Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification.

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An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model

A smart municipal waste management system based on deep-learning and Internet of Things.

A fault-tolerant distributed decision fusion in the presence of sensor faults via Collaborative Sensor Fault Detection (CSFD) was used traditionally. CSFD scheme is proposed in which the results of a homogeneity test are used to identify the faulty nodes within the network such that their quantized messages can be filtered out when estimating the parameter of interest. The scheme can identify the faulty nodes efficiently and improve the performance of the decision fusion significantly. It achieves very good performance at the expense of such extensive computations as exponent and multiplication/division in the detecting process. In many real-time WSN applications, the fusion center might be implemented in an ASIC and included in a stand-alone device. Therefore, a simple and efficient decision fusion scheme requiring lower hardware cost and power consumption is extremely desired. In this paper, we propose the Approximated Collaborative Sensor Fault Detection (ACSFD) scheme and its VLSI architecture. Sorting operation are required in ACSFD to find out four biggest faulty node indexes for subsequent usage for this purpose .we have implemented different sorting algorithm to evaluate the efficiency of the sorting network

Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Network

Detecting glass and metal in consumer trash bags during waste collection using convolutional neural networks.

Domestic waste classification was incorporated into legal provisions recently in China. However, relying on manpower to detect and classify domestic waste is highly inefficient. To that end, in this article, we propose a multimodel cascaded convolutional neural network (MCCNN) for domestic waste image detection and classification. MCCNN combined three subnetworks (DSSD, YOLOv4, and Faster-RCNN) to obtain the detections. Moreover, to suppress the false-positive predicts, we utilized a classification model cascaded with the detection part to judge whether the detection results are correct. To train and evaluate MCCNN, we designed a large-scale waste image dataset (LSWID), containing 30 000 domestic waste multilabeled images with 52 categories. To the best of our knowledge, the LSWID is the largest dataset on domestic waste images. Furthermore, a smart trash can is designed and applied to a Shanghai community, which helped to make waste recycling more efficient. Experimental results showed a state-of-the-art performance, with an average improvement of 10% in detection precision.

Automatic Detection and Classification System of Domestic Waste via Multimodel Cascaded Convolutional Neural Network

Waste management is a pervasive problem in today’s world and is rising continuously with rise in urbanization. For ecologically sustainable development, waste management is a vital requirement in many countries. It is very essential to sort the waste at base level so that there can be proper disposal of waste at the dumping sites. Sorting of waste requires more manpower and consumes more time too. Waste can be sorted and managed in numerous types of techniques. Analysing and classifying the garbage using image processing can be a very productive way to process waste materials. These papers talk about the traditional methods in which waste disposals are taking place. These also talk about the drawbacks faced by the already existing systems and ways to overcome it.

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P Mahalakshmi

Papers

Survey onidentification and classification of waste for efficient disposal and recycling