Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

A review of urban air pollution monitoring and exposure assessment methods

The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data

Evaluating the performance of eMTC and NB-IoT for smart city applications

Low power wide area network (LPWAN) is a wireless telecommunication network that is designed for interconnecting devices with low bitrate focusing on long range and power efficiency. In this paper, we study two recent technologies built from existing Long-Term Evolution (LTE) functionalities: Enhanced machine type communications (eMTC) and Narrow band internet of things (NB-IoT). These technologies are designed to coexist with existing LTE infrastructure, spectrum, and devices. We compare the performance of both systems in terms of energy consumption, latency, and scalability. We introduce a model for calculating the energy consumption and study the effect of clock drift and propose a method to overcome it. We also propose a model for analytically evaluating the latency and the maximum number of devices in a network. Furthermore, we implement the main functionalities of both technologies and simulate the end-to-end latency and the maximum number of devices in a discrete-event network simulator NS-3. Numerical results show that 8 years battery life time can be achieved by both technologies in a poor coverage scenario and that depending on the coverage conditions and data length, one technology consumes less energy than the other. The results also show that eMTC can serve more devices in a network than NB-IoT, while providing a latency that is 10 times lower

A high-accuracy phase-based ranging solution with Bluetooth Low Energy (BLE)

Nowadays, indoor positioning and asset tracking have become popular and essential for many applications and use-cases. As Bluetooth Low Energy (BLE) is widely deployed in smart tags, smart-phones, and smart-devices, adding functionality to support localization and asset tracking is crucial. In order to locate a device, either the angle or range to the device needs to be calculated. In this paper, we focus on a phase-based solution to calculate the range between devices. We introduce a multi-carrier phase-based ranging solution compatible with the BLE standard that utilizes BLE channel hopping to exchange tones in the entire 2.4 GHz frequency band to mitigate the multi-path fading problem. We recognize that in the BLE standard, slow channel-hopping, long packet size and frame spacing between two consecutive communications (named TIFS) affect ranging error/accuracy, in the case of crystal offset and phase-noise. Therefore, we introduce a new mathematical model to analyze the impact of the BLE link layer protocol on the ranging error. Finally, we evaluate the accuracy of our proposed solution through modelling, by considering the results of real experiments and by validating the correctness of our mathematical model for the ranging error

Evaluating the performance of eMTC and NB-IoT for smart city applications

\u3cp\u3eLow power wide area network (LPWAN) is a wireless telecommunication network that is designed for interconnecting devices with low bitrate focusing on long range and power efficiency. In this paper, we study two recent technologies built from existing Long-Term Evolution (LTE) functionalities: Enhanced machine type communications (eMTC) and Narrow band internet of things (NB-IoT). These technologies are designed to coexist with existing LTE infrastructure, spectrum, and devices. We compare the performance of both systems in terms of energy consumption, latency, and scalability. We introduce a model for calculating the energy consumption and study the effect of clock drift and propose a method to overcome it. We also propose a model for analytically evaluating the latency and the maximum number of devices in a network. Furthermore, we implement the main functionalities of both technologies and simulate the end-to-end latency and the maximum number of devices in a discrete-event network simulator NS-3. Numerical results show that 8 years battery life time can be achieved by both technologies in a poor coverage scenario and that depending on the coverage conditions and data length, one technology consumes less energy than the other. The results also show that eMTC can serve more devices in a network than NB-IoT, while providing a latency that is 10 times lower.\u3c/p\u3