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Environment Observation System based on

Semantics in the Internet of Things

Liang Hu, Jingyan Jiang, Jin Zhou, and Kuo Zhao* College of Computer Science and Technology, Jilin University, Changchun 130012, China

Email: [email protected], [email protected]

*Corresponding author

Liang Chen College of Software, Jilin University, Changchun 130012, China

Huimin Lu College of Software, Changchun University of Technology, Changchun 130012, China

Abstract—The Internet of Things is rapidly developing in

recent years. A number of devices connect with the Internet.

Hence, the interoperability, which is the access and

interpretation of unambiguous data, is strongly needed by

distributed and heterogeneous devices. The semantics

promotes the interoperability in the Internet of Things using

ontology to provide precise definition of concepts and

relations. In this paper, we demonstrate the importance of

the semantics in three aspects: firstly, the semantics means

that the machines could understand and respond to the

human command. Secondly, the semantics is mainly

reflected in the ontology of the physical world. Thirdly, the

semantics is important for interoperability, data integration

and reasoning. Then, we introduce a semantic approach to

construct an environment observation system in the Internet

of Things. The environment observation system contains

three major components, the first is the ontology of the

environment observation system, the second is the semantic

map of the environment observation system, and the last is

exposing the observation data. Finally, the system publishes

the environment observation data on the Web successfully.

The environment observation system with semantics

provides a better service in the Internet of Things.

Index Terms—Internet of Things; Semantics; Ontology;

Semantic Sensor Network; Environment Observation

System

I. INTRODUCTION

The Internet of Things (IoT) is an expansion of the

current Internet, which refers to interconnecting “Things”

has attracted lots of interest in recent years. It is the potentialities of the IoT that makes it possible for the

development of a huge number of applications [1], such

as transportation and logistics domain, healthcare domain,

smart environment (home, office, plant) domain.

Reference [2] proposed a smart hospital model using IoT

technology in healthcare domain. Reference [3] proposed

an intelligent traffic monitoring system based on IoT

technology in transportation and logistics domain. In smart environment domain, reference [4] puts forward a

smart home system based on the Internet of Things. The essential requirements of IoT are the interoperability,

automation and data analytics, in which case, the

semantics is a significant perspective and solution.

With the help of the semantic technology, we could

effectively help promote the interoperability of the IoT.

So, a number of works has been developed.

The ontology is a modeling tool for describing

knowledge in a semantic way. The ontologies in IoT have a great improvement. The Sensor Web Enablement (SWE)

of OGC defines a suite of interfaces and services to

connect heterogeneous sensors. It contains several

specifications: Observations & Measurement (O&M)

defines standard models and XML Schema for encoding

observations and measurements, which are archived and

real-time; Sensor Model Language (SensorML) defines

standard models and XML Schema for describing sensors systems and processes refer to sensor observations;

Sensor Observations Service (SOS) is a standard web

service interface for a client to request, filter, and retrieve;

Transducer Model Language (TransducerML or TML),

Sensor Planning Service (SPS), Sensor Alert Service

(SAS) and Web Notification Services (WNS) are also

part and parcel of SWE. Recently, an ontology called

SSN for describing sensors and sensor network resources has been developed by the W3C Semantic Sensor

Networks Incubator Group. The SSN ontology provides a

better support for abstraction, categorization, and

reasoning offered by semantic technologies [5]. Since the

SSN ontology defines high level schema of deployments,

systems, the operating restrictions, processes, devices and

data, it answers the need for interoperating with other

ontologies. Linked data plays an important role in effective

reasoning, processing mechanisms and better

interoperability. It is based on the HTTP and URIs and

enables not only the people but also the machines to

explore data on the Web. The resources are identified by

URI and described in RDF, URIs link to other URIs, so

that data from different sources are connected [6]. The

JOURNAL OF NETWORKS, VOL. 8, NO. 12, DECEMBER 2013 2721

© 2013 ACADEMY PUBLISHERdoi:10.4304/jnw.8.12.2721-2727

linked open data (LOD) integrates numerous distributed

data in form of RDF across the Web [7]. Many

applications use linked data principles to annotate IoT

data: Kno. e. sis linked sensor data describes the weather

data using linked data principles and links the Geonames

for searching by geographical location [8]. Sense2Web

provides a GUI to describe IoT data [9]. Page et al. propose an API to publish data of observatory using

linked data principles and REST [10].

