspired Aquaculture Optimization Network，Q-AQUAOptNet）。为在时间预测之前改进特征选择、超参数优化和模型稳定性，所提出的架构结合了可持续性指数建模和量子启发多目标进化优化。该优化方法平衡了探索与利用，增强了预测模型的学习能力。该框架的实现通过基于Python的仿真工具完成，涵盖预处理、优化和性能评估。所构建系统的均方根误差为0.24，平均绝对误差为0.21，R²为0.97，这意味着其具有有效的预测能力和较强的方差解释能力。将进化优化与时间学习相结合，提高了泛化性能并降低了预测不确定性。研究结果表明，Q-AQUAOptNet在基于仿真的框架内提供了有效的预测性能，并显示出作为面向可持续性的智能水质监测与决策支持系统应用于水产养殖的潜力。","Frontiers in Sustainable Food Systems",71,{"impact":77,"substance":75,"depth":76,"authority":19,"freshness":20,"relevant":21,"comment":213},"提出量子启发多目标优化网络用于水产养殖水质预测，仿真指标较优，属智慧渔业细分领域的方法学进展，但尚处仿真阶段、缺乏真实场景验证。",[215],{"name":210,"url":207},[26,84,28,217,218],"多目标优化","水质预测",[220,221],"Q-AQUAOptNet 水产养殖 水质预测","农业人工智能 多目标优化 智慧农业 水产养殖","Q-AQUAOptNet水产养殖水质预测-2913","10.3389\u002Ffsufs.2026.1827991",{"doi":223,"openalex_id":225,"authors":226,"venue":210,"cited_by_count":35,"oa_url":207,"card":239,"direction":59,"ingested_from":61},"W7213532503",[227,230,233,235,237],{"name":228,"orcid":229},"Abdel‐Haleem Abdel‐Aty","https:\u002F\u002Forcid.org\u002F0000-0002-6763-2569",{"name":231,"orcid":232},"Ali Jaber Almalki","https:\u002F\u002Forcid.org\u002F0009-0004-1359-2612",{"name":234,"orcid":9},"Sara A. Ghorashi",{"name":236,"orcid":9},"Betty Wan Niu Voon",{"name":238,"orcid":9},"Mohamed Hafez",{"tldr":240,"method":241,"finding":242,"direction":59,"opportunity":243},"提出量子启发多目标优化网络Q-AQUAOptNet，实现水产养殖水质实时预测。","量子启发多目标进化优化结合时间预测与可持续指数建模，Python仿真。","RMSE 0.24、MAE 0.21、R² 0.97，预测精度高且泛化好。","可探索真实养殖场部署与多源传感器融合，验证量子启发优化在边缘端的实时性。","2026-09-19T23:30:07.985707Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":253,"is_selected":14,"score":254,"score_detail":255,"sources":257,"tags":263,"search_phrases":266,"slug":269,"view_count":35,"doi":270,"paper":271,"created_at":283},2865,"AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809198","Abstract The proliferation of Internet of Things (IoT) devices in smart cities, industrial automation, and healthcare monitoring has created an unprecedented demand for sustainable energy solutions. Traditional battery-powered IoT deployments face significant challenges including limited operational lifespan, environmental hazards from battery disposal, and high maintenance costs associated with manual replacement. This study presents a novel AI-enabled wireless power transfer (WPT) architecture that leverages machine learning algorithms to optimize energy delivery, predict device energy demands, and autonomously manage power distribution across large-scale IoT ecosystems. We propose a three-tier architecture comprising (1) an intelligent RF energy harvesting layer with adaptive rectenna arrays, (2) a reinforcement learning-based power allocation engine, and (3) a cloud-native energy orchestration platform. Through extensive simulation and prototype validation across three distinct IoT scenarios smart agriculture, industrial asset monitoring, and wearable health networks we demonstrate that the proposed system achieves 78.4% energy transfer efficiency (a 34% improvement over conventional directional WPT), reduces network energy waste by 62%, and extends device operational lifespan by 4.7× compared to battery-dependent counterparts. Our findings establish that AI-driven dynamic optimization of WPT parameters including beam-forming angles, transmission power, and duty cycling enables scalable, sustainable IoT deployments that were previously infeasible. This research contributes to the emerging field of intelligent energy harvesting networks and provides a replicable framework for next-generation green IoT infrastructure. Keywords: wireless power transfer, Internet of Things, machine learning, energy harvesting, sustainable computing, reinforcement learning, smart cities, green communication. References Bi, S., Zeng, Y., & Zhang, R. (2019). Wireless powered communication networks: Techniques, applications, and future directions. IEEE Communications Surveys & Tutorials, 21(2), 1324–1353. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2018.2881084 Gartner. (2023). Forecast: Internet of Things—Endpoints and associated services, worldwide, 2023. Gartner Research. Kim, S., Park, J., & Lee, K. (2023). AI-driven optimization of RF energy harvesting for IoT devices using deep reinforcement learning. IEEE Internet of Things Journal, 10(4), 3124–3138. