Semantic Multi-sensor Data Processing for Smart Environments
Fano Ramparany
Orange Labs, 28 chemin du Vieux Chene, 38243 Meylan, France
Semantic Web, Internet of Things, Smart Home, Smart Sensors.
One salient feature of data produced by the IoT is its heterogeneity. Despite this heterogeneity, future IoT
applications including Smart Home, Smart City, Smart Energy services, will require that all data be easily
compared, correlated and merged, and that interpretation of this resulting aggregate into higher level context
better matches people needs and requirements. In this paper we propose a framework based on semantic
technologies for aggregating IoT data. Our approach has been assessed in the domain of the Smart Home
with real data provided by Orange Homelive solution. We show that our approach enables simple reasoning
mechanisms to be conducted on the aggregated data, so that contexts such as the presence, activities of people
as well as abnormal situations requiring corrective actions, be inferred.
An IDC study (idc, 2015) predicts that the number
of connected objects is approaching 200 billion today
with 7% (14 billion) already connected to the internet.
Most of these objects automatically record, report and
receive data. Although the volume of these IoT data
currently represents only 2% of the world’s data, the
same study report that by 2020 it will increase up to
This data has been characterized by IBM data sci-
entists along four dimensions: volume, variety, veloc-
ity and veracity.
In this paper we mainly address the Variety issue,
which further refer to incompatible data formats, non-
aligned data structures and inconsistent data seman-
IoT data is heterogeneous both semantically (the
temperature in my bedroom doesn’t have much to
do with the positioning of my fridge in the kitchen)
and syntactically (a temperature is a floating point
number expressed in celsius degrees, whereas a posi-
tion is a coordinates pair expressed in meters with re-
spect to some defined reference origin). Despite this
heteogenity, future IoT applications including Smart
Home, Smart City, Smart Energy services, will re-
quire that all data be easily compared, correlated and
merged and that interpretation of the resulting aggre-
gate into higher level context better matches people
needs and requirements, bringing user experience at
the next level. In this paper we propose a framework
based on semantic technologies to aggregate IoT data.
Our approach has been assessed in the domain of
the Smart Home with real data provided by Orange
Homelive solution. We show that our approach en-
ables simple reasoning mechanisms to be conducted
on the aggregated data, so that contexts such as the
presence, activities ofpeople as well as abnormal situ-
ations requiring corrective actions, can be recognized
and properly handled.
In the next section we state the problems and draw
the related state of the art. We then introduce the ex-
perimental platform that we used, to develop and as-
sess our solution approach. This will enable us to il-
lustrate the technical and scientific challenges that we
face with a real Smart Home setting. We then de-
velop our semantic modeling approach and elaborate
on the benefit of this approach in terms of reasoning
and high level interpration that this model allows. We
finally discuss our approach with its short terms per-
spectives and will unveil a first repertoire of use-cases
exploiting our approach that will improve the experi-
ence of Smart Home occupants.
In a previous study we already pointed out this issue
and named it “aggregation of heterogeneous pieces
of information (Ramparany et al., 2014). We men-
tioned this recommendation use case: Suppose that
we could gather in the same model, the weather fore-
Ramparany, F.
Semantic Multi-sensor Data Processing for Smart Environments.
DOI: 10.5220/0005729601990206
In Proceedings of the 5th International Confererence on Sensor Networks (SENSORNETS 2016), pages 199-206
ISBN: 978-989-758-169-4
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
cast for the next 4 hours, a person geographical loca-
tion, her/his preferences/profile/activities and the list
of public swimming pools in the area. Having these
multiple information in a single place makes it easy
to reason upon and for instance to produce person-
nalized recommendation such as enjoying a dip in the
nearest swimming pool if the person is available and
enclined to do so.
Some work has been conducted in analyzing the
benefit of semantic modeling in the domain of per-
vasive computing ((Ramparany et al., 2007), (Sorici
et al., 2015), (Ye et al., 2015)), but to our knowledge
none have pushed to the point of implementing and
evaluating it on real life data.
