Efficient Edge Server Computing on a Scaled IoT Based Sensory Network

Edge server-based computing is one the most effective way of approaching and dealing with the problem of a wide or scaled sensory network where we have an excessive amount of data to deal with. The approach to implementing such a system used here offers not just a singular set of sensor networks but allows users to control, add and/or maintain the entire daisy chain of the sensory network on a local scale controlled by IoT cloud access and local computing potential using Raspberry Pi as the main edge computing resource. In this method, the ESP32 sensor node will do a small computation on the sensed data. It can be read in any form either analog or digital. After computation, it will send these data packets by publishing them to the locally connected edge node created by using Edge X Foundry service, management tools, and Mosquitto as the main broker. This edge node gathers the data from all the sensors and then publishes them accordingly to the cloud server based on subscriptions configured by the cloud host. The main objective of this project is to achieve a high level of control that allows users to manipulate a local system that is capable of locally administering and governing the data and release them upon requests with minimal possible power draw, having a potential of scalability as well as portability as compared to the solutions currently available and used. This method has the potential to be applied on any kind of application ranging from consumer, industrial or infrastructural levels.