In this paper, we just focus on the environment

observation, which is in a higher level of the Internet of

Things. The lower level is the part of data sensing and

data collection. We deployed many sensors in different

rooms of our lab, and these sensors sensed the

environment and collected real data (e. g. temperature, humidity and illumination) into a relational database. In

the higher level, we introduce an approach of semantics

to publish the real sensor data on the Web, it contains

three parts: ontology, semantic mapping and exposing.

The ontology is a data abstract of the existing sensor data,

the real things are mapped to concept and properties. In

our environment observation system, the nodes and some

environmental indicators (e. g. temperature) are described as classes with properties. The goal of semantic mapping

is to map the relational database schema to RDF or

ontology using D2R mapping language, in this process,

the data integration can be applied for context and

situation awareness. Semantic interpretation is a crucial

issue for exploring the semantic descriptions. In this work,

the D2R server is applied to implement the publishment.

The rest of this paper is structured as follows. In section 2, we demonstrate the importance of the

semantics in three aspects. In section 3, we introduce the

architecture of the environment observation system in the

Internet of Things. In section 4, we general evaluate the

environment observation system. Section 5 is a summary

of this paper.

II. SEMANTICS IN THE INTERNET OF THINGS

The semantics makes a great contribution in many domains and draws a lot interests. Reference [11] extends

the FOAF vocabulary for social network domain.

Reference [12] and reference [13] propose the novel

video retrieval approach based on semantics in the muti-

media domain. The semantics is important for the Internet

of Things as well. In order to shed light on the semantics

in the Internet of Things, three issues should be discussed.

Firstly, we will explain the meaning of the semantics. The semantics is the interpretation of an expression. M.

Uschold divided semantics into four levels: implicit,

explicit and informal, explicit and formal for human

processing, and explicit and formal for machine

processing. The implicit semantics is a kind of shared

human consensus such as meaning tags. The explicit and

informal semantics is an informally expressed notation or

language which is mainly for humans. For example, the meaning terms in the Dublin core. Explicit and formal for

human processing semantics is a formal documentation

just for humans. For example, the modal logic for the

semantic definition of ontological categories. The explicit

and formal for machine processing semantics is the kind

of semantics which is used at runtime for automated

inference for machines. In Semantic Web, the semantics

means that the machines could understand and respond to

the human command. It is explicit and formal for

machine processing as M. Uschold explained in 2003

[14]. For example, the machines understand the “temperature” by meaning instead of a character string.

And then the machines will have a reaction relate to

“temperature”, for instance, the machines will give a

command or instruction to a device to open the air

condition. In this process, semantically structured

information (Explicit and formal for human processing

semantics) should change into the machines processing

semantics level for automated inference. Secondly, we will set forth the formal description of

the semantics. The “understanding” ability of machine

requires the information which is semantically structured

derived from the Internet of Things. Hence, the IoT data

should be modeled as ontology. The ontology is an

explicit, formal and shared conceptual model which is

structured using standard ontology language such as

OWL and RDF, and it provides the precise definition of concepts and relations. The precise definition is the key

of knowledge representation, information sharing and

semantic reasoning. Therefore, the semantics is mainly

reflected in the ontology. In the Internet of Things, the

scenario could be described as ontology. The nodes and

observation attributes are the classes and relations.

Thirdly, we will underline importance of the semantics

in the Internet of Things. First, the semantics is important for interoperability. The Internet of Things requires a

stunning number of devices connecting with Internet. For

this reason, the interoperability, which is the access and

interpretation of unambiguous data, is intensively needed

by distributed and heterogeneous devices and is provided

by the unambiguous data representation. The

semantically structured data precisely answers the need to

unambiguity. Therefore, the semantics is essential or even indispensable in interoperability. Second, the semantics

plays a vital role in data integration. The goal of the

Internet of Things is to sense the environment and react

intelligently. The IoT data can combine with other

domain information and integrate to infer new

information. Consequently, the unambiguous data

representation is an approach for heterogeneous data

integration and semantic descriptions. Third, the semantics is a key foundation in reasoning. The reasoning

enables the inference from existing rules and information.

