Edge assisted Reliable Landslide Early Warning System

Abstract

In many parts of the world including south-east Asia, a lot of landslides occur every year. There are a few IoT technologies exist that allow landslides' monitoring but there is a requirement of a more reliable and efficient Landslide Early Warning System (LEWS). The communication between the IoT nodes and the cloud is vulnerable to link failure due to various types of disruptions in mountainous regions. In real life scenarios, this connection loss might hinder the decision making at the cloud. So, the IoT system must not fail in any given situation. Taking reliability into consideration, this paper provides an edge computing based solution to resolve the issue. Recently, edge computing has emerged as an effective solution to decrease latency for delay sensitive IoT applications. Furthermore, it has scope to make an IoT application such as LEWS more reliable as edge server can keep on system running in case of cloud's failure or communication failure between IoT node and cloud. This paper demonstrate the implementation of reliable data processing so that even if the connection is lost between the source/coordinator node and the cloud server, the data can still be processed and feedback are obtained. During implementation, the edge server has a limited computing and storage resources, but enough to process and analyze landslide data such as rain-fall, pore pressure, moisture, and displacement, to produce meaningful results similar to the cloud. The system keeps on working even if there is a network failure between edge server and cloud server or cloud server crashes.