Use of Internet of Things in water resources applications: challenges and future directions: a critical review

Water is one of the most precious natural resources on the earth and its planning and management remains always a challenging task. With the advancement of new techniques such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML) and utilization of advanced sensors, the management of water resources has improved manifold. This paper provides a comprehensive review of the literatures on the application of IoT combined with AI/ML techniques, printed circuit board/protocols/technologies such as Arduino UNO, ESP8266, Raspberry Pi, TCP, and LoRaWAN in different domains of water resources engineering (WRE) from the last decades during years 1998 to 2024. The main objective of this review paper is to highlight the increasing role of IoT applications in the field of WRE. With the ability to collect and monitor real-time data, IoT technologies are becoming essential for timely and informed decision-making. This review covers the use of IoT in various areas such as water quality monitoring, smart irrigation, groundwater quality/level assessment, domestic/industrial wastewater monitoring, and urban/flash flood monitoring. It explores how these innovations have contributed to more efficient, accurate, and sustainable approaches to water resource management. In this paper, 166 research papers are reviewed which are published in leading journals during the specified period and explores the key challenges, current status and future directions in the potential applications of IoT in various domains of WRE. The review revealed that the most highly regarded IoT sub-vertical is water quality monitoring, which is followed by smart irrigation systems, ground water quality monitoring, monitoring of domestic/industrial wastewater quality, and monitoring of urban/flash floods. It also revealed that pH sensors were used in 47% of the cases, turbidity sensors in 44%, and ML techniques in 42% for predictive analysis, often combined with communication protocols such as LoRaWAN, which appeared in 36% of the studies.