# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 2 Related Studies  
### Edge Computing for Warning Systems  
[PERSON] et al. defined edge computing as computation and network resources placed close to the data source [20], which could provide an effective and efficient solution for data-driven low latency IoT applications. Edge systems can respond in real-time to urgent events triggered by machines or sensors. Since computation can be completed close to the data sources, real-time decisions and time sensitive applications can be solved in a timely manner. Benefits from edge computing include reduced response, improved latency, and reduced energy consumption. [PERSON] and [PERSON] proposed a warning system to detect and prevent outbreaks of mosquito borne diseases (MBDs) [8], utilizing fog-cloud based computing to process data and machine learning algorithm(s) to detect infected persons, generating early warning alerts when abnormalities were detected, i.e., high possibility of dense mosquito regions and/or breeding sites. Uninfected persons could then take immediate precautions and MBD outbreaks could be prevented. Their results confirmed that the proposed model could effectively monitor,detect, and prevent MBD outbreaks. In addition, [PERSON] et al. proposed an architecture based on edge and fog computing to solve the integration and interoperability issues in heterogenous smart building services [21]. Their experimental results showed the feasibility of the integration and interoperability between existing services and new services not only in smart building, but also in other areas.  
Edge computing has been adopted in manufacturing areas to monitoring machine health, improving assembly line productivity and increasing quantity and quality. [PERSON] et al. proposed a framework based on fog-computing for cyber manufacturing to monitor machine health and generate predictive analytics [22]. Their results demonstrated positive impacts from adopting the proposed approach for a real factory. [PERSON] et al. developed a flexible production line edge computing system for a white-appliances industry, using edge computing to process the order and communicate with other devices [23]. Their results showed that the developed model improved system productivity and decreased reconfiguration costs. [PERSON] et al. proposed an intelligent robot (iRobot) factory based on cognitive manufacturing and edge computing in the production line [24]. Edge computing provided optimal computing resources for the iRobot and reduced network transmission time. The proposed model provided significantly increased production compared with a traditional factory approach. [PERSON] et al. showed that cloud-based data storage and analytics were inappropriate for low latency and/or real time applications [25]. They recommended fog and edge computing be adopted to store and process data, significantly reducing bandwidth requirements and latency.  
Therefore, the present study utilized edge computing to monitor manufacturing processes and integrated edge computing and machine learning to provide early fault detection and warning, helping to prevent further losses due to manufacturing faults.