Green Smart Cities

Srivastava, Akanksha; Gupta, Mani Shekhar; Kaur, Gurjit

doi:10.1201/9780429454837-1

Cited by 3 publications

(4 citation statements)

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“…In particular, we resorted to the BME280 [27], an integrated environmental sensor developed by Bosch Sensortech, offering the possibility of sensing the relative humidity, the barometric pressure and the ambient temperature. This chip, with its 8-pin metal-lid 2.5 × 2.5 × 0.93 mm 3 LGA package, has mainly been developed for IoT devices, particularly wearable devices, where size and low power consumption are key design parameters. Current-wise, it is indeed perfectly feasible for low current consumption, and it has a 0.1 µA current consumption when it is in sleep-mode and 3.6 µA when it is in active-mode.…”

Section: Gateway Interface To Sensing Layer: the Esp32 Platformmentioning

confidence: 99%

“…The number of industrialized cities, which are more and more aware of eco-sustainability issues, has been constantly growing over the past few years [3]; this trend led government authorities to sponsor the adoption of eco-friendly means of transportation, such as e-bikes, electric scooters, Segways and so on. However, as the number of non-conventional means of transportation has been rising exponentially, there has been a similar rise in the number of road accidents, involving so-called vulnerable road users (VRUs) [4]; therefore, the implementation of wearable technologies is remarkably beneficial in terms of user safety by helping people connect to a much broader intelligent ecosystem often referred to as a Smart City.…”

Section: Introductionmentioning

confidence: 99%

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Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios

2022

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Many innovative services are emerging based on the Internet of Things (IoT) technology, aiming at fostering better sustainability of our cities. New solutions integrating Information and Communications Technologies (ICTs) with sustainable transport media are encouraged by several public administrations in the so-called Smart City scenario, where heterogeneous users in city roads call for safer mobility. Among several possible applications, recently, there has been a lot of attention on the so-called Vulnerable Road Users (VRUs), such as pedestrians or bikers. They can be equipped with wearable sensors that are able to communicate their data through a chain of devices towards the cloud for agile and effective control of their mobility. This work describes a complete end-to-end IoT system implemented through the integration of different complementary technologies, whose main purpose is to monitor the information related to road users generated by wearable sensors. The system has been implemented using an ESP32 micro-controller connected to the sensors and communicating through a Bluetooth Low Energy (BLE) interface with an Android device, which is assumed to always be carried by any road user. Based on this, we use it as a gateway node, acting as a real-time asynchronous publisher of a Message Queue Telemetry Transport (MQTT) protocol chain. The MQTT broker is configured on a Raspberry PI device and collects sensor data to be sent to a web-based control panel that performs data monitoring and processing. All the architecture modules have been implemented through open-source technologies. The analysis of the BLE packet exchange has been carried out by resorting to the Wireshark packet analyzer. In addition, a feasibility analysis has been carried out by showing the capability of the proposed solution to show the values gathered through the sensors on a remote dashboard. The developed system is publicly available to allow the possible integration of other modules for additional Smart City services or extension to further ICT applications.

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Section: Gateway Interface To Sensing Layer: the Esp32 Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios

2022

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“…At certain hours of the day, vehicles will move towards that area. In order to properly model this feature, we assume that α t j is the probability that the selected vehicles move towards the downtown cell when in jth cell at time t, while (1 − α t j ) is the probability that they move in the opposite direction (line 6) 4 . Based on two random values (lines 7-8), the demands for each cell are shifted following the shifting probability and the probability to move towards the destination area.…”

Section: ) the Mobility Modelmentioning

confidence: 99%

“…Modern technologies, like 5G and Internet of Things (IoT), can be employed to support the development of green cities [2]- [4]: a huge distributed cloud infrastructure made by thousands of smart devices (typically small and embedded ones), able to communicate with each other in real-time [5]. Among several application domains, the Internet of Vehicles (IoV) [6], [7], a sub-domain of IoT, can be described as a distributed and interconnected network that supports the need for sharing data created by vehicles -but could include also pedestrians, bikes, and other urban objects-in a realtime fashion with roadside units (RSUs).…”

Section: Introductionmentioning

confidence: 99%