A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deployment

被引:0
|
作者
Pagano, Antonino [1 ,3 ]
Garlisi, Domenico [2 ,3 ]
Tinnirello, Ilenia [1 ,3 ,4 ]
Giuliano, Fabrizio [1 ,3 ]
Garbo, Giovanni [1 ,3 ]
Falco, Mariana [1 ]
Cuomo, Francesca [5 ]
机构
[1] Univ Palermo, Dept Engn, Palermo, Italy
[2] Univ Palermo, Dept Math & Comp Sci, Palermo, Italy
[3] Consorzio Nazl Interuniv Telecomunicazioni, CNIT, Parma, Italy
[4] Univ Catania, Dept Elect Elect & Comp Engn DIEEI, Catania, Italy
[5] Sapienza Univ Rome, DIET Dept, Rome, Italy
关键词
Internet of Things (IoT); Low power wide area network (LPWAN); Massive ioT; Wireless sensor node (WSN); Edge computing (EC); Water distribution system (WDS); Water distribution network (WDN); Water monitoring; Water management; ANOMALY DETECTION; SENSOR PLACEMENT; NETWORKS; ARCHITECTURE; PERFORMANCE; INTEGRATION; PLATFORM;
D O I
10.1016/j.adhoc.2024.103714
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This survey explores the convergence of Internet of Things (IoT) technologies with Water Distribution Systems (WDSs), focusing on large-scale deployments and the role of edge computing (EC). Effective water management increasingly relies on IoT monitoring, resulting in massive deployments and the generation of Big Data. While previous research has examined these topics individually, this work integrates them into a comprehensive analysis. We systematically reviewed 255 studies on IoT in WDS, identifying key challenges such as interoperability, scalability, energy efficiency, network coverage, and reliability. We also examined technologies like LPWAN and the growing use of EC for real-time data processing. In large-scale WDS scenarios, where vast amounts of data are generated, we highlighted the importance of technologies like NB-IoT, SigFox, and LoRaWAN due to their low power consumption and wide coverage. Based on our findings, we provide guidelines for sustainable, large-scale IoT deployment in WDS, emphasizing the need for edge data processing to reduce cloud dependency, improve scalability, and enable smarter cities and digital twins.
引用
收藏
页数:23
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