Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. The working and implementation of this project will be very useful to the society. The supervision and the maintenance are done through the Internet of Things. Sensors such as tilt sensors are used to identify rifts and damage to manhole lids, and the information obtained is then sent to the authorities of the municipal corporation department and the councillor of the local region, who will find the manhole location. The goal of this project is to create an effective accident-avoidance system by avoiding open manholes in large cities. These damaged manholes will be hazard to the personal safety. Because of the damaged manholes, there are chances of occurrence of accidents on the road. As most of the manhole’s lids are in the damaged condition. In observation most of the manhole’s lids were not in the settled emplacement. Also, contamination of fresh water due to problem in sewage drainage system is of concern. Nowadays manholes and its maintenance are the main problem in the metropolitan smart cities. The system proposed in this study is intended to be implemented in a rural area, where it can potentially solve the recyclable waste separation problem.Ī good manhole management is a symbol of good city. To deliver the capacity monitoring feature, the uploaded trash bin capacity information is displayed on the mobile application in the form of a bar level developed in the MIT App Inventor for the user to quickly take action if required. The capacity and GPS information are uploaded to Firebase Database via theESP8266 Wi-Fi module. Several Internet of Things hardware, such as ultrasonic sensors for measuring trash bin capacity and GPS for locating trash bin coordinates, are implemented to provide capacity monitoring controlled by Arduino Uno. The performance of the YOLO model was evaluated to measure its accuracy, which was 91% under an optimal computing environment and 75% when deployed in Raspberry Pi. The classification result rotates the trash bin lid and reveals the correct trash bin compartment for the user to throw away trash. This study describes the development of a smart trash bin that separates and collects recyclables using a webcam and You Only Look Once (YOLO) real-time object detection in Raspberry Pi, to detect and classify these recyclables into their correct categories. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach. Real-time mobile app monitors the bin’s level and location. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Delay is introduced in the order of 3–8 s while the alert message is sent to the common waste collection unit. It helps to predict the level of the smart bin. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. It also helps in identifying the type of waste material. These algorithms are useful in updating the level of the bin via alert messages. The proposed smart bin uses k-Nearest Neighbor’s algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. Nowadays, modern industries generate their energy by using renewable solar.
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