Scholarlink

<p>Solar-powered Internet of Things (IoT) water-quality monitoring systems support real-time surveillance in off-grid environments, but evidence across sensing, power management, communication, and analytics remains fragmented. This review analysed 90 studies published between 2012 and 2025 that integrated sensing technologies, IoT architectures, and machine learning (ML) approaches. Over 80% of systems monitor physicochemical parameters such as pH, turbidity, temperature, electrical conductivity, and dissolved oxygen, while chemical contaminant detection appears in about 14% of studies and microbial monitoring in less than 10%. Typical photovoltaic capacities range from 10–50 W with 2–20 Ah batteries, yet fewer than 15% implement energy-aware sampling. LoRa/LoRaWAN dominates communication, and only 18% of systems use ML analytics. Limitations include fragmented sensing platforms, limited energy-adaptive design, and short deployments. A solar-centric co-design framework is proposed for scalable autonomous monitoring.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Solar-powered multi-contaminant detection for real-time water quality monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

查看全文
  • Joseph Malisaba,
  • Obinna Onyebuchi Barah,
  • Samuel George Onep,
  • David Mushabe,
  • Emmanuel Ninsiima

摘要

Solar-powered Internet of Things (IoT) water-quality monitoring systems support real-time surveillance in off-grid environments, but evidence across sensing, power management, communication, and analytics remains fragmented. This review analysed 90 studies published between 2012 and 2025 that integrated sensing technologies, IoT architectures, and machine learning (ML) approaches. Over 80% of systems monitor physicochemical parameters such as pH, turbidity, temperature, electrical conductivity, and dissolved oxygen, while chemical contaminant detection appears in about 14% of studies and microbial monitoring in less than 10%. Typical photovoltaic capacities range from 10–50 W with 2–20 Ah batteries, yet fewer than 15% implement energy-aware sampling. LoRa/LoRaWAN dominates communication, and only 18% of systems use ML analytics. Limitations include fragmented sensing platforms, limited energy-adaptive design, and short deployments. A solar-centric co-design framework is proposed for scalable autonomous monitoring.