02179nas a2200205 4500008004100000022001400041245007400055210006900129300001700198490000600215520157700221653002401798653002001822653001601842653001501858100001801873700002001891700001501911856004701926 2013 eng d a2157-690400aLocateMe: Magnetic-fields-based Indoor Localization Using Smartphones0 aLocateMe Magneticfieldsbased Indoor Localization Using Smartphon a73:1–73:270 v43 a

Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements.

By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis.

Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.

10aIndoor localization10aMagnetic fields10asmartphones10aubiquitous1 aSubbu, Kalyan1 aGozick, Brandon1 aDantu, Ram uhttp://doi.acm.org/10.1145/2508037.250805401532nas a2200205 4500008004100000245003700041210003700078520097800115653001801093653001701111653002001128653001201148653001901160100002001179700002001199700001501219700001901234700002501253856004801278 2012 eng d00aSafe Driving Using Mobile Phones0 aSafe Driving Using Mobile Phones3 a

As vehicle manufacturers continue to increase their emphasis on safety with advanced driver-assistance systems (ADASs), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smartphones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver’s overall awareness to maximize safety.

10aAccelerometer10amobile phone10aroad conditions10asensors10avehicle safety1 aFazeen, Mohamed1 aGozick, Brandon1 aDantu, Ram1 aBhukhiya, Moiz1 aGonzález, Marta, C. uhttp://dx.doi.org/10.1109/TITS.2012.218764001811nas a2200265 4500008004100000245005300041210005300094260002700147520111600174653001301290653001401303653001401317653001001331653001101341653002201352653001701374653001801391653001801409653001201427653001601439100001801455700002001473700001501493856003701508 2011 eng d00aIndoor localization through dynamic time warping0 aIndoor localization through dynamic time warping aAnchorage, AKc10/20113 a

Identifying and locating oneself in different hallways of high rise buildings forms the classic indoor localization problem. GPS does not work indoors and WiFi may not be omnipresent. This paper presents a novel approach to ambient magnetic fields based indoor localization. We present a system that classifies magnetic signatures using dynamic time warping. Specifically, by aligning similar magnetic signatures that differ in magnitude or time, we classify the signatures and infer the location irrepective of the person and his/her mode of commuting. A Nexus One smartphone was employed, utilizing its builtin magnetic field sensor to create a user friendly localization application solely on the phone. By using a variety of subjects including sighted, blindfolded and people using wheelchairs to handle the human speed variation problem, we evaluated the system across 26 and 15 hallways of two different buildings and obtained accuracies of 92.6%, and 91.1% respectively. With these encouraging results, we believe our proposed solution is user independent and caters to a wide range of hallways.

10aAccuracy10aBuildings10aDatabases10aHuman10aHumans10aLegged locomotion10aLocalization10aMagnetometers10amobile phones10asensors10aWheelchairs1 aSubbu, Kalyan1 aGozick, Brandon1 aDantu, Ram uhttps://nsl.cse.unt.edu/node/21602078nas a2200277 4500008004100000245004000041210004000081300001600121490000700137520135000144653001401494653003101508653003201539653002001571653001801591653002001609653001801629653001501647653001201662653001601674100002001690700001801710700001501728700002001743856003701763 2011 eng d00aMagnetic Maps for Indoor Navigation0 aMagnetic Maps for Indoor Navigation a3883 - 38910 v603 a

Magnetic field fluctuations and anomalies inside buildings tend to have a great effect on the compass, which is one of the simplest navigation devices. Alternative navigation requires landmark identification, so those landmarks can be used as guideposts in assisting individuals. By employing a mobile phone with built\_in magnetometer, an extensive data set of 2000 measurements was collected. Using these fields, we identify landmarks and guideposts and create magnetic maps for multiple corridors of a floor in a building. Different phones are used at different sensitivity rates, which effectively portray similar results. Magnetic signatures are used for identifying locations and rooms and are independent of the person, the phone, and the sensitivity of the sensor being used. Magnetic field behavior is demonstrated and compared with theoretical distributions of these fields. The developed magnetic maps can complement existing visual maps for location tracking and navigation of autonomous robots indoors. These maps are particularly useful during limited visual feedback in poor lighting conditions. Moreover, building designers could include this landmark and guidepost information when developing the architecture of a building, which could in turn help people or robots navigate during disasters and emergency evacuations.

10aBuildings10aMagnetic field measurement10aMagnetic field measurements10aMagnetic fields10aMagnetometers10aMobile handsets10amobile phones10aNavigation10asensors10aUncertainty1 aGozick, Brandon1 aSubbu, Kalyan1 aDantu, Ram1 aMaeshiro, Tomyo uhttps://nsl.cse.unt.edu/node/211