The semantically structured data is the premise of the

reasoning. In this way, the Internet of Things could react

intelligently.

III. THE ARCHITECTURE OF THE ENVIRONMENT

OBSERVATION SYSTEM

This section introduces the architecture of the environment observation system. The environment

observation system is based on semantics, using standard

ontology languages such as RDF or OWL to represent the

data which is stored in the RDB (relational database).

2722 JOURNAL OF NETWORKS, VOL. 8, NO. 12, DECEMBER 2013

© 2013 ACADEMY PUBLISHER

And this system is collecting real world data (i. e.

humidity and temperature) through the sensors deployed

in the Internet of Things platform. Consequently, this

system aims to publish the real observational

environment data in a semantic way and integrate more

information in specific scenario.

The environment observation system comprises three major components as illustrated in Fig. 1, the first one is

the ontology of the environment observation system, the

second one is the semantic map of the environment

observation system, and the last one is exposing the

observation data. And the system is in the top level of

Internet of Things platform. Obviously, the initial data is

provided by sensors and stored in the database in the

lower level. The ontology of the environment observation system

describes the observation data in sensor network domain,

and exploits the Semantic Sensor Network (SSN)

ontology which is the standard ontology for describing

sensors and sensor network by the W3C Semantic Sensor

Networks Incubator Group. In this paper, we just focus

on the observation data of the sensors so that only a

subset of the SSN ontology concepts is applied to the system. The ontology is modeled through the actual IoT

scene to describe the environment of two rooms in our

labs.

The semantic map of the environment observation

system is the core component of this system. The RDFS

vocabularies and OWL ontologies could be published on

the Web, thus, the observation data which stored in the

relational database (i. e. MySQL) could be mapped into the RDFS vocabularies and OWL ontologies. In order to

describe the relation between a relational database

schema and RDFS vocabularies or OWL ontologies, the

D2RQ platform is applied in the system. The observation

table is mapped into an observation class, and the

columns in the table are mapped into the properties. The

classes and properties provide precise descriptions of the

environment observation scene and the semantization of data. In addition, the semantic map could provide more

information by integrating other domain knowledge such

as the location. The data in relational database is flexibly

reassembled and linked as well.

Data Provider

Data Collection

semantic mapontology

Exposing

Environment observation system

Database

Sensors

Internet of Things

Figure 1. The levels of the environment observation system.

The exposing the observation data process publishes

data on the Web. In order to access the content of the

database as linked data over the Web, the D2R server is

an appropriate choice. D2R server provides a linked data

view, a HTML view and a SPARQL Protocol endpoint

over the database by HTTP protocol [15]. Hence, the

linked data could be browsed and searched using web

browsers and SPARQL easily.

sensor

RDB

sensor

ontology

mapping

Virtual RDF Graphs

HTML Browser

data

information

knowledge

Lo

we

r

Figure 2. The data flow diagram

Figure 3. The database schema

In order to illustrate the data flow of the environment observation system, a data flow diagram is shown in Fig.

2. The data transformation process is mainly divided into

three stages. First, the raw sensory data is perceived by

the sensors, and collected by a relational database directly.

The data is structured in the form of database table

without any semantics. Then the data is described as a

sensory ontology based on the content of database table.

All the observation items are described as classes, and each class has several properties. The mapping file is

created on the basis of the ontology and database schema

using D2R mapping language. Finally, the data from

relational database is mapped into a virtual RDF graphs

via a mapping file. It is worth noting that a dotted line

points to virtual RDF graphs from RDB. In fact, the

transformation from RDB to virtual RDF graph is direct

under the guidance of mapping file. Since the mapping file guides how the instances of virtual RDF graph are

created, it is crucial in the process. On the right of the

figure, a “knowledge hierarchy” corresponding to the

three transformation stages are shown. The data layer is a

lower layer refers to numberous raw data collected by the

IoT devices. The information layer is in the middle, the

interoperability is achieved by structuring with semantics

from the raw data. The knowledge layer provide machine-readable and machine-comprehensible data,

which could be browsed and searched on the Web. It is

obvious that the data becomes more structured, integrated

and semantic through the environment observation

system, which answers the requirement of the Internet of

Things.



