7,408 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
Parkinson's disease is a neurodegenerative disease that can affect a person's
movement, speech, dexterity, and cognition. Clinicians primarily diagnose
Parkinson's disease by performing a clinical assessment of symptoms. However,
misdiagnoses are common. One factor that contributes to misdiagnoses is that
the symptoms of Parkinson's disease may not be prominent at the time the
clinical assessment is performed. Here, we present a machine-learning approach
towards distinguishing between people with and without Parkinson's disease
using long-term data from smartphone-based walking, voice, tapping and memory
tests. We demonstrate that our attentive deep-learning models achieve
significant improvements in predictive performance over strong baselines (area
under the receiver operating characteristic curve = 0.85) in data from a cohort
of 1853 participants. We also show that our models identify meaningful features
in the input data. Our results confirm that smartphone data collected over
extended periods of time could in the future potentially be used as a digital
biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201
5GNOW: Challenging the LTE Design Paradigms of Orthogonality and Synchronicity
LTE and LTE-Advanced have been optimized to deliver high bandwidth pipes to
wireless users. The transport mechanisms have been tailored to maximize single
cell performance by enforcing strict synchronism and orthogonality within a
single cell and within a single contiguous frequency band. Various emerging
trends reveal major shortcomings of those design criteria: 1) The fraction of
machine-type-communications (MTC) is growing fast. Transmissions of this kind
are suffering from the bulky procedures necessary to ensure strict synchronism.
2) Collaborative schemes have been introduced to boost capacity and coverage
(CoMP), and wireless networks are becoming more and more heterogeneous
following the non-uniform distribution of users. Tremendous efforts must be
spent to collect the gains and to manage such systems under the premise of
strict synchronism and orthogonality. 3) The advent of the Digital Agenda and
the introduction of carrier aggregation are forcing the transmission systems to
deal with fragmented spectrum. 5GNOW is an European research project supported
by the European Commission within FP7 ICT Call 8. It will question the design
targets of LTE and LTE-Advanced having these shortcomings in mind and the
obedience to strict synchronism and orthogonality will be challenged. It will
develop new PHY and MAC layer concepts being better suited to meet the upcoming
needs with respect to service variety and heterogeneous transmission setups.
Wireless transmission networks following the outcomes of 5GNOW will be better
suited to meet the manifoldness of services, device classes and transmission
setups present in envisioned future scenarios like smart cities. The
integration of systems relying heavily on MTC into the communication network
will be eased. The per-user experience will be more uniform and satisfying. To
ensure this 5GNOW will contribute to upcoming 5G standardization.Comment: Submitted to Workshop on Mobile and Wireless Communication Systems
for 2020 and beyond (at IEEE VTC 2013, Spring
Smartphone sensing platform for emergency management
The increasingly sophisticated sensors supported by modern smartphones open
up novel research opportunities, such as mobile phone sensing. One of the most
challenging of these research areas is context-aware and activity recognition.
The SmartRescue project takes advantage of smartphone sensing, processing and
communication capabilities to monitor hazards and track people in a disaster.
The goal is to help crisis managers and members of the public in early hazard
detection, prediction, and in devising risk-minimizing evacuation plans when
disaster strikes. In this paper we suggest a novel smartphone-based
communication framework. It uses specific machine learning techniques that
intelligently process sensor readings into useful information for the crisis
responders. Core to the framework is a content-based publish-subscribe
mechanism that allows flexible sharing of sensor data and computation results.
We also evaluate a preliminary implementation of the platform, involving a
smartphone app that reads and shares mobile phone sensor data for activity
recognition.Comment: 11th International Conference on Information Systems for Crisis
Response and Management ISCRAM2014 (2014
CommuniSense: Crowdsourcing Road Hazards in Nairobi
Nairobi is one of the fastest growing metropolitan cities and a major
business and technology powerhouse in Africa. However, Nairobi currently lacks
monitoring technologies to obtain reliable data on traffic and road
infrastructure conditions. In this paper, we investigate the use of mobile
crowdsourcing as means to gather and document Nairobi's road quality
information. We first present the key findings of a city-wide road quality
survey about the perception of existing road quality conditions in Nairobi.
Based on the survey's findings, we then developed a mobile crowdsourcing
application, called CommuniSense, to collect road quality data. The application
serves as a tool for users to locate, describe, and photograph road hazards. We
tested our application through a two-week field study amongst 30 participants
to document various forms of road hazards from different areas in Nairobi. To
verify the authenticity of user-contributed reports from our field study, we
proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to
verify whether submitted reports indeed depict road hazards. We found 92% of
user-submitted reports to match the MTurkers judgements. While our prototype
was designed and tested on a specific city, our methodology is applicable to
other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer
Interaction with Mobile Devices and Services (MobileHCI 2015
Locational wireless and social media-based surveillance
The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date
360 Quantified Self
Wearable devices with a wide range of sensors have contributed to the rise of
the Quantified Self movement, where individuals log everything ranging from the
number of steps they have taken, to their heart rate, to their sleeping
patterns. Sensors do not, however, typically sense the social and ambient
environment of the users, such as general life style attributes or information
about their social network. This means that the users themselves, and the
medical practitioners, privy to the wearable sensor data, only have a narrow
view of the individual, limited mainly to certain aspects of their physical
condition.
In this paper we describe a number of use cases for how social media can be
used to complement the check-up data and those from sensors to gain a more
holistic view on individuals' health, a perspective we call the 360 Quantified
Self. Health-related information can be obtained from sources as diverse as
food photo sharing, location check-ins, or profile pictures. Additionally,
information from a person's ego network can shed light on the social dimension
of wellbeing which is widely acknowledged to be of utmost importance, even
though they are currently rarely used for medical diagnosis. We articulate a
long-term vision describing the desirable list of technical advances and
variety of data to achieve an integrated system encompassing Electronic Health
Records (EHR), data from wearable devices, alongside information derived from
social media data.Comment: QCRI Technical Repor
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