516 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
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Digital maps have become a part of our daily life with a number of commercial
and free map services. These services have still a huge potential for
enhancement with rich semantic information to support a large class of mapping
applications. In this paper, we present Map++, a system that leverages standard
cell-phone sensors in a crowdsensing approach to automatically enrich digital
maps with different road semantics like tunnels, bumps, bridges, footbridges,
crosswalks, road capacity, among others. Our analysis shows that cell-phones
sensors with humans in vehicles or walking get affected by the different road
features, which can be mined to extend the features of both free and commercial
mapping services. We present the design and implementation of Map++ and
evaluate it in a large city. Our evaluation shows that we can detect the
different semantics accurately with at most 3% false positive rate and 6% false
negative rate for both vehicle and pedestrian-based features. Moreover, we show
that Map++ has a small energy footprint on the cell-phones, highlighting its
promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on
Sensing, Communication, and Networking (IEEE SECON 2014
Online change detection for energy-efficient mobilec crowdsensing
Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts
Evaluating Sensor Data in the Context of Mobile Crowdsensing
With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach
Large-scale assessment of mobile crowdsensed data: a case study
Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as PPE). The present work performs statistical modelling of PPE to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on PPE . Several road properties affecting PPE as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of PPE is evaluated, incorporating previous results on the relationship between vehicle speed and PPE . These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
The recent proliferation of increasingly capable mobile devices has given
rise to mobile crowd sensing (MCS) systems that outsource the collection of
sensory data to a crowd of participating workers that carry various mobile
devices. Aware of the paramount importance of effectively incentivizing
participation in such systems, the research community has proposed a wide
variety of incentive mechanisms. However, different from most of these existing
mechanisms which assume the existence of only one data requester, we consider
MCS systems with multiple data requesters, which are actually more common in
practice. Specifically, our incentive mechanism is based on double auction, and
is able to stimulate the participation of both data requesters and workers. In
real practice, the incentive mechanism is typically not an isolated module, but
interacts with the data aggregation mechanism that aggregates workers' data.
For this reason, we propose CENTURION, a novel integrated framework for
multi-requester MCS systems, consisting of the aforementioned incentive and
data aggregation mechanism. CENTURION's incentive mechanism satisfies
truthfulness, individual rationality, computational efficiency, as well as
guaranteeing non-negative social welfare, and its data aggregation mechanism
generates highly accurate aggregated results. The desirable properties of
CENTURION are validated through both theoretical analysis and extensive
simulations
Mobile crowdsensing for road sustainability: exploitability of publicly-sourced data
ABSTRACTThis paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the parti..
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