Smart Bins: Managing Waste the smart way. // Mentor: Dr Sajith Variyar V V (https://scholar.google.com/citations?user=4qN_eKEAAAAJ&hl=en)

Figure 1: Initial prototype of the proposed model.

8 Billion People. 8 Billion lives and 2.01 billion tonnes of solid waste are being generated worldwide. Despite the recent stunt in world population, there is a staggering increase in world waste generation. Just recently human trash was found on mars, Perhaps, mankind's dream of venturing the world and beyond is being fulfilled by waste generated by man before him. This sheds light on the need for smart waste management now more than ever before.

With more than 10000 residents and students in Amrita Vishwa Vidhyapeetham, Coimbatore campus, a resilient waste collection, and management system is imperative. The campus does an extraordinary job with more than 800 dustbins spread across its perimeter, timely collection of waste through mobile vehicles, and commercialization of waste where ever possible.

However, there is a dire need for smart collection,segregation, and commercialization of the waste. The existing process can be hugely aided by the use of smart methods like IoT and predictive analysis of waste generation.

In this light, a student team from CEN, School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore guided by Mr. Sajith Variyar V. V implemented A comprehensive solution for managing waste within the campus.

The smart waste management system includes “Clean Separated Collection at Source(CSCS)”, specialized sensors for detecting waste, and a smart dashboard for smart waste management. The collection point consists of separate bins for food, solid, plastic, and paper waste. Specialized sensors are used depending upon the nature of the waste. Bin fill levels are reported at regular intervals to an application server. A centralized dashboard is created for visualizing fill levels and managing resource persons, and a section for predictive analysis based on wastes such as expected revenue from food waste, e-waste, etc is implemented. In addition to the above-mentioned features, the system also warns through an SMS when the bin level crosses a particular threshold for appropriate personnel.

The team plans to make the system more robust by introducing more sensors for type-of-waste(ToW) detection and downstream tasks. The team aims to build a truly data-driven system for forecasting and seamless management of waste across the campus. In this account, the team will be developing various intelligent algorithms like ToW detection, revenue prediction, etc for an end-to-end smart intelligent and resilient system