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2023.3245678 Ku, M.-L., Li, W., Chen, Y., & Liu, K. J. R. (2016). Advances in energy harvesting communications: Past, present, and future challenges. IEEE Communications Surveys & Tutorials, 18(2), 1384–1412. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2015.2497328 Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2021). Wireless charger networking for mobile devices: Fundamentals, standards, and applications. IEEE Wireless Communications, 22(2), 32–41. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMWC.2015.7091061 R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3.1) [Computer software]. R Foundation for Statistical Computing. https:\u002F\u002Fwww.R-project.org\u002F Statista. (2023). Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2030. https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F1183457\u002Fiot-connected-devices-worldwide\u002F Tran, N. H., Hoang, D. T., Niyato, D., Nguyen, C. M., & Han, Z. (2021). The roadmap to 6G: AI-empowered wireless networks. IEEE Communications Magazine, 59(1), 112–117. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMCOM.001.2000406 United Nations Environment Programme. (2022). Global e-waste monitor 2022: Electronic waste management in the circular economy. United Nations Publications. Zhang, J., Guo, H., & Liu, H. (2022). Intelligent reflecting surface aided wireless power transfer for IoT devices. IEEE Transactions on Communications, 70(5), 3381–3396. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTCOMM.2022.3156789 How to Cite Lucky Joseph, O., & Osaremwinda, O. (2026). AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS. GPH-International Journal of Computer Science and Engineering, 9(1), 176-188. https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199","摘要 物联网（IoT）设备在智慧城市、工业自动化和医疗健康监测中的激增，催生了对可持续能源解决方案前所未有的需求。传统的电池供电物联网部署面临诸多重大挑战，包括运行寿命有限、电池处置带来的环境危害，以及人工更换电池所产生的高昂维护成本。本研究提出了一种新颖的人工智能赋能无线能量传输（WPT）架构，利用机器学习算法优化能量传输、预测设备能量需求，并在大规模物联网生态系统中自主管理功率分配。我们提出了一种三层架构，包括：（1）具有自适应整流天线阵列的智能射频能量收集层；（2）基于强化学习的功率分配引擎；（3）云原生能量编排平台。通过在三种不同物联网场景——智慧农业、工业资产监测和可穿戴健康网络——中进行大量仿真和原型验证，我们证明所提出的系统实现了78.4%的能量传输效率（较传统定向WPT提升34%），将网络能量浪费降低了62%，并将设备运行寿命较依赖电池的同类设备延长了4.7倍。我们的研究结果表明，对WPT参数（包括波束成形角度、发射功率和占空比）进行人工智能驱动的动态优化，能够实现此前不可行的大规模、可持续物联网部署。本研究为智能能量收集网络这一新兴领域作出了贡献，并为下一代绿色物联网基础设施提供了可复制的框架。关键词：无线能量传输，物联网，机器学习，能量收集，可持续计算，强化学习，智慧城市，绿色通信。参考文献 Bi, S., Zeng, Y., & Zhang, R. (2019). Wireless powered communication networks: Techniques, applications, and future directions. IEEE Communications Surveys & Tutorials, 21(2), 1324–1353. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2018.2881084 Gartner. (2023). Forecast: Internet of Things—Endpoints and associated services, worldwide, 2023. Gartner Research. Kim, S., Park, J., & Lee, K. (2023). AI-driven optimization of RF energy harvesting for IoT devices using deep reinforcement learning. IEEE Internet of Things Journal, 10(4), 3124–3138. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2023","Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z",40,77,{"impact":18,"substance":17,"depth":18,"authority":13,"freshness":20,"relevant":21,"comment":256},"论文提出AI驱动的无线能量传输三层架构，在智慧农业等场景验证能效提升34%、设备寿命延长4.7倍，对农业物联网可持续供电有参考价值，但属仿真与原型验证阶段，产业落地尚早。",[258,259,261],{"name":251,"url":248},{"name":251,"url":260},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199",{"name":251,"url":262},"https:\u002F\u002Fwww.gphjournal.org\u002Findex.php\u002Fcse\u002Farticle\u002Fview\u002F2581",[26,84,27,264,265],"无线能量传输","绿色通信",[267,268],"AI 无线能量传输 物联网","智能农业 能量采集 可持续","AI无线能量传输物联网-2865","10.5281\u002Fzenodo.22809198",{"doi":270,"openalex_id":272,"authors":273,"venue":251,"cited_by_count":35,"oa_url":248,"card":278,"direction":59,"ingested_from":61},"W7213495899",[274,276],{"name":275,"orcid":9},"Lucky Joseph Ogbogbo",{"name":277,"orcid":9},"Osaremwinda OMOROGIUWA",{"tldr":279,"method":280,"finding":281,"direction":59,"opportunity":282},"提出AI驱动的无线供电三层架构，优化大规模IoT设备能量传输与分配。","强化学习功率分配、自适应整流天线阵列与云原生编排平台仿真验证。","能量传输效率达78.4%，浪费减少62%，设备寿命延长4.7倍。","可将该WPT架构落地农田传感器网络，研究作物环境下的能量预测与波束自适应优化。","2026-09-18T23:30:08.902438Z"]