# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 1 Introduction  
Internet of Things (IoT) connects various industrial actuators, devices, and people at work. IoT provide additional insight into industrial applications, as well as minimize human labour and time and create a path for Industry 4.0. [1, 2]. Artificial intelligence (AI) technologies are used in the IoT to process and analyse data from various sources and perform advanced predictive analytics such as predictive maintenance, fault class prediction, and demand forecasting [3]. It is challenging to create an accurate mathematical model for intelligent IoT applications since they are an enormous connected and complex process that produces a large volume of multi-feature data [4]. However, AI algorithms can extract critical information without needing a thorough knowledge of the underlying physical structure of the system. As a result, AI algorithms are well-suited to IoT in terms of self-adaptation, and self-learning [5]. AI technologies play a crucial role in IoT because of the highly dynamic system state and data structure and the time-varying monitoring parameters [6]. In the IoT, for example, predictive maintenance uses machine learning to detect abnormalities in systems and then anticipate device failure by correlating and evaluating the change in the pattern [7]. IoT devices, such as sensors, are of limited computation resources, hence, they have been mainly used for data collection which includes monitoring and transmitting data to robust systems such as cloud-based or centralised, for storage and processing [8]. Thus, a common IoT data analysis processing occurs at the cloud then communicating the findings to IoT devices for informed decision. Typically, data from sensor data are transported tens of thousands of kilometres to a data centrewhere they are stored and analysed. This approach increases data transfer latencies as well as network traffic as IoT devices have limited computation capabilities, the computation of data analytics cannot be conducted entirely on the devices [9]. As a result, bringing intelligence to the edge is without doubt a potential development trend. Integrating edge and cloud computing offers the promise to minimise IoT network traffic and related delay while enabling complex data analytics operations to be performed [10; 11; 12]. The widespread use of Internet of Things and robust cloud services has contributed to the demand for edge computing, in which data processing takes place in part at the network edge rather than entirely in the cloud [13]. Edge computing helps to solve issues such as bandwidth costs, latency, power consumption of mobile devices, security and privacy. Edge computing uses small data centres on the network's edge to deliver distributed computing services and it acts as a middle interface between a cloud server and IoT devices/sensors [14; 15]. In edge computing, the cloud takes data from the collected databases, as it has historically done, as well as from sensing devices and smartphones. The devices serve as both data consumers and data generators. As a result, queries between end devices and the cloud can be bidirectional, rather than merely from end devices to the cloud. To minimise traffic from devices to the cloud, nodes at the network edge perform a variety of computational functions, including data processing, device management, caching, and privacy protection (see Figure 1). Edge computing, in contrast to mobile cloud computing, allows for real-time data analytics while maintaining privacy, expands network capabilities, and avoids congestion in backbone networks and the internet core [16; 17]. This paper investigates the use of edge computing for data analytics by integrating the edge with cloud computing to deliver deep learning applications. Four different scenarios have been used to identify the optimal framework for edge-cloud computing framework to monitor water quality.