The value of semantic technologies has been rec-
ognized for sometimes now for integrating database
schema, data modeling and processing.
2.1 Semantic Data Integration
Data integration research has been focused in
database schema integration approaches and the
use of ontologies and related semantic technologies
to provide data consistency among heterogeneous
database schemas. The theoretical foundations of
this Ontology-Based Data Access (OBDA) (Lenz-
erini, 2011) have been thoroughly investigated. Pro-
totypical implementations have been also conducted
such as Quest (Rodriguez-Muro et al., 2012) or MAS-
TRO (Calvanese et al., 2011). As a matter of fact,
internally, ontologies will be based on DL-Lite logic
which essentially captures standard conceptual mod-
elling formalisms, such as UML Class Diagrams and
Entity-Relationship Schemas, and are at the basis of
OWL 2, the current W3C standard language for on-
tologies (W3C, ).
The Web since its origins has been a vehicle of
data interchange. However, automatic discovery and
integration of Web data has been impractical until
the availability of the RDF framework and RDF data
sources. The flagship initiative on this area, Linked-
Data (Berners-Lee, 2006) has fostered both the size
of the structured Web data and its exploitation (Bizer
et al., 2009). One of the pillars of this idea is the
possibility of retrieving specific data in the web of
data; this task is performed by SPARQL (Hartig et al.,
2009), a SQL-like language that enables querying a
RDF store. Also, the Web currently explores other
approaches based on embedded JSON information or
microformats, using the tag facilities for HTML. In
particular, a specific syntax for using JSON called
JSON-LD has ben recently introduced to serialize
LinkedData with the motivation to reduce the size
of RDF documents compared to the size yielded by
XML serialization.
2.2 Semantic Data Modeling
One major benefit of expressing data representation
with semantic langage relates to its ability to provide
high level and expressive abstractions. For instance,
in the IoT, data abstraction is concerned with the ways
that the physical world is perceived and managed.
In this domain, a Semantic Sensor Network ontol-
ogy (Compton et al., 2012) has been developped and
proposed at the W3C for standardization.
This vision of introducing abstraction based on a
semantic approach, i.e. on ontologies shared by the
IoT community is being pushed forward within sev-
eral Standard Defining Organisations such as ETSI
M2M and OneM2M. One motivation of semantic
abstraction resides in interacting with higher level
entities rather than with sensors and actuators and
thus making it possible to understand data without
prior knowledge about their sources (device, web ser-
2.3 Semantic Data Processing
Semantic web technologies allow logical reasoning so
that new information or knowledge can be inferred
from existing assertions and rules. IoT applications
will require reasoning for various purposes such as
resource discovery, data abstraction and knowledge
extraction. To this purpose, specific algorithms are
usually implemented within dedicated reasoners (e.g.
Pellet, FACT++ and Jena) so developers do not need
to be concerned with the complexities of the reason-
ing process itself. Examples of IoT resource discov-
ery in the linked data can be found in (Pschorr et al.,
We aim at applying this approach to integrating
IoT Data and to experiment this approach in a real
operational setting.
As we have set high the ambition of assessing our ap-
proach in today’s home, we have based our experi-
mental platform on an off the shelf home automation
solution called Homelive (hom, ). Homelive allows
people to manage their home appliances remotely.
The Homelive pack offers a range of intelligent sen-
sors and connected devices, broughtby Orange’s part-
ners: weather monitors, thermostats, light switches,
sound and movement detectors, water leak and smoke
detectors, to name a few. We have thus instrumented
SENSORNETS 2016 - 5th International Conference on Sensor Networks
a space in our building with Homelive connected de-
vices. It is worth noting that this space was already
used by people for lunch around noon, coffee breaks
in the morning, tea breaks in the afternoon, and for
short breaks throughout the day during which people
could engage in informal discussions or simply get
some rest. Deploying Homelive in this space didn’t
have any impact on the way it was used. More specif-
ically, we have installed 5 move detectors (entrance,
kitchen, living and dining areas), 4 door state sen-
sors (main entrance, fridge, freezer, medecine cabi-
net, drawer below the sink), 5 luminosity sensors (in-
tegrated in the move detectors), 5 thermometers (in-
tegrated in the move detectors) and 5 smart plugs
(fridge, 2 coffee machines, boiler, TV set)
Each of these device is associated to a physical ob-
ject to which it has been attached or into which it has
been placed. For example, each electrical appliance
including the fridge, the two coffee machine, a boiler
and the TV set has been plugged into a smartplug,
which itself is plugged onto the wall power socket.
