We present the design, implementation, and evaluation of AirCloud -- a novel client-cloud system for pervasive and personal air-quality monitoring at low cost. At the frontend, we create two types of Internet-connected particulate matter (PM2:5) monitors -- AQM and miniAQM, with carefully designed mechanical structures for optimal air-flow. On the cloud-side, we create an air-quality analytics engine that learn and create models of air-quality based on a fusion of sensor data. This engine is used to calibrate AQMs and mini-AQMs in real-time, and infer PM2:5 concentrations. We evaluate AirCloud using 5 months of data and 2 month of continuous deployment, and show that AirCloud is able to achieve good accuracies at much lower cost than previous solutions. We also show three real applications built on top of AirCloud by 3rd party developers to further demonstrate the value of our system.

We present the design, implementation, and preliminary results of PAM - a low-cost and portable personal air quality monitor that provides real-time air quality measurements for the user's immediate environment. PAM consists of a PAM client and a cloud-side PAM service. The PAM client utilizes two inexpensive dust sensors to obtain raw particulate concentration measurement, and two Arduino boards for local filtering, web services, and communication with PAM service. The cloud-side PAM service constructs a statistical model of air quality and provides PAM clients with context-dependent calibration curves. Together, PAM is able to approximate PM2.5, PM10, and AQI measurement for users in-situ and at low cost.

When designing radial basis function neural networks, the central task is to set parameters of radial basis functions. In this paper, subtractive clustering is improved by particle swarm optimization (PSO) to automatically select the number and locations of radial basis functions. Subtractive clustering is used to find center prototypes and then PSO Flues their locations iteratively. Comparative experiments were executed between subtractive clustering and PSO-based subtractive clustering proposed in this paper for designing RBF neural networks on several datasets. The experimental results suggest that the PSO-based subtractive clustering algorithm can be successfully applied to design RBF neural networks with competitive classification accuracy and small number of radial basis functions. The RBF neural networks evolved by PSO-based subtractive clustering have stronger generalization ability than the ones evolved by subtractive clustering.