Pervasive Mobile Environmental Sensor Grids

道路交通对当地空气质量的影响是一项重大的公共政策关切，并促使大量研究致力于改善基本的车辆和交通管理技术，并为公共政策行动提供信息。最近的工作应该使用各种基于车辆、基于人员和基于基础设施的传感器系统来收集关于驾驶员和交通行为、排放、污染物浓度和暴露的关键方面的数据。由于现有技术(例如，基于GPS的车辆跟踪、车载发动机管理系统数据的CAN Bus接口)在车辆维修中心内的日益广泛的渗透，以及新技术(例如，紫外线传感和基于纳米技术的微型传感器)的引入，未来可输入的传感器的种类和普及性将显著增加。未来特别令人振奋的发展方向将是将车辆用作面向外的环境传感器系统的平台，允许车辆作为移动环境探测器运行，从而从根本上提高环境污染物和有害物质的检测和监测能力。然而，这些发展带来了新的和艰巨的研究挑战，需要传输、整合、模拟和解释大量高度多样化(空间和时间变化)的传感器数据。我们在这个项目中的方法是通过将最先进的eScience、传感器、定位和建模(数据融合、交通、运输、排放、分散)技术进行新颖的组合和扩展来应对这些挑战。通过这样做，我们的目标是开发使用无处不在的路边和车载传感器来测量、模拟和预测各种环境污染物和危害(包括与交通相关的和其他方面的)的能力。这项工作将处于eScience的前沿，在处理大量传感器数据的许多方面延伸网格的能力。

The impact of road traffic on local air quality is a major public policy concern and has stimulated a substantial body of researchaimed at improving underlying vehicle and traffic management technologies and informing public policy action. Recent work hassought to use a variety of vehicle-based, person-based and infrastructure-based sensor systems to collect data on key aspects ofdriver and traffic behaviour, emissions, pollutant concentrations and exposure. The variety and pervasiveness of the sensor inputsavailable will increase significantly in the future as a result both of the increasingly widespread penetration of existingtechnologies (e.g., GPS based vehicle tracking, CANbus interfaces to on-board engine management system data) within thevehicle parc and the introduction of new technologies (such as e.g., UV sensing and nanotechnology based micro sensors). Aparticularly exciting direction for future development will be in the use of vehicles as platforms for outward facing environmentalsensor systems, allowing vehicles to operate as mobile environmental probes, providing radically improved capability for thedetection and monitoring of environmental pollutants and hazardous materials.However, these developments present new and formidable research challenges arising from the need to transmit,integrate, model and interpret vast quantities of highly diverse (spatially and temporally varying) sensor data. Our approach in thisproject is to address these challenges by novel combination and extension of state-of-the-art eScience, sensor, positioning andmodelling (data fusion, traffic, transport, emissions, dispersion) technologies. By so doing, we aim to develop the capability tomeasure, model and predict a wide range of environmental pollutants and hazards (both transport related and otherwise) using agrid of pervasive roadside and vehicle-mounted sensors. This work will be at the leading edge of eScience, stretching thecapabilities of the grid in a number of aspects of the processing of massive volumes of sensor data.