Giacomo Peruzzi

Bio: Giacomo Peruzzi is an academic researcher from University of Siena. The author has contributed to research in topics: Computer science & LPWAN. The author has an hindex of 6, co-authored 23 publications receiving 181 citations.

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

Abstract: 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.

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

Abstract: 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.

A Review of Energy Harvesting Techniques for Low Power Wide Area Networks (LPWANs)

Abstract: The emergence of Internet of Things (IoT) architectures and applications has been the driver for a rapid growth in wireless technologies for the Machine-to-Machine domain. In this context, a crucial role is being played by the so-called Low Power Wide Area Networks (LPWANs), a bunch of transmission technologies developed to satisfy three main system requirements: low cost, wide transmission range, and low power consumption. This last requirement is especially crucial as IoT infrastructures should operate for long periods on limited quantities of energy: to cope with this limitation, energy harvesting is being applied every day more frequently, and several different techniques are being tested for LPWAN systems. The aim of this survey paper is to provide a detailed overview of the the existing LPWAN systems relying on energy harvesting for their powering. In this context, the different LPWAN technologies and protocols will be discussed and, for each technology, the applied energy harvesting techniques will be described as well as the architecture of the power management units when present.

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

Abstract: 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 Underground to Aboveground Data Transmission Performances for Different Soil Compositions

Abstract: The aim of this article is to discuss the usability of the long-range (LoRa) transmission technology together with the LoRa wide-area network (LoRaWAN) protocol for underground monitoring activities. In particular, this article focuses on the transmission performances in different soils (i.e., gravel, sand, and clay), for an underground-to-aboveground (UG2AG) communication. The three soils have been chosen in order to test the system behavior in case of pure soil compositions, in order to provide a general result that can be used to evaluate the transmission chances for any kind of soil. The performances of the transmission channel have been tested using an experimental setup for depths up to 50 cm, acquiring the values of the received signal strength indicator (RSSI) and the signal-to-noise ratio (SNR) for every transmission and analyzing the packet loss (PL). Such a kind of system may be crucial in several application scenarios, such as environmental monitoring or smart agriculture, where the real-time, remote acquisition of underground parameters at different depths is required.

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

Abstract: 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.

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

Abstract: 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.