Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> E. Environmental <s> PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> E. Environmental <s> A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87% when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization. <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> E. Environmental <s> In this paper we present a new approach to monitor noise pollution involving citizens and built upon the notions of participatory sensing and citizen science. We enable citizens to measure their personal exposure to noise in their everyday environment by using GPS-equipped mobile phones as noise sensors. The geo-localised measures and user-generated meta-data can be automatically sent and shared online with the public to contribute to the collective noise mapping of cities. Our prototype, called Noise Tube, can be found online. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> E. Environmental <s> This paper develops a navigation service, called GreenGPS, that uses participatory sensing data to map fuel consumption on city streets, allowing drivers to find the most fuel efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular fuel consumption sensors, available via the OBD-II interface standardized in all vehicles sold in the US since 1996. The interface gives access to most gauges and engine instrumentation. The most fuel-efficient route does not always coincide with the shortest or fastest routes, and may be a function of vehicle type. Our experimental study shows that a participatory sensing system can influence routing decisions of individual users and also answers two questions related to the viability of the new service. First, can it survive conditions of sparse deployment? Second, how much fuel can it save? A challenge in participatory sensing is to generalize from sparse sampling of high-dimensional spaces to produce compact descriptions of complex phenomena. We illustrate this by developing models that can predict fuel consumption of a set of sixteen different cars on the streets of the city of Urbana-Champaign. We provide experimental results from data collection suggesting that a 1% average prediction error is attainable and that an average 10% savings in fuel can be achieved by choosing the right route. <s> BIB004 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> E. Environmental <s> Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly-changing operating environments with little access to situational information for first responders. In-situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed, but are less practical due to need for large infrastructure, lack of adaptivity and limited coverage. Controlled-mobility in sensor networks, i.e. the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations. In this paper, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve 3-D sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy. <s> BIB005 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> E. Environmental <s> This paper describes a novel deployment of an intelligent user-centered HVAC (Heating, Ventilating and Air Conditioner) control system. The main objective of this system is to optimize user comfort and to reduce energy consumption in office buildings. Existing commercial HVAC control systems work in a fixed and predetermined way. The novelty of the proposed system is that it adapts dynamically to the user and to the building environment. For this purpose the system architecture has been designed under the paradigm of Ambient Intelligence. A prototype of the system proposed has been tested in a real-world environment. <s> BIB006
Environmental monitoring, on one hand, aims at sensing and collecting information about surrounding environment by basically providing personalized environmental scorecards at the human level; on the other hand, it creates an impact toward environmental exposure by contributing environmental solutions at the community level. The surrounding environment is either a small scale area (e.g., indoor) or a large one (e.g., outdoor). For indoor environments, applications to monitor HVAC systems and building maintenance can be considered BIB005 , BIB006 . For instance, one can use a smartphone to measure room temperature, and then smartphone can adjust heater or ventilator automatically to change air balance in a smart home environment. Moreover, it would be more reasonable to apply environmental monitoring in the context of community sensing. The studies in BIB001 - BIB004 provides applications for environmental monitoring to track and notify hazard exposures such as carbon emission level, air pollution, waste accumulation, water intoxication level, etc. In addition, noise pollution and ambience fingerprinting (fusion of sound, light and color) are other topics that have been studied in this content BIB002 , BIB003 .