Under Water Waste Cleaning by Mobile Edge Computing and Intelligent Image Processing Based Robotic Fish

TL;DR

This paper presents a robotic fish equipped with sensors and intelligent image processing, leveraging mobile edge computing and 5G networks to efficiently detect and clean underwater waste, reducing manual effort and environmental impact.

Contribution

It introduces an integrated robotic fish system utilizing edge computing and AI for real-time underwater waste detection and removal, enhancing efficiency over traditional methods.

Findings

Reduced time delay in waste detection and removal

Enhanced waste collection capacity of the robotic fish

Effective use of mobile edge computing and AI for underwater cleaning

Abstract

As water pollution is a serious threat to underwater resources, i.e., underwater plants and species, we focus on protecting the resources by cleaning the non-biodegradable waste from the water. The waste can be recycled for further usage. Here we design a robotic fish which mainly comprises optical biosensor, camera module, piston module, and wireless transceiver. By exploiting the LTE and 5G network architecture, the fish stores the information about the underwater waste in the nearest mobile edge computing server as well as in the centralized cloud server. Finally, when the fish clears the underwater waste, it offloads the captured image of the located object to the mobile edge computing server or sometimes to the cloud server for making a decision. The servers employ intelligent image processing technology and an adaptive learning process to make a decision. However, if the servers fail to make a decision, then the fish utilizes its optical biosensor. By this scheme, the time delay for clearing any water body is minimized and the waste collection capacity of the fish is maximized. This technique can effectively help the government or municipal personnel for making clean water without manual efforts.