Barriers to smart waste management for a circular economy in China

Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters

Intelligent decision-making of online shopping behavior based on internet of things

This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented.

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A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context.

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

In this paper the authors discuss the realization of a Long Range Wide Area Network (LoRaWAN) network infrastructure to be employed for monitoring activities within the marine environment. In particular, transmission ranges as well as the assessment of parameters like Signal to Noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) are analyzed in the specific context of an aquaculture industrial plant, setting up a transmission channel from an offshore monitoring structure provided with a LoRaWAN transmitter, to an ashore receiving device composed of two LoRaWAN Gateways. A theoretical analysis about the feasibility of the transmission is provided. The performances of the system are then measured with different network parameters (in particular the Spreading Factor—SF) as well as with two different heights for the transmitting antenna. Test results prove that efficient data transmission can be achieved at a distance of 8.33 km even using worst case network settings: this suggests the effectiveness of the system even in harsher environmental conditions, thus entailing a lower quality of the transmission channel, or for larger transmission ranges.

Low Power Wide Area Networks (LPWAN) at Sea: Performance Analysis of Offshore Data Transmission by Means of LoRaWAN Connectivity for Marine Monitoring Applications.

This paper focuses on the comparison of transmission performances within critical environments (i.e., underwater, within metal enclosures and underground) for two of the most adopted enabling technologies for the Internet of Things (IoT): the Long Range Wide Area Network (LoRaWAN) protocol and the Narrowband Internet of Things (NB-IoT) standard. After a literature review, the two technologies are surveyed and compared. Then, the most exploited path loss models for the aforesaid application scenarios are presented. In order to assess and compare performances, a multi-protocol wireless sensor node prototype exploiting both the technologies for transmitting is described. Such prototype is then employed within field tests which consisted of underwater, through-metal and underground broadcasts with the aim of measuring losses only ascribed to the media. Eventually, tests results are analyzed and compared with the theoretical models.

LoRaWAN vs NB-IoT: Transmission Performance Analysis within Critical Environments

In this work an IoT framework for the monitoring of chemical emissions in industrial plants is presented. The proposed system can embed different sensors. In particular, each sensor node manages a humidity sensor and an array of temperature and electrochemical gas sensors for the detection of CO, NO x and O 2 . Moreover it exploits some dedicated processing algorithms to compensate the dependence of the sensor response on temperature. The sensor node is provided with LoRa LPWAN connectivity that allows Wide Area data transmission: tests carried out in urban areas proved that a 3km communication range is achievable in noisy environments. A network architecture and a data acquisition and management structure is then presented for an application scenario in a Smart Industry context. A multilayer, modular topology is presented, combining the features of LoRa technology with shorter and larger range telecommunication channels, to develop an IoT framework that can be customized according to the physical and technical features of the deployment environment.

An IoT Framework for the Pervasive Monitoring of Chemical Emissions in Industrial Plants

This paper discusses the use of an integrated ultrasonic transducer for the realization of a LoRa-based Smart Bin architecture, to be employed for waste management in the Smart Cities context. In particular, the paper analyses the usability of waterproof ultrasonic sensors for the measurement of waste level inside a trash bin: in this context, a solution is described to increase the directionality of the sensor. Indeed, ultrasonic sensors used in common off-the-shelf waterproof ranging systems are characterized by a wide radiation lobe, corresponding to an aperture angle up to 70°: this means that, when employed in trash bins, these sensors detect the presence of the lateral surface of the bin and are not able to detect the garbage layer top or its position in the bin. In this paper we propose a tailored cap, designed to be mounted on a commercial ultrasonic sensor, able to reduce the detection angle down to around 28° degrees. The modified sensor can thus be exploited in this application. The proposed sensing structure is integrated into a sensor node provided with a LoRa transmission module, that allows to employ it for the development of city-scale monitoring infrastructures.

Stefano Parrino

Papers

A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context.

Low Power Wide Area Networks (LPWAN) at Sea: Performance Analysis of Offshore Data Transmission by Means of LoRaWAN Connectivity for Marine Monitoring Applications.

LoRaWAN vs NB-IoT: Transmission Performance Analysis within Critical Environments

An IoT Framework for the Pervasive Monitoring of Chemical Emissions in Industrial Plants

A LoRa-based IoT Sensor Node for Waste Management Based on a Customized Ultrasonic Transceiver