44,025 research outputs found
CERN Storage Systems for Large-Scale Wireless
The project aims at evaluating the use of CERN computing infrastructure for next generation sensor networks data analysis. The proposed system allows the simulation of a large-scale sensor array for traffic analysis, streaming data to CERN storage systems in an efficient way. The data are made available for offline and quasi-online analysis, enabling both long term planning and fast reaction on the environment
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
With the advent of the Internet of Things (IoT), an increasing number of
energy harvesting methods are being used to supplement or supplant battery
based sensors. Energy harvesting sensors need to be configured according to the
application, hardware, and environmental conditions to maximize their
usefulness. As of today, the configuration of sensors is either manual or
heuristics based, requiring valuable domain expertise. Reinforcement learning
(RL) is a promising approach to automate configuration and efficiently scale
IoT deployments, but it is not yet adopted in practice. We propose solutions to
bridge this gap: reduce the training phase of RL so that nodes are operational
within a short time after deployment and reduce the computational requirements
to scale to large deployments. We focus on configuration of the sampling rate
of indoor solar panel based energy harvesting sensors. We created a simulator
based on 3 months of data collected from 5 sensor nodes subject to different
lighting conditions. Our simulation results show that RL can effectively learn
energy availability patterns and configure the sampling rate of the sensor
nodes to maximize the sensing data while ensuring that energy storage is not
depleted. The nodes can be operational within the first day by using our
methods. We show that it is possible to reduce the number of RL policies by
using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
In-Network Distributed Solar Current Prediction
Long-term sensor network deployments demand careful power management. While
managing power requires understanding the amount of energy harvestable from the
local environment, current solar prediction methods rely only on recent local
history, which makes them susceptible to high variability. In this paper, we
present a model and algorithms for distributed solar current prediction, based
on multiple linear regression to predict future solar current based on local,
in-situ climatic and solar measurements. These algorithms leverage spatial
information from neighbors and adapt to the changing local conditions not
captured by global climatic information. We implement these algorithms on our
Fleck platform and run a 7-week-long experiment validating our work. In
analyzing our results from this experiment, we determined that computing our
model requires an increased energy expenditure of 4.5mJ over simpler models (on
the order of 10^{-7}% of the harvested energy) to gain a prediction improvement
of 39.7%.Comment: 28 pages, accepted at TOSN and awaiting publicatio
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