The number of some devices seem overdone, such as
thermometers or the luminosity sensors, but actually,
these sensors are integrated into the move detector de-
vice. Such devices are thus qualified as 3-in-1 which
simply means that these physical device embed 3 dif-
ferent sensors.
Each device is assigned a name which makes ex-
plicit its type. Thus smart plugs have been named
The picture displayed in Fig. 1 details where each
device has been placed.
Figure 1: Devices deployment.
All these devices are connected through the wire-
less communication technology Z-Wave (zwa, ). A
Home Automation Box (HAB) is a dedicated gateway
which makes it possible to access these devices from
the IP world as depicted in Fig. 2, and make them
part of the HAN (Home Area Network). In order to
further extend their reachability from the HAN to the
WAN (Wide Area Network), this HAB has to be con-
nected to another gateway, such as the white box at
the bottom of the figure. In our case it was an Orange
Figure 2: Experimental setup.
In our experimental setup, the HAB collects all
devices events and forwards them to a local server
which will handle the aggregation and interpretation
Such device events are formatted in json, follow-
ing a fixed “key-value” schema. An example of such
an event emitted by smartplug
is as shown
in Fig. 3
From this comprehensiveevent description, which
consists of 10 key-value pairs, we will mainly keep
the following four:
is the date the event was received
by the HAB. It is expressed as the number of sec-
onds elapsed since jan. 1
, 1970 at 1:00AM.
is the name of the device. As mentioned earlier
we made is so that the type of the sensor could be
identified from its name. For instance, we know
from the name
that the event has been
emitted by a smartplug.
is the physical parameter that the
event is about. In the event sample above, this pa-
rameter is the current electrical power consumed
by the appliance it supplies.
{ "deviceId": "22",
"deviceType": "BinaryLight",
"homelive": "47122383",
"id": "3",
"name": "MLPlug1",
"room": "A118",
"service": "EnergyMetering1",
"timestamp": "1428595051",
"variable": "Watts",
"value": "45" }
Figure 3: Event Description in JSON.
Semantic Multi-sensor Data Processing for Smart Environments
is the current value of this physical parame-
As you can notice, the above definitions of the keys
are necessary for the reader to understand what the
values associated to these keys mean, although the
name of the keys have been chosen in a way that the
reader would have figured out these definitions eas-
ily by himself. For an information processing system
to correctly interpret an event, the meaning should be
made explicit and even be embedded in the represen-
tation. In the section 4 we explain how we make this
possible. But before that, in the following section we
elaborate on why, remaining at the basic event de-
scription is too low a level to expect any interesting
interpretation of the information it conveys.
IoT sensors are usually very talkative and versatile.
For instance, in our Homelive platform, the smart-
plugs regularly delivers dense streams of power mea-
surements that amount to up to 10 measures per min-
utes, although these smartplugs have been config-
ured so that a new measurement is issued only if the
current power consummed differs from the previous
measure sent, by more than 10Watts. More generally
Homelive devices send an event whenever a signifi-
cant change in the data it measures occurs.
