Contextual sensitivity of the ambient temperature sensor in Smartphones
04 May 2015-pp 1-8
TL;DR: This work evaluates the sensitivity and accuracy of the on-board ambient temperature sensor under various circumstances and measures its performance against standardized weather monitoring equipment, and identifies the roles of several internal and external factors that affect the temperature data.
Abstract: Environmental monitoring using external and Smartphone-interfaced wireless sensors has been widely used in the past. The roadblocks start emerging when we use on-board sensors in off-the-shelf Smartphones to estimate context aware environmental parameters like ambient temperature, humidity and atmospheric pressure. In this work, we evaluate the sensitivity and accuracy of the on-board ambient temperature sensor under various circumstances and measure its performance against standardized weather monitoring equipment. Additionally, we identify the roles of several internal and external factors that affect the temperature data. Such an investigation is motivated by the need of pervasive temperature sensing to power Smart HVAC environments and for weather crowdsourcing. Our experiments reveal that while the on-board temperature sensors have great potential, using them for large scale data collection still requires significant work.
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Zhejiang University1, Cooperative Research Centre2, University of Adelaide3, International Institute for Applied Systems Analysis4, University of Exeter5, University of Illinois at Urbana–Champaign6, Hong Kong University of Science and Technology7, Swiss Federal Institute of Aquatic Science and Technology8, Southern Methodist University9, Zhejiang University of Technology10, Delft University of Technology11
TL;DR: A review of the state of the art in this field can be found in this article, where the authors present a framework for categorizing the methods used in the seven domains of geophysics considered in this review.
Abstract: Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.
59 citations
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TL;DR: The proposed approach, aiming at overcoming the limitation of existing disease surveillance approaches, combines the hybrid crowdsensing paradigm with sensing individuals’ bio-signals using optical sensors for monitoring any risks of spreading emerging infectious diseases in any (ad-hoc) crowds.
Abstract: The risk of spreading diseases within (ad-hoc)crowds and the need to pervasively screen asymptomatic individuals to protect the population against emerging infectious diseases, request permanentcrowd surveillance., particularly in high-risk regions. Thecase of Ebola epidemic in West Africa in recent years has shown the need for pervasive screening. The trend today in diseases surveillance is consisting of epidemiological data collection about emerging infectious diseases using social media, wearable sensors systems, or mobile applications and data analysis. This approach presents various limitations. This paper proposes a novel approach for diseases monitoring and risk prevention of spreading infectious diseases. The proposed approach, aiming at overcoming the limitation of existing disease surveillance approaches, combines the hybrid crowdsensing paradigm with sensing individuals’ bio-signals using optical sensors for monitoring any risks of spreading emerging infectious diseases in any (ad-hoc) crowds. A proof-of-concept has been performed using a drone armed with a cat s60 smartphone featuring a Forward Looking Infra-Red (FLIR) camera. According to the results of the conducted experiment, the concept has the potential to improve the conventional epidemiological data collection. The measurement is reliable, and the recorded data are valid. The measurement error rates are about 8%.
12 citations
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TL;DR: Batteries-as-Thermometers is designed and implemented, a temperature sensing service based on the information of mobile device batteries, expanding the ability to sense the device's ambient temperature without requiring additional sensors or taking up the limited on-device space.
Abstract: The ability to sense ambient temperature pervasively, albeit crucial for many applications, is not yet available, causing problems such as degraded indoor thermal comfort and unexpected/premature shutoffs of mobile devices. To enable pervasive sensing of ambient temperature, we propose use of mobile device batteries as thermometers based on (i) the fact that people always carry their battery-powered smart phones, and (ii) our empirical finding that the temperature of mobile devices' batteries is highly correlated with that of their operating environment. Specifically, we design and implement Batteries-as-Thermometers (BaT), a temperature sensing service based on the information of mobile device batteries, expanding the ability to sense the device's ambient temperature without requiring additional sensors or taking up the limited on-device space. We have evaluated BaT on 6 Android smartphones using 19 laboratory experiments and 36 real-life field-tests, showing an average of 1.25°C error in sensing the ambient temperature.
6 citations
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TL;DR: This work looks at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings fromWi-Fi and barometer sensors from a collection of mobile devices.
Abstract: We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings from Wi-Fi and barometer sensors from a collection of mobile devices. Our key contribution is a novel two-step process: (i) using longitudinal, crowd-sourced readings of (possibly incorrect) Wi-Fi location estimates to statistically estimate the barometer calibration offset of individual mobile devices, and (ii) then, using such offset-corrected barometer readings from devices (that are supposedly collocated) to identify likely errors in indoor localization. We evaluate this approach using data collected from 104 devices collected on the SMU campus over a period of 61 days: our results show that (i) 49% of the devices had barometer offsets that result in errors in floor-level estimation, and (iii) 46% of the Wi-Fi location estimates were potentially incorrect. By identifying specific locations with unusually high fraction of incorrect location estimates, we attempt to more accurately pinpoint the areas that need re-fingerprinting.
5 citations
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TL;DR: In this article, the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance was investigated based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002.
Abstract: Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)–vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth’s surface that determine LST.
1,612 citations
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TL;DR: TailEnder is developed, a protocol that reduces energy consumption of common mobile applications and aggressively prefetches several times more data and improves user-specified response times while consuming less energy.
Abstract: In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology.Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For applications that can tolerate a small delay such as e-mail, TailEnder schedules transfers so as to minimize the cumulative energy consumed meeting user-specified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2x of the optimal and show that any online algorithm can at best be within a factor 1.62x of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves user-specified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications - email, news feeds, and web search - based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60% more news feed updates and download search results for more than 50% of web queries, compared to using the default policy.
1,220 citations
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1,188 citations
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TL;DR: The Oklahoma mesonet as discussed by the authors is a joint project of Oklahoma State University and the University of Oklahoma, which is used to measure air temperature, humidity, barometric pressure, wind speed and direction, rainfall, solar radiation, and soil temperatures.
Abstract: The Oklahoma mesonet is a joint project of Oklahoma State University and the University of Oklahoma. It is an automated network of 108 stations covering the state of Oklahoma. Each station measures air temperature, humidity, barometric pressure, wind speed and direction, rainfall, solar radiation, and soil temperatures. Each station transmits a data message every 15 min via a radio link to the nearest terminal of the Oklahoma Law Enforcement Telecommunications System that relays it to a central site in Norman, Oklahoma. The data message comprises three 5-min averages of most data (and one 15-min average of soil temperatures). The central site ingests the data, runs some quality assurance tests, archives the data, and disseminates it in real time to a broad community of users, primarily through a computerized bulletin board system. This manuscript provides a technical description of the Oklahoma mesonet including a complete description of the instrumentation. Sensor inaccuracy, resolution, height ...
625 citations
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TL;DR: In this article, an intelligent decision support model using rule sets based on a typical building energy management system is presented, which can control how the building operational data deviates from the settings as well as carry out diagnosis of internal conditions and optimize building's energy operation.
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