[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-NB-IoT":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"NB-IoT","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":29,"tags":31,"search_phrases":36,"slug":39,"view_count":40,"doi":41,"paper":42,"created_at":67},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":20,"substance":24,"depth":25,"authority":26,"freshness":27,"relevant":7,"comment":28},22,18,13,9,"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。",[30],{"name":17,"url":13},[32,33,34,35,4],"智慧农业","物联网","水产养殖","水质监测",[37,38],"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":41,"openalex_id":43,"authors":44,"venue":17,"cited_by_count":40,"oa_url":13,"card":60,"direction":64,"ingested_from":66},"W7213982361",[45,48,51,54,57],{"name":46,"orcid":47},"Tsai-Chen Yang","https:\u002F\u002Forcid.org\u002F0009-0007-2271-6690",{"name":49,"orcid":50},"Yin-Tzu Huang","https:\u002F\u002Forcid.org\u002F0000-0001-8679-091X",{"name":52,"orcid":53},"Yu‐Fang Chung","https:\u002F\u002Forcid.org\u002F0000-0002-7373-7201",{"name":55,"orcid":56},"Tzer‐Shyong Chen","https:\u002F\u002Forcid.org\u002F0000-0001-8915-5057",{"name":58,"orcid":59},"Chao‐Tung Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9579-4426",{"tldr":61,"method":62,"finding":63,"direction":64,"opportunity":65},"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"]