As the population in cities continues to increase rapidly, air pollution becomes a serious issue from public health to social economy. Among all pollutants, fine particulate matters (PM2.5) directly related to various serious health concerns, e.g., lung cancer, premature death, asthma, and cardiovascular and respiratory diseases. To enhance the quality of urban living, sensors are deployed to create smart cities. In this paper, we present a participatory urban sensing framework for PM2.5 monitoring with more than 2500 devices deployed in Taiwan and 29 other countries. It is one of the largest deployment project for PM2.5 monitor in the world as we know until May 2017. The key feature of the framework is its open system architecture, which is based on the principles of open hardware, open source software, and open data. To facilitate the deployment of the framework, we investigate the accuracy issue of low-cost particle sensors with a comprehensive set of comparison evaluations to identify the most reliable sensor. By working closely with government authorities, industry partners, and maker communities, we can construct an effective eco-system for participatory urban sensing of PM2.5 particles. Based on our deployment achievements to date, we provide a number of data services to improve environmental awareness, trigger on-demand responses, and assist future government policymaking. The proposed framework is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities, and citizen science in the future.

As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an Anomaly Detection Framework (ADF) for large-scale, real-world environmental sensing systems. The framework is comprised of four modules: 1) Time-Sliced Anomaly Detection (TSAD), which detects Spatial, Temporal, and Spatio-temporal anomalies in the real-time sensor measurement data stream; 2) Real-time Emission Detection (RED), which detects potential regional emission sources; 3) Device Ranking (DR), which provides a ranking for each sensing device; and 4) Malfunction Detection (MD), which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.

We propose PRoPHET+, a routing scheme for opportunistic networks designed to maximize successful data delivery rate and minimize transmission delay. PRoPHET+ computes a deliverability value to determine the routing path for packets. Deliverability is calculated using a weighted function consisting of evaluations of nodes' buffer size, power, location, popularity, and the predictability value from PRoPHET. Even though the proposed PRoPHET+'s weights are chosen based on qualitative considerations, it is possible for PRoPHET+ to perform even more efficiently in various environments by shifting the weights accordingly. Our simulation illustrates that PRoPHET+ can perform better or equal to the routing protocol PRoPHET if logical choices for weights are used.

Information and communication technologies have been widely used to achieve the objective of smart city development. A smart air quality sensing and forecasting system is an important part of a smart city. One of the major challenges in designing such a forecast system is ensuring high accuracy and an acceptable computation time. In this paper, we show that it is possible to accurately forecast fine particulate matter (PM2.5) concentrations with low computation time by using different clustering techniques. An Internet of Things framework comprising of Airbox devices for PM2.5 monitoring has been used to acquire the data. Our main focus is to achieve high forecasting accuracy with reduced computation time. We use a hybrid model to do the forecast and a grid based system to cluster the monitoring stations based on the geographical distance. The experiments and evaluation is done using Airbox devices data from 557 stations deployed all over Taiwan. We are able to demonstrate that a proper clustering based on geographical distance can reduce the forecasting error rate and also the computation time. Also, in order to further evaluate our system, we have applied wavelet-based clustering to group the monitoring stations. A final comparative analysis is done for different clustering schemes with respect to accuracy and computational time.

Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model’s performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 $\mathsf{\mu }$ g/ ${\mathrm{m}}^3$ and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 $\mathsf{\mu }$ g/ ${\mathrm{m}}^3$ which is significantly low.