Such an abundance of information is superfluous
to the inhabitants as well as to most smart home appli-
cations. These cumbersome data could be synthesized
by applying one or more of the following policies:
compute a mean value over a time slice of say
compute a general trend from which strong in-
creases and decreases could be easily detected
or check compliancy to predetermined thresholds.
such abstraction of raw data will uplift the level of
information and will place it closer to the home occu-
The main idea is that we want to bridge the gap
between low level raw data and high level informa-
tion, so that the step that remains to be done to make
a decision or to engage an action will be straightfor-
One positive side effect not to be underrated is that
through this abstraction process, we reduce the size of
the information and thus reduce the traffic. The gain
in traffic size is particularly high if the this abstrac-
tion process is carried out close to the source of the
information, i.e. close to the sensor. Having this pro-
cess handled by the HAB or at least in the HAN is
technically a reasonable solution.
In order for a computer system to be able to pro-
cess this low level data, it is necessary to reformat this
data into a representation that incorporates the seman-
tics of the data as well as the data itself. Applied to
the data produced by our homelive devices, this will
result into a semantic model the SmartHome data. In
the next section we explain this reformating process.
We first introduce the architecture of our system
so that we get an overall perspective on where the raw
data comes from, where the target semantic model
will be stored and how it will be further exploited for
high level interpretation and reasoning. This architec-
ture is depicted on Fig. 4
Figure 4: System architecture.
Input low level data is provided in a
push/asynchronous mode by the Homelive HAB,
that we have represented as a rectangular box on
the lower left part of the diagram. As explained in
section 3, this data consists of a flow of independent
events emitted by each Homelive device. The HAB
acts as a pass-through proxy which collects events
from each device and forwards them right away to
the local server, which has subscribed to receive such
events as mentioned before. Events are described in
json as shown in Fig. 3
Because we use the semantic web framework and
its associated modeling languages RDF/OWL, our
first task is to interpret the data conveyed in the event
description in terms of elements of these languages.
An event is a piece of information that is produced
by an IoT device. Thus we create a concept represent-
ing this piece of information and one representing this
device. As this event is possibly not the first one pro-
duced by this device, the concept representing this de-
vice might already exists. In which case, we don’t cre-
ate it but will refer to the existing one instead, as will
be shown later. Each key-value pair in this description
SENSORNETS 2016 - 5th International Conference on Sensor Networks
has to be properly annotated. Although those pairs
are syntactically similar to each other, each of them
express quite different things. For instance:
"variable": "Watts"
means that the event reports about a physical phe-
nomenon which is related to the rate at with elec-
trical energy is consumed. This phenomenon is
not specific to the event nor to the device that has
produced this event. Thus we need to relate the in-
formation conveyed by this event to a concept that
models this phenomenon. So, if this concept al-
ready exists we link the concept representing this
information to the concept representing the phe-
nomenon. If it doesn’t exist we will simply create
it. We call such concepts topics and create them
as instances of a class called
The name of the link between Information in-
stances and InformationTopic instances is called
The rate of electrical energy con-
sumption is a quantitative characteristic of this
phenomenon, which in the OWL modeling lan-
guage we model as a datatype property link.
"value": "45"
means that the target of this datatype property link
should equate to the litteral value
The process of semantically annotating the event
description is illustrated in Fig. 5.
Figure 5: Homelive data semantic annotation.
As you see, some key-value pairs correspond to
links between existing concepts, some refer to con-
cepts that already exist or eventually that have to be
created, some refer to litteral values to be assigned to
concepts through links that already exist or eventually
that have to be created.
Such analysis can be only conducted by one or a
team of domain experts which collectively know the
domain ontology, i.e. the catalog of concepts classes
necessary to describe the application domain, the po-
tential links between instances of these classes, and
axioms that constrain the use of these links. An ex-
ample of such an axiom is that the arity of the re-
is 1, which means that a piece of
information can only has one value and not more.
Such axioms are necessary for the system to decide
on the policy to adopt upon reception of new events
from a device, which has already sent events about the
same topic in the past. Note that this is generally the
case, because once a device has been freshly provi-
sionned and sent its first event to report about a phys-
ical phenomenon, its job is to update this report by
sending other events. If the involved relations, such
is of arity 1 (or “is a functional relation
in the OWL terminology), the current target node in
the model should be removed and replaced by the new
node created by the event abstraction process.
