# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 2 Background and Related Work
### Edge Computing in IoT Applications  
In the modern world and modern wireless telecommunication, the Internet of things (IoT) has gained significant traction with billions of devices connected to the internet. The number of IoT devices is estimated to reach 14.5 billion by the end of 2022, with the market size forecasted to grow to USD 525 billion from 2022 to 2027 [35]. One of the biggest strengths of the IoT is the massive impact it has on the everyday life of users. It does not come as a surprise that the IoT has been included in the list of six \"Disruptive Civil Technologies\" with a potential influence on US national power. Some of the potential application domains of the IoT are transportation and logistics, healthcare and smart environments [36]. Within the IoT, there are several technologies involved such as radiofrequency identification (RFID), near-field communication (NFC), intelligent sensing, wireless sensor networks (WSNs) and cloud computing. The IoT essentially defines the upcoming generation of the Internet where physical objects will be accessed via the Internet. As current research stands, the use of RFID-based verification and identification in the logistics and retail industries has been rampant. Furthermore, the success of the IoT is dependent on standardisation, which will ultimately provide reliability, compatibility, interoperability and effectiveness at a global level. IoT devices can be implemented on a much larger scale in numerous industries and areas, where billions of things can be integrated seamlessly, and for that, objects in the IoT need to exchange information and be able to communicate autonomously [37]. The number of layers in an IoT architecture is defined differently depending on the underlying technologies. However, from the recent literature, the basic model of the IoT is a five-layer architecture consisting of an object layer, object abstraction layer, service management layer, application layer and a business layer. The five-layer architecture is an ideal model since it encapsulates scalability and interoperability, both of which are important in IoT applications [38]. Prior to edge computing, data produced by IoT sensors were processed on the cloud. Cloud computing has been extremely cost-efficient and flexible in terms of computing power and storage capabilities. However, with the advancement in the IoT and the rapid development in mobile internet, cloud computing has been unable to meet the sophisticated demands of extremely low latency due to the additional load on the backhaul networks. The network bandwidth requirement to send all the data to the cloud generated by IoT applications is prohibitively high. To tackle the shortcomings, an edge computing paradigm has been thoroughly investigated, which processes the generated data at the network edge instead of transmitting them to centralised cloud servers. Edge computing is a much more suitable solution to be integrated with the IoT for a large user base and can be considered for future IoT infrastructure [39]. The edge computing architecture relies on the serversbeing closer to the end user or node and even though they possess less computational power than cloud servers, the latency and quality of service (QoS) provided are better. The edge-computing-based IoT satisfies the QoS requirements of time-sensitive applications and solves the bottleneck issues of network resources faced in the IoT [7]. [PERSON] and [PERSON] [40] discussed an open edge cloud model infrastructure which is necessary moving forward in an IoT world. An open edge cloud addresses local computing, networking resources and storage to aid resource-constrained IoT devices. Data generated by edge devices can be stored and preprocessed locally by the edge cloud. For loads or data that are beyond the capabilities of the IoT devices, the devices can offload tasks to the edge servers. This will enable latency control since the edge cloud is nearer to the nodes. [PERSON] et al.