1,664 research outputs found
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Mobile phones as medical devices in mental disorder treatment: an overview
Mental disorders can have a significant, negative impact on sufferersâ lives, as well as on their friends and family, healthcare systems and other parts of society. Approximately 25 % of all people in Europe and the USA experience a mental disorder at least once in their lifetime. Currently, monitoring mental disorders relies on subjective clinical self-reporting rating scales, which were developed more than 50 years ago. In this paper, we discuss how mobile phones can support the treatment of mental disorders by (1) implementing humanâcomputer interfaces to support therapy and (2) collecting relevant data from patientsâ daily lives to monitor the current state and development of their mental disorders. Concerning the first point, we review various systems that utilize mobile phones for the treatment of mental disorders. We also evaluate how their core design features and dimensions can be applied in other, similar systems. Concerning the second point, we highlight the feasibility of using mobile phones to collect comprehensive data including voice data, motion and location information. Data mining methods are also reviewed and discussed. Based on the presented studies, we summarize advantages and drawbacks of the most promising mobile phone technologies for detecting mood disorders like depression or bipolar disorder. Finally, we discuss practical implementation details, legal issues and business models for the introduction of mobile phones as medical devices
Hardware for recognition of human activities: a review of smart home and AAL related technologies
Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AALâsmartphones, wearables, video, and electronic componentsâand two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard
Inferring transportation mode from smartphone sensors:Evaluating the potential of Wi-Fi and Bluetooth
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Concerns about data privacy are omnipresent, given the increasing usage of
digital applications and their underlying business model that includes selling
user data. Location data is particularly sensitive since they allow us to infer
activity patterns and interests of users, e.g., by categorizing visited
locations based on nearby points of interest (POI). On top of that, machine
learning methods provide new powerful tools to interpret big data. In light of
these considerations, we raise the following question: What is the actual risk
that realistic, machine learning based privacy attacks can obtain meaningful
semantic information from raw location data, subject to inaccuracies in the
data? In response, we present a systematic analysis of two attack scenarios,
namely location categorization and user profiling. Experiments on the
Foursquare dataset and tracking data demonstrate the potential for abuse of
high-quality spatial information, leading to a significant privacy loss even
with location inaccuracy of up to 200m. With location obfuscation of more than
1 km, spatial information hardly adds any value, but a high privacy risk solely
from temporal information remains. The availability of public context data such
as POIs plays a key role in inference based on spatial information. Our
findings point out the risks of ever-growing databases of tracking data and
spatial context data, which policymakers should consider for privacy
regulations, and which could guide individuals in their personal location
protection measures
Semantic Trajectories: Mobility Data Computation and Annotation
With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called ''trajectories''. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the {\it semantic'' behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this paper lies in a ''Semantic Model'' and a ''Computation and Annotation Platform'' for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with annotations and segmentations. We also analyze a number of experiments we did with semantic trajectories in different domains
Semantic trajectories: Mobility data computation and annotation
With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains
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