The result of annotating one event description
is a graph fragment consisting of a set of concepts
which are classes of the ontology or instances of
these classes, interrelated by relations of the ontolo-
gies. Some of these concepts are common to differ-
ent events. Which means that assembling these frag-
ments together will result into a larger graph which
will aggregate and relate the information conveyed by
the different event to each other. This graph gives an
overall account of the state of the physical environ-
nement as seen by the pool of devices collectively.
We call this state the situation. Then this graph con-
stitutes a semantic model of the situation.
A userfriendly way to visualize this aggregated
graph and more generally any RDF model, and to
navigate along its edges is to use the Prot´eg´e edi-
tor (pro, ). Using this editor, the semantic model of
our SmartHome data can be displayed as shown in
Fig. 6.
Figure 6: Browsing the model using Prot´eg´e editor.
The concepts classes of the ontology are displayed
as a hierarchical tree on the left part of the screen.
On the right part, concepts, links are displayed as a
graph. Instances are displayed on the right. Hovering
the mouse over nodes will popup datatype properties
revealing the name of the datatype link and its litteral
value. Hovering the mouse over links will reveal the
name of the link.
In the following section we show how this situa-
Semantic Multi-sensor Data Processing for Smart Environments
tion semantic model can be easily exploited to infer
higher level information, which can be directly pro-
cessed to improve the home occupants experience.
Several inferences can be drawn on the basis of this
semantic model. To give a glimpse of the variety
we can mention the following three which have been
identified has highly expected by endusers through
dedicated focus groups conducted by the marketing
Being informed that somebody has just entered
the roomA118, can optimise your personnal pro-
ductivity in case you have set up an appointement
with a colleague for meeting at roomA118 and
you don’t want to wait unecessarily for her/his ar-
Infering that nobody’sin the roomA118is a useful
information for those seeking quiet spots to shut
themselves away and relax.
Detecting that the fridge has being opened for
more than one minute strongly suggest to have
a look and close it if somebody has inadvertedly
forgotten to close it. This will prevent damaging
stored food and unecessary energy loss.
Let’s now elaborate on how this inferences can be
drawn. It would take too much space to detail the
mechanism for all these inferences, so lets take the
first inference “somebody has just entered” and elab-
orate this particular case.
Here is the basic reasoning: As shown in Fig. 1
a move detector
has been placed behind the
door, and the door itself is equipped with a door open-
ing detector
. If a move has been detected by
has detected that the door has
been opened, for sure somebody has entered.
In order to check if the statement S:“
detected a move after
has been opened”, we
have to search the situation model for some fragment
which describes this statement. Searching in a RDF
model amounts to query it using SPARQL query lan-
guage. A sparql query is a graph pattern, i.e. a
subgraph defined using the same ontology elements
(concepts and relations) than the complete graph but
where some of the nodes, resp. some of the links,
may be defined as variables, i.e. can match any node,
resp. any link, in the graph. Before elaborating the
SPARQL query, lets first define the graph pattern that
describe statement S.
detects a move it sends an
event which as we have seen in section 4 up-
dates a piece of information which captures infor-
mation about movement within
zone. Information about mouvement is an instance
of the class
. We then
have to search for a node which is an instance of
and which is linked to
the node that models
device with the rela-
, as according to our ontology, this relation
to its
From this node we should search its value along the
and its timestamp along the rela-
. This preliminary graph fragment,
that describes this part of the search corresponds to
the 6 nodes and corresponding links, on the upper part
of the Fig. 7. Nodes which are searched are named
with a prefix
”. For instance, the node represent-
ing the move information has been named
This is also the case for
which represent
the timestamp of the
information. We have
to do the same for searching the status of the door in-
formed by
detector. This additional part of
our search corresponds to the 6 nodes and correspond-
ing links on the lower part of the figure.
