This vision paper presents the case for MUSIC, a programmable framework for building distributed mobile IoT applications for urban sensing. The Mobile Urban Sensing, Inference and Control (MUSIC) framework is contextualized for scenarios where a distributed collection of static or mobile sensors collectively achieve an urban sensing task. The MUSIC platform is designed for urban-centric sensing applications such as location sensing on mobile phones for road traffic monitoring, air quality sensing and urban quality monitoring using remote cameras. This programmable system, at a high level, consists of several small sensors placed throughout a city on mobile vehicles and a centralized controller that makes decisions on sensing in order to achieve certain well-defined objectives such as improving spatial coverage of sensing and detection of hotspots. The system is programmable in that our framework allows one to create custom smart systems by writing custom control logic for sensing. Our contributions are two-fold -- a backend software stack to enable centralized control of distributed devices and programmability, and algorithms for intelligent control in the presence of practical power and network constraints. We briefly present three different urban sensing applications built on top of the MUSIC stack.
 Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.  We introduce Mosaic, a mobile sensor network system for city scale sensing. Currently, we focus on accurate PM2.5 measurements. We design and implement low-cost smart sensors for this purpose. Those sensors are specifically designed for moving vehicles such as buses and taxies. The mobile sensing capability of our system enables a wide coverage of a city with a modest number of sensors. We incorporate a three-layer architecture in Mosaic. Mosaic faces many practical challenges which need to be properly addressed, e.g., reliable node hardware and software design, and accurate PM2.5 measurements. We have built three versions of Mosaic nodes. We present a novel data calibration approach combining traditional Artificial Neural Network (ANN) and Support Vector Machines (SVMs). We launch real-world deployments of Mosaic in two main cities in China -- Hangzhou and Ningbo. Our initial efforts show that Mosaic is a feasible approach for city scale sensing and our system can produce highly accurate PM2.5 measurements. Monitoring street-level particulates is essential to air quality management but challenging in high-density Hong Kong due to limitations in local monitoring network and the complexities of street environment. By employing vehicle-based mobile measurements, land use regression (LUR) models were developed to estimate the spatial variation of PM2.5 and PM10 in the downtown area of Hong Kong. Sampling runs were conducted along routes measuring a total of 30 km during a selected measurement period of total 14 days. In total, 321 independent variables were examined to develop LUR models by using stepwise regression with PM2.5 and PM10 as dependent variables. Approximately, 10 increases in the model adjusted R2 were achieved by integrating urban building morphology as independent variables into the LUR models. Resultant LUR models show that the most decisive factors on street-level air quality in Hong Kong are frontal area index, an urban building morphological parameter, and road network line density and traff... Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action. Few studies have quantified relationships between bicyclist exposure to air pollution and roadway and traffic variables. As a result, transportation professionals are unable to easily estimate exposure differences among bicycle routes for network planning, design, and analysis. This paper estimates the effects of roadway and travel characteristics on bicyclist exposure concentrations, controlling for meteorology and background conditions. Concentrations of volatile organic compounds (VOC) and carbon monoxide (CO) are modeled using high-resolution data collected on-road. Results indicate that average daily traffic (ADT) provides a parsimonious way to characterize the impact of roadway characteristics on bicyclists’ exposure. VOC and CO exposure increase by approximately 2 per 1000 ADT, robust to different regression model specifications. Exposure on off-street facilities is higher than at a park, but lower than on-street riding – with the exception of a path through an industrial corridor with significantly higher exposure. VOC exposure is 20 higher near intersections. Traffic, roadway, and travel variables have more explanatory power in the VOC models than the CO model. The quantifications in this paper enable calculation of expected exposure differences among travel paths for planning and routing applications. The findings also have policy and design implications to reduce bicyclists’ exposure. Separation between bicyclists and motor vehicle traffic is a necessary but not sufficient condition to reduce exposure concentrations; off-street paths are not always low-exposure facilities.    