217,445 research outputs found
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which
explores the tremendous data collected by mobile smart devices with prominent
spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing
Systems, temporally recruited mobile users can provide agile, fine-grained, and
economical sensing labors, however their self-interest cannot guarantee the
quality of the sensing data, even when there is a fair return. Therefore, a
mechanism is required for the system server to recruit well-behaving users for
credible sensing, and to stimulate and reward more contributive users based on
sensing truth discovery to further increase credible reporting. In this paper,
we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile
Crowdsensing Systems, which achieves credibility-driven user recruitment and
payback maximization for honest users with quality data. Via theoretical
analysis, we demonstrate the correctness of our design. The performance of our
scheme is evaluated based on extensive realworld trace-driven simulations. Our
evaluation results show that our scheme is proven to be effective in terms of
both guaranteeing sensing accuracy and resisting potential cheating behaviors,
as demonstrated in practical scenarios, as well as those that are intentionally
harsher
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
A validation of mobile sensing actigraphy devices for generating a biomechanical model of posture
Mobile sensing actigraphy was tested and validated as a modality for computing dynamic posturography. Twelve healthy volunteer subjects (6 male) were administered risperidone and assessed for postural stability using a NeuroComÂź Balance Master system and BioSensicsÂź mobile sensors at baseline, 2 hours, 6 hours, and 24 hours post-dose. A strong positive correlation was shown between BioSensics and Balance Master systems in a modified Sensory Organization Task, with Pearsonâs r = 0.76, p < 0.001 on composite equilibrium scores. Strong to moderate correlations during the same task showed r = 0.48, p < 0.001 to r = 0.74, p < 0.001. Mobile sensing actigraphy may be a viable alternative to force plate posturography in assessing drug-induced postural instability
Recommended from our members
MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones
Mobile sensing and mapping applications are becoming more prevalent because sensing hardware is becoming more portable and more affordable. However, most of the hardware uses small numbers of fixed sensors that report and share multiple sets of environmental data which raises privacy concerns. Instead, these systems can be decentralized and managed by individuals in their public and private spaces. This paper describes a robust system called MobGeoSens which enables individuals to monitor their local environment (e.g. pollution and temperature) and their private spaces (e.g. activities and health) by using mobile phones in their day to day life
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
- âŠ