nodeIdCls

time

airHumidityCls_unit

airTemperatureCls

LightCls

nodeIdCls_parent

nodeIdCls_board_id

nodeIdCls_roomOnessn:observedBy

ssn:observationRusultTime

ssn:observedProperty



eos:unit

ssn:hasValue

eos:hasParent

eos:hasBoard

eos:roomBelong

airHumidityCls

airTemperatureCls_unit

LightCls_light

airHumidityCls_humidity

airTemperatureCls_temperature

LightCls_uniteos:unit

ssn:hasValue

eos:unit

ssn:hasValue

nodeIdCls_roomTwo

eos:roomBelong

Observation_sht11_5w

Figure 4. The ontology of the environment observation system

A. The Ontology of the Environment Observation System

In general, ontology is a specification of a

conceptualization [16], it is a modeling tool for

describing knowledge in a semantic way. In this paper,

the ontology is applied to modeling the concept of the environment observation system based on the actual IoT

scene and take advantage of the SSN ontology.

TABLE I. THE PROPERTIES OF ONTOLOGY

Property name explanation

ssn:

observationRusult

Time

Points to the time when the observation

result became available.

ssn: observedBy Points to a sensor which produced the

observation data.

ssn:

observedProperty

Points to the specific quality of the feature

of interest which was observed.

eos: hasParent Points to the parent nodes.

eos: hasBoard Points to the sensor board ID.

ssn: hasValue Points to the actual value of the

observation data.

eos: roomBelong Points to the room ID which the nodes

belong to.

eos: unit Points to the measuring unit of the

observation value.

The observation data is obtained from the sensors

which are deployed in our lab, and is stored in the

database. The database schema is shown in Fig. 3.

Idsht11_5w is the primary key, it is an ID of all these

data. Nodeid is the ID of the nodes which are placed in

the lab. The parent records the node ID which represents

routing path of the data. Board_id is applied to manage sensor boards by recording the ID of sensor boards which

is attached to the nodes. The airHumidity is the real air

humidity of the room, and measured by the relative

humidity. The airTemperature is the real temperature of

the room, and its unit is centigrade. The light is the real

illumination intensity of the room, its unit of

measurement is lux. The time is the date and time, and

the pattern demonstrates like this “yyyy-mm-dd hh: mm: ss”.

The ontology of the system is proposed on the base of

database schema as illustrated in Fig. 4, The ellipse

means classes and the arrows means properties. Explicitly,

the ontology provides more information, the measuring

unit and the room ID that deployed nodes is the

additional information. The data is coordinated by the location information and the time information which

makes the observation model more concrete and precise.

Also the ontology reassembles the data in a hierarchical

way. The properties are shown in the Table I.

B. The Semantic Map of the Environment Observation

System

The semantic map plays a very important role in the

system. It maps the schema of a relational database to

RDF and defines a virtual RDF graph which contains

information from the database. D2RQ platform provides

a mapping language to accomplish the map process. The

main goal of the mapping language is to map a RDF from the RDB without changing the existing database schema.

The complexity is reduced greatly by joining the SQL

statement into the mapping process. The mapping process

is mainly divided into four steps as follows:

(1). A record set is selected for each class by SQL

statement.

(2). Grouping the record set according to the specific

ClassMap. (3). Creating the instances of each class and assigning

the URI.

(4). Datatype and property bridges are applied to each

instance property.

A virtual RDF graph is created after the four steps

above. The virtual RDF structure for the system is shown

in Fig. 5.

The database is mapped to RDF terms through d2rq: ClassMap and d2rq: PropertyBridges, shown on the right.

The class map is the most important concept. The class

map represents a class or a group of similar classes of the

ontology. A class map specifies how URIs are generated

for the instances of the classes [17]. In this semantic map,

the map: observation_sht11_5w_ClassMap is the major

class map, and all the observation items are included in it.

