Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors

© 2020 Forehead et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Air pollution with PM2.5 (particulate matter smaller than 2.5 micro-metres in diameter) is a major health hazard in many cities worldwide, but since measuring instruments have traditionally been expensive, monitoring sites are rare and generally show only background concentrations. With the advent of low-cost, wirelessly connected sensors, air quality measurements are increasingly being made in places where many people spend time and pollution is much worse: on streets near traffic. In the interests of enabling members of the public to measure the air that they breathe, we took an open-source approach to designing a device for measuring PM2.5. Parts are relatively cheap, but of good quality and can be easily found in electronics or hardware stores, or on-line. Software is open source and the free LoRaWAN-based “The Things Network” the platform. A number of low-cost sensors we tested had problems, but those selected performed well when co-located with reference-quality instruments. A network of the devices was deployed in an urban centre, yielding valuable data for an extended time. Concentrations of PM2.5 at street level were often ten times worse than at air quality stations. The devices and network offer the opportunity for measurements in locations that concern the public

A Distributed User-Centered Approach For Control in Ambient Robotic

Designing a controller to supervise an ambient application is a complex task. Any change in the system composition or end-users needs involves re-performing the whole design process. Giving to each device the ability to self-adapt to both end-users and system dynamic is then an interesting challenge. This article contributes to this challenge by proposing an approach named Extreme Sensitive Robotic where the design is not guided by finality but by the functionalities provided. One functionality is then seen as an autonomous system, which can self-adapt to what it perceives from its environment (including human activity). We present ALEX, the first system built upon the Extreme Sensitive paradigm, a multi-agent system that learns to control one functionality in interaction with its environment from demonstrations performed by an end-user. We study through an evolutive experimentation how the combination of Extreme Sensitive Robotic paradigm and ALEX eases the maintenance and evolution of ambient systems. New sensors and effectors can be dynamically integrated in the system without requiring any action on the pre-existing components

SMART Infrastructure Facility Building Data

The time series data has been generated by Droplet sensors installed in every room of the SMART Infrastructure Facility of the University of Wollongong. The sampling rate is set to one minute and the data is transmitted via a LoRaWAN network. Each device sense temperature, humidity, luminosity, pressure, movement and CO2 (*). In addition, they transmit their orientation (*), node id, room id and battery voltage. Each packet received by the LoRaWAN network is also characterized by a RSSI, an SNR and a checksum. The (*) CO2 and orientation data are not accurate

Extreme Sensitive Robotic - A Context-Aware Ubiquitous Learning

Our work focuses on Extreme Sensitive Robotic that is on multi-robot applications that are in strong interaction with humans and their integration in a highly connected world. Because human-robots interactions have to be as natural as possible, we propose an approach where robots Learn from Demonstrations, memorize contexts of learning and self-organize their parts to adapt themselves to new contexts. To deal with Extreme Sensitive Robotic, we propose to use both an Adaptive Multi-Agent System (AMAS) approach and a Context-Learning pattern in order to build a multi-agent system ALEX (Adaptive Learner by Experiments) for contextual learning from demonstrations