=Paper=
{{Paper
|id=Vol-2192/ialatecml_paper6
|storemode=property
|title=An Interactive Learning Scenario for Real-time Environmental State Estimation Based on Heterogeneous and Dynamic Sensor Systems
|pdfUrl=https://ceur-ws.org/Vol-2192/ialatecml_paper6.pdf
|volume=Vol-2192
|authors=Agnes Tegen,Paul Davidsson,Jan Persson
|dblpUrl=https://dblp.org/rec/conf/pkdd/TegenDP18
}}
==An Interactive Learning Scenario for Real-time Environmental State Estimation Based on Heterogeneous and Dynamic Sensor Systems==
https://ceur-ws.org/Vol-2192/ialatecml_paper6.pdf
An Interactive Learning Scenario for Real-time
Environmental State Estimation Based on
Heterogeneous and Dynamic Sensor Systems
Agnes Tegen, Paul Davidsson, and Jan A. Persson
Malmö University, School of Technology, Sweden
Internet of Things and People Research Center
{firstname.lastname}@mau.se
With the ongoing advances in the area of Internet of Things, the number of
devices with sensors streaming data in our surroundings is growing rapidly. This
will create new possibilities in continuously monitoring the state of the envi-
ronment. However, this increasingly more complex setting is also posing new
challenges, e.g. how to properly fuse data from different types of sensors with
uncertain availability.
We are focusing on a setting where the task is to do real-time continuous
estimations of certain aspects of the state of an environment. These estimations
are based on data streams from a heterogeneous and dynamic set of sensors in
that environment. Typically, data from different types of sensors needs to be
fused in order estimate the aspect. For instance, within an office setting this
could be what type of activity is currently taking place in a room or the number
of people in a certain area of a building. In previous work [1], the concept of
dynamic intelligent virtual sensors was suggested as a framework for data fusion.
Common for many scenarios of this type, is that there is no available model that
fuses the data and estimates the desired aspect of the state of the environment.
Thus, such a model needs to be learned based on the streamed data provided
by the sensors. Although the sensors may generate large amounts of data, there
is typically a lack of labeled data that can be used for supervised learning.
The interactive learning challenge described above has been identified within
an ongoing project with a number of industrial partners, partially validating its
relevance to many real world applications.
1 Application Scenario Description
In a given environment, we define the set of all sensors that generates data
as S = {s1 , s2 , . . .}. While all sensors in S produce at least one instance of
data, note that they do not necessarily have available data at all times. We
also introduce a set St ⊆ S, containing all sensors from which data is available
regarding the current point in time t. The data is generated by the sensors in a
sequential fashion. Each instance of data contains the following information: id
(unique identifier for the sensor), data (numerical or categorical measurement of
the environment), ts (timestamp, the point in time when the data was measured
according to the device).
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Interactive
2 Learning
A. Tegen et al.Scenario for Real-time Environmental State Estimation
We define a set of states, Y , representing the possible values of the aspect
of the environment that we are interested in. If the entire state set is known
beforehand, it can be defined along with the task. If not, the state set has to be
defined over time, as labeled data becomes available. The labels provided by a
user, denoted yts ∈ Y , may be used as training data and can be seen as ground
truth for the state of the environment at time ts. They can be provided by the
user’s own initiative or when queried by the learner. The labeled data are stored
for a certain time ct , which can be set based on e.g. data storage possibilities.
The problem discussed in this paper can now be stated as follows: At any
given point in time t, the task is to maximise the accuracy of the estimation of
an aspect of the current state of the environment ŷt ∈ Y , using data from St , as
well as labels and data, where (t − ct ) ≤ ts < t, as input.
2 Discussion
In active learning the learner asks an oracle to label data [3]. Compared to the
more common pool-based setting, where the learner starts with all unlabeled
data and can ask for labels in any order, our setting is stream-based. Starting
with a shortage, or non-existence, of labeled data, the algorithm must learn and
adapt over time, as labeled data gradually becomes available. At each point in
time the learner must decide whether to query or not, as it is not possible to query
for old data points. Still, how much and when to query needs to be balanced, as
there is a cost attached to it. Also, the aspect of reliability of the provided labels
has to be considered, as human feedback is typically noisy. Different humans do
not always agree on definitions or even with themselves over time.
Another complicating factor of the problem is that the entire state set might
not be known from the beginning. This means that the learner must query not
only to obtain training data, but also to learn the state space. If, for instance, the
algorithm is not able to classify a state at a given time with sufficient certainty,
it could query the user for the current state.
Generally, the different sensors do not provide data at synchronized points
in time, as some are time triggered, with non-standardized time intervals, while
others are event triggered. Furthermore, the timestamp attached to each data
point is based on the sensor’s individual clock. The learner, or a preprocessing
step, needs to accommodate for this and align the data time-wise.
Sensor intensive systems can produce large amount of data and while it might
be possible to store some data for a specified length of time, chances are all data
cannot be stored indefinite. The learner must therefor incorporate the knowledge
obtained from the data, so that it is not lost if the data is later discarded.
Transfer learning techniques is another way to handle the shortage of labeled
data. For instance, if a model is trained to classify an aspect based on data from
sensors in room A, the model could be adapted to do the corresponding task in
room B. While transfer learning has been used successfully in many applications,
the case where the input feature space for source and target (e.g. sensors in room
A and B respectively) differ, is still a relatively new field of research [2, 4].
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Interactive Learning Scenario
An Interactive for Real-time
Learning Environmental
Scenario for State Estimation 3
Real-time Environmental...
Acknowledgements This work was partially financed by the Knowledge Foun-
dation through the Internet of Things and People research profile.
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sensing using dynamic intelligent virtual sensors. In: International Symposium on
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knowledge and data engineering 22(10), 1345–1359 (2010)
3. Settles, B.: Active learning literature survey. Computer Sciences Technical Re-
port 1648, University of Wisconsin–Madison (2009)
4. Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. Journal of
Big Data 3(1), 9 (2016)
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