In order to expand the observation model, we structure additional class map, nodeidCls_classMap provides more

information of the nodes, such as serial numbers, board

IDs and the locations. It is noteworthy that the nodeid

property of observation_sht11_5w_ClassMap doesn’t

have the concrete value, instead, it references another

d2rq: ClassMap which creates the instances which are

used as the values of nodeidCls_classMap by d2rq:



Map:database

Map:nodeIdCls_classMap

Map:id_PropertyBridge

Map:parent_PropertyBridge

Map:board_id_PropertyBridge

Map:room_one_PropertyBridge

Map:room_two_PropertyBridge

eos:id

eos:hasParent

eos:hasBoard

eos:roomBelong

eos:roomBelong

refersToClassMap map:nodeIdCls

belo

ngsTo

ClassM

ap

uriPattern "nodeIdCls/@@observation_sht11_5w.ideos_5w@@"

column "observation_sht11_5w.nodeid"

column "observation_sht11_5w.parent"

column "observation_sht11_5w.board_id"

constantValue eos:roomOne

constantValue eos:roomTwo;

uriPattern "airHumidityCls/@@observation_sht11_5w.ideos_5w@@"

constantValue eos:Relative_Humidity

property

property

property

property

property

Map:Observation_eos_5w_classMap

Map:ideos_5w_PropertyBridge

Map:Nodeid_PropertyBridge

Map:airHumidity_PropertyBridge

Map:time_PropertyBridge

column "observation_sht11_5w.ideos_5w"

vocab:observation_eos_5w_idsht11_5w

ssn:observationResultTime


ssn:observedBy

column "observation_sht11_5w.time"

uriPattern "observation_eos_5w/@@observation_sht11_5w.ideos_5w@@"

refersToClassMap map:airHumidityCls

belo

ngsTo

ClassM

ap

property

property

property

property

Map:airHumidityCls_classMap

Map:humidity_PropertyBridge

Map:unit_PropertyBridge

ssn:hasValue

eos:uint

column "observation_sht11_5w.airHumidity"

Property

property

ssn:Sensor

class

class

class

vocab:observation_sht11_5w

vocab:airHumidityCls

:dataStorage

:dataStorage

:dataStorage

…

…

…

…

belo

ngsTo

ClassM

ap

Figure 5. The virtual RDF structure for the system

referenceClassMap. It is just like the concept of the

foreign key. Along the same lines,

airHumidityCls_classMap, airTemperatureCls_classMap

and lightCls_classMap are structured for the same

purpose.

Each class map has many property bridges, which

specifies how the properties of an instance are created [17]. Therefore, property bridges map a database column

to an RDF property. The d2rq: column points the

properties with literal values which are the table column

names in the relational database. For example, d2rq:

column "observation_sht11_5w. time" means the value of

map: time_PropertyBridge is observation_sht11_5w. time.

The d2rq: constantValue points that the property has the

same constant value on all the instances of the class map.

For example, d2rq: constantValue eos: roomTwo is the

constant value of the map: room_two_PropertyBridge,

which implies that the location for some nodes. Taking

advantage of the d2rq: constantValue, we could provide some constant value inexistent in the database. The d2rq:

referenceClassMap is also a property of d2rq:

PropertyBridge and we have discussed it before.



Figure 6. The query interface

Figure 7. The classes of the system

Figure 8. The properties of the system

C. Exposing the Observation Data

In order to publish the RDF that we have created before, the D2R server is applied. D2R Server is a tool

for publishing the content of relational databases on the

Semantic Web. D2R Server uses a D2RQ mapping to

map relational database schemas into RDF, and allows

the RDF data to be browsed and searched on the

Semantic Web. To start D2R Server, we need the

command as follows: “d2r-server –p 8080 mappingName.

ttl”, then open the URL “http: //localhost: 8080/snorql/” in a web browser. A query interface is shown in the web

just as the Fig 6, Then we could write the query into the

text of the SPARQL section and push the "Go!" button to

obtain the results of the query in the SPARQL results

section. The classes and properties of the system is shown

in the Fig. 7 and Fig. 8. An example of a query for air

temperature resources is shown in the Fig. 9. This

example searches all the useful information about the air temperature of No. 997 through the data links such as

time, unit, value and observed nodeid. The result is

shown in the Fig. 10. The information of air temperature

of No. 997 has been assigned a uniform resource

identifier (URI), http: //localhost:

8080/resource/airTemperatureCls/997. The SPARQL

result is a triple in a pattern as “subject-verb-object”. The

subject is the information of air temperature of No. 997,

in other words, the URI mentioned above. The verb is the

property column listed in the left such as eos: unit and rdf:

type. The object is the hasValue column and isValueOf

column. A underlying sentence is made by connecting the

subject-verb-object. For example, the unit of air

temperature of No. 997 is centigrade. The underlying sentence demonstrates the characteristics of semantic.

WHERE { { <http://localhost:8080/resource/airTemperatureCls/997> ?property ?hasValue } UNION { ?isValueOf ?property <http://localhost:8080/resource/airTemperatureCls/997> } UNION { <http://localhost:8080/resource/observation_sht11_5w/997> ?property ?isValueOf FILTER (?isValueOf != <http://localhost:8080/resource/LightCls/997> ). FILTER (?isValueOf != <http://localhost:8080/resource/airHumidityCls/997>) . FILTER (?isValueOf != <http://localhost:8080/resource/airTemperatureCls/997> ). FILTER (?property != rdf:type) }}ORDER BY (!BOUND(?hasValue)) ?property ?hasValue ?isValueOf

Figure 9. The SPARQL query

IV. EVALUATION

The Internet of Things is rapidly developing in recent

years. Many applications based on the Internet of Things

are developed in various domains. Reference [4] puts

forward a smart home system based on the Internet of

Things. The monitoring system has three levels, the sensing layer, network layer and application layer. The

data is collected in the sensing layer, transmitted in the

networks layer and processed in application layer.

Obviously, the whole system is without the semantics.

In this paper, we propose an environment observation

system architecture which exposed the observation data

on the Semantic Web. Besides the three layers mentioned

above, environment observation system has a semantics layer. Hence, the observation data which is provided by

the sensors could be browsed and searched in a semantic

way. The semantics is derived from the given ontology

model. The ontology is modeled through the actual IoT

scene to describe the environment of two rooms in our

labs. The ontology provides precise definition of concepts

and relations which reflects semantics. However, the

smart home system could not be searched in the semantic way and different devices do not share the same concepts

which lead to the lower interoperability.

The D2R mapping language perfectly maps the

relational database schemas to the RDF. The mapping

process mixes the data from the database and integrates

more information as specific scenario. However, in smart

home system, the data is structured in relation database

without semantics. Hence, the integration of the data is only implemented in application layer and the degree of

integration is lower.

In general, to publish environment observation system

in a semantic way is a non-trivial task. In the Internet of

Things, it is not enough to network the devices, but the

semantization of data, which promote data understanding

and semantic reasoning, so that it makes the Internet of

Things smarter.



http://localhost:8080/resource/airTemperatureCls/997

http://localhost:8080/resource/airTemperatureCls/997

Figure 10. An example

V. CONCLUSIONS AND FUTURE WORK

In this paper, we first illustrate the importance of the semantics in the Internet of Things by discussing three

issues: What is the semantics? Where is semantics in the

Internet of Things? Why is semantics important? Then,

we introduce a semantic approach to the Internet of

Things through constructing an environment observation

system. The environment observation system comprises

three major components, the first is the ontology of the

environment observation system, the second is the semantic map of the environment observation system,

and the last is exposing the observation data. The system

publishes the environment observation data on the

Semantic Web successfully. In the future, we could add

our semantic environment observation data in the Internet

of Things into the Linked Open Data (LOD) for sharing

and interoperability.

ACKNOWLEDGEMENT

This work was supported in part by the National High

Technology Research and Development Program of

China under Grant No. 2011AA010101, the National

Natural Science Foundation of China under Grant No.

61103197, 61073009 and 61240029, the Science and

Technology Key Project of Jilin Province under Grant No.

2011ZDGG007, the Youth Foundation of Jilin Province

of China under Grant No. 201101035, and the Fundamental Research Funds for the Central Universities

of China under Grant NO. 200903179, the China

Postdoctoral Science Foundation under Grant No.

2011M500611, he 2011 Industrial Technology Research

and Development Special Project of Jilin Province under

Grant No. 20110069, the 2012 National College Students'

Innovative Training Program of China.

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