Semantic Resource Discovery with CoAP in the Internet of Things
Ali Yachir
, Badis Djamaa
, Kheireddine Zeghouani
, Marwen Bellal
and Mohammed Boudali
Artificial Intelligence Laboratory, Military Polytechnic School, PO BOX 17, Bordj-El-Bahri, 16111, Algiers, Algeria
Centre for Electronic Warfare, Cranfield University, SN6 8LA, Shrivenham, U.K.
Keywords: Internet of Things, Smart Environments, CoAP, Device, Resource, Service, Smart Object, Semantic