Now that weve introduced the two timestamps
, our final question is “is
greater than
. If the answer is
yes, then we can conclude that statement S is true. To
complete our graph fragment, and thus add this last
question to our SPARQL query, we will use a specific
mechanism that the SPARQL language provides, for
combining several variables and for defining “virtual”
nodes, i.e. nodes which are defined in terms of other
nodes of the graph fragment. In our particular case,
we introduce the “virtual” node
which is computed by subtracting
Figure 7: Has somebody moved in since the door has been
Now that we’ve visualized the graph pattern that
represents our query it is straightforward to format it
using the SPARQL language. Fig. 8 shows how the
SENSORNETS 2016 - 5th International Conference on Sensor Networks
PREFIX hl:<>
SELECT ?m3val ?m3ts ?d1val ?d1ts ?nbSecFromM3toD1
?dev1 hl:says ?info1 . ?dev1 hl:name "MLMove3" .
?info1 hl:isAboutTopic hl:TrippedInformationTopic .
?info1 hl:hasValue ?m3val .
?info1 hl:timestamp ?m3ts .
?dev2 hl:says ?info2 . ?dev2 hl:name "MLDoor1" .
?info2 hl:isAboutTopic hl:TrippedInformationTopic .
?info2 hl:hasValue ?d1val .
?info2 hl:timestamp ?d1ts .
BIND((?d1ts - ?m3ts) AS ?nbSecFromM3toD1)}
Figure 8: SPARQL query.
query looks like.
Basically, each line in the
section corre-
sponds to an edge in the graph representation of the
query as displayed in Fig. 7. Each line represents an
edge as a triplet. For instance the line:
?dev1 hl:says ?info1 .
represents an edge where the source of the link
has the identifier
, the link has the identifier
and the target of the link has the identifier
This work is ongoing. We have obtained good re-
sults on few experiments which show that inferences
that we make with our approach are sound. However
we plan to conduct an extensive testing campain that
confirm the robustness of our system.
A wider perspective and discussion on these first
results are developed in the next section.
Adopting semantic modeling technologies opens up
an avenue of user experience improvements. For in-
stance, one use case we’ve briefly evoked in our pa-
per (Ramparany et al., 2014) but haven’t tested with
real data yet is to take into account information about
devices location, such as rooms where the devices are
located and devices functionality, such as the nature
of data the device measures and reports in case it is a
sensor. Aggregating devices information into the pic-
ture makes it possible for occupants to converse with
their home with questions such as “what is the tem-
perature in the kitchen?”. This query would be de-
composed into looking up all devices located in the
kitchen (device location), then identifying which of
those devices is a thermometer (device function) and
finally retrieving the current temperature measured by
this device. The answer to these 3 sub-query can be
found in the aggregated RDF graph.
Widening the range of information sources be-
yond the IoT domain would even make possible
fancier use cases. For example, if we don’t limit our-
selves to the restricted scope of smart home data, as
we did in the work reported here, but aggregate data
from the Open Data world, we could for example find
out which IKEA cupboard would fit best in kitchen,
in the space between the oven and the wall. For this,
we simply need the dimensions of our kitchen and
its appliances (our Smart Home data) and the dimen-
sions of IKEA products. The laters could be found on
the IKEA online catalog if this catalog is available as
open data. Once aggregated on the common semantic
model, the respective dimensions could be compared.
To this view of having IoT system access open
data sources, there’s of course the dual view point, of
inserting the IoT in the realm of the semantic web and
consider our home, our car, the city as new contribu-
tors to the semantic web by having them publish real-
time information about themselves, their states, their
moods, etc... By the end of the day, the philosophy
would be the same: an aggregation of data originat-
ing from the IoT and the one side and of data from the
public Web on the other side, to form a consolidated
model, and reason upon this consolidated model. The
main difference is about where the aggregation takes
place and who performs the reasoning, an IoT appli-
cation or a Web service? Who cares? the technology
to implement these processes would probably be the
same, and as attested by our experiments this technol-
ogy is there and mature enough to be applied.
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