59,163 research outputs found
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Supporting ethnographic studies of ubiquitous computing in the wild
Ethnography has become a staple feature of IT research over the last twenty years, shaping our understanding of the social character of computing systems and informing their design in a wide variety of settings. The emergence of ubiquitous computing raises new challenges for ethnography however, distributing interaction across a burgeoning array of small, mobile devices and online environments which exploit invisible sensing systems. Understanding interaction requires ethnographers to reconcile interactions that are, for example, distributed across devices on the street with online interactions in order to assemble coherent understandings of the social character and purchase of ubiquitous computing systems. We draw upon four recent studies to show how ethnographers are replaying system recordings of interaction alongside existing resources such as video recordings to do this and identify key challenges that need to be met to support ethnographic study of ubiquitous computing in the wild
Sensing as a service: A cloud computing system for mobile phone sensing
Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains such as environmental monitoring, social networking, healthcare, etc. We introduce a new concept, Sensing-as-a-Service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud should meet the following requirements:
1) It must be able to support various mobile phone sensing applications on different smartphone platforms.
2) It must be energy-efficient. 3) It must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions
Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain
[EN] Mobile crowdsensing (MCS) is a technique where people with computing and sensing devices such as smartphones collectively share data that are of potential interest to the rest of society. MCS includes two different trends (i) mobile sensing, which shares raw data generated from the sensors that are embedded in mobile devices, and (ii) social sensing, which uses the information shared by people in online social networks (OSNs). In this study, the authors present the timeline evolution of the COVIDÂż19 pandemic in Spain, and summarise the MCS research efforts that are being undertaken by the Spanish community to address COVIDÂż19 outbreak. Indeed, the COVIDÂż19 pandemic is putting today's society at risk; lockdown and social distancing measures proposed by governments are dramatically affecting economies. In this regard, MCS tools can become a powerful solution to provide smart quarantine strategies in periods of a steep decrease of infections, or new outbreaks.This work was partially supported by the FundaciĂłn SĂ©neca del
Centro de CoordinaciĂłn de la InvestigaciĂłn de la RegiĂłn de Murcia
under Project 20813/PI/18, and by the Spanish Ministry of Science,
Innovation and Universities under grants RTI2018-096384-B-I00
and RTC-2017-6389-5.Cecilia-Canales, JM.; Cano, J.; HernĂĄndez-Orallo, E.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2020). Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain. IET Smart Cities. 2(2):1-6. https://doi.org/10.1049/iet-smc.2020.0037S1622World Health Organization:âNovel coronavirus (2019âncov): Situation report 91â [accessed 30âAprilâ2020]Instituto de Salud.Carlos.III:âSituaciĂłn de covidâ19 en españaâ [accessed 30âAprilâ2020].https://covid19.isciii.es/LiR.RiversC.TanQ.et al.: âThe demand for inpatient and ICU beds for COVIDâ19 in the US: lessons from Chinese citiesâ medRxiv 2020 pp.1â12 doi:10.1101/2020.03.09.20033241World Health Organization:âCritical preparedness readiness and response actions for COVIDâ19: interim guidance 22 March 2020âKissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020). Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science, 368(6493), 860-868. doi:10.1126/science.abb5793International Labour Organization: âThe socioeconomic impact of COVIDâ19 in fragile settings: peace and social cohesion at riskâ https://www.ilo.org/global/topics/employmentâpromotion/recoveryâandâreconstruction/WCMS_741158/langen/index.htm [accessed 30âAprilâ2020]Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015). Mobile Crowd Sensing and Computing. ACM Computing Surveys, 48(1), 1-31. doi:10.1145/2794400AdolphC.AmanoK.Bang JensenB.et al.: âPandemic politics: timing stateâlevel social distancing responses to COVIDâ19â medRxiv 202
Crowdsensing the Speaker Count in the Wild: Implications and Applications
Abstract-The Mobile Crowd Sensing (MCS) paradigm enables large-scale sensing opportunities at lower deployment costs than dedicated infrastructures by utilizing the large number of today's mobile devices. In the context of MCS, end users with sensing and computing devices can share and extract information of common interest. In this article, we examine Crowd++, a MCS application, which accurately estimates the number of people talking in a certain place through unsupervised machine learning analysis on audio segments captured by mobile devices. Such a technique can find application in many domains, such as crowd estimation, social sensing, and personal well-being assessment. In this article, we demonstrate the utility of this technique in the context of conference room usage estimation, social diary, and social engagement in a power efficient manner followed by a discussion on privacy and possible optimizations to Crowd++ software
Advanced Location-Based Technologies and Services
Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements
- âŠ