# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 3 Related Work
### IoT Applications Based on Edge-Cloud Framework  
One of the most often mentioned use cases and applications of edge computing is smart cities. [PERSON] et al. [37] described how the service-oriented middleware (SOM) approach might benefit from resolving the issues associated with designing and managing smart city services utilising cloud and fog computing. They proposed SmartCityWare as a SOM for the complete coordination and usage of fog and cloud computing. The proposed framework isolates components and services used in smart city applications and makes them available as services via the service-oriented approach. This improves integration and enables the flexible incorporation and use of the many services required in a smart city application. Another study by [PERSON] et al. [38] provided a multi-tier fog computing system for extensive data analytics in smart cities. Because of the inherent geo-distribution of massive data collected by enormous sensors, they disseminate smart at the edge of a tiered fog computing system. To maintain the safety of essential infrastructure components, computing nodes at each tier conduct latency-sensitive applications and offer rapid controller loops. Using smart pipeline monitoring as an example, a four-layer fog-based computing paradigm is presented to showcase the usefulness and feasibility of the system's potential city-wide implementation. [PERSON] et al. [39] created a three-layer conceptual framework, dividing the city into zones for dispersed management. The fog and cloud layers offer potential computing abilities and resources for message processing without adding network expenses. According to queueing theory, model stationary and moving vehicle-based fog nodes predict the average reply time depending on their processing abilities. They conclude that moving fog nodes of vehicle-based may be seen as servers with queue theory M/M/1. To show the efficiency of the proposed framework, a performance assessment based on a real-world taxi path dataset is presented. In terms of average system response time and processing resources required, the proposed framework outperforms the competition. [PERSON] et al. [40] proposed multi-tier fog for smart city applications with a large-scale data analysis service. The multi-layer fog is made up of devoted fogs and ad-hoc fogs, each having adaptable and dedicated computing resources. The developed novel fog computing framework, which includes distinct functional modules, can reduce the potential issues of a specialised computing platform and delayed reply in cloud computing. The results of experiments show that the analytical services are efficient across multitier fogs, and the proposed QoS systems are effective. In terms of job blocking likelihood and service utilities, fogs may significantly increase smart city analytical services than cloud models. Other applications have used the edge-cloud framework for data analysis, [PERSON] and [PERSON] (2017) proposed a new real-time intelligent transportation system (ITS) significant data analysis framework on the Internet of Vehicles (IoV) environment. The architecture presented fuses three dimensions: intelligent computing real-time extensive data analysis, intelligent computing, and IoV dimension, i.e., fog and cloud computing. This study also provides an exhaustive overview of the IoV setup lambda real-time large data analysis architecture, ITS big data characteristics, and various smart computing technologies. [PERSON] et al. (2018) proposed an energy-efficient system using an edge-IoT framework and a social computing architecture inside a public building scenario. The system is assessed in a public building, and the findings show the remarkable advantages that edge computing brings to energy-saving circumstances and the framework. These advantages encompassed lowering the IoT-Edge data transmission into the cloud and lowering cloud, computer and networking expenses.