114,526 research outputs found
From online experiments to smart devices
Online experiments have been available for more than a decade. The integration of online experiments into collaborative environments is more recent. The wealth of client applications/environments, the versatility of possible interaction protocols/technologies and the needs for more autonomous actions impel the evolution of online experiments to the smart device concept. This paper reviews the evolution of an electrical drive experiment and presents the requirements for turning online experiments into smart devices
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions
StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge
Today, massive amounts of streaming data from smart devices need to be
analyzed automatically to realize the Internet of Things. The Complex Event
Processing (CEP) paradigm promises low-latency pattern detection on event
streams. However, CEP systems need to be extended with Machine Learning (ML)
capabilities such as online training and inference in order to be able to
detect fuzzy patterns (e.g., outliers) and to improve pattern recognition
accuracy during runtime using incremental model training. In this paper, we
propose a distributed CEP system denoted as StreamLearner for ML-enabled
complex event detection. The proposed programming model and data-parallel
system architecture enable a wide range of real-world applications and allow
for dynamically scaling up and out system resources for low-latency,
high-throughput event processing. We show that the DEBS Grand Challenge 2017
case study (i.e., anomaly detection in smart factories) integrates seamlessly
into the StreamLearner API. Our experiments verify scalability and high event
throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner:
Distributed Incremental Machine Learning on Event Streams: Grand Challenge.
In Proceedings of the 11th ACM International Conference on Distributed and
Event-based Systems (DEBS '17), 298-30
Battery Health Estimation for IoT Devices using V-Edge Dynamics
Deployments of battery-powered IoT devices have become ubiquitous, monitoring everything from environmental conditions in smart cities to wildlife movements in remote areas. How to manage the life-cycle of sensors in such large-scale deployments is currently an open issue. Indeed, most deployments let sensors operate until they fail and fix or replace the sensors post-hoc. In this paper, we contribute by developing a new approach for facilitating the life-cycle management of large-scale sensor deployments through online estimation of battery health. Our approach relies on so-called V-edge dynamics which capture and characterize instantaneous voltage drops. Experiments carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating battery health with up to 80% accuracy, depending on the characteristics of the devices and the processing load they undergo. Our method is particularly well-suited for the sensor devices, operating dedicated tasks, that have constant discharge during their operation.Peer reviewe
Shrewd Selection Speeds Surfing: Use Smart EXP3!
In this paper, we explore the use of multi-armed bandit online learning
techniques to solve distributed resource selection problems. As an example, we
focus on the problem of network selection. Mobile devices often have several
wireless networks at their disposal. While choosing the right network is vital
for good performance, a decentralized solution remains a challenge. The
impressive theoretical properties of multi-armed bandit algorithms, like EXP3,
suggest that it should work well for this type of problem. Yet, its real-word
performance lags far behind. The main reasons are the hidden cost of switching
networks and its slow rate of convergence. We propose Smart EXP3, a novel
bandit-style algorithm that (a) retains the good theoretical properties of
EXP3, (b) bounds the number of switches, and (c) yields significantly better
performance in practice. We evaluate Smart EXP3 using simulations, controlled
experiments, and real-world experiments. Results show that it stabilizes at the
optimal state, achieves fairness among devices and gracefully deals with
transient behaviors. In real world experiments, it can achieve 18% faster
download over alternate strategies. We conclude that multi-armed bandit
algorithms can play an important role in distributed resource selection
problems, when practical concerns, such as switching costs and convergence
time, are addressed.Comment: Full pape
Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing
In this paper, a first approach to the design of a portable device for non-contact monitoring
of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart
vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive
Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation
exercises at home. To provide an extensible solution to the remote monitoring using this sensor and
other devices, the design and preliminary development of an e-Health platform based on the Internet
of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution,
two quasi-experimental studies have been developed, comparing the estimations with respect to the
golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error,
the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm),
0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values
were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period
show the technical and functional feasibility of the prototype and serve as a preliminary validation of
the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de AndalucÃa PI-0010-2013Junta de AndalucÃa PI-0041-2014Junta de AndalucÃa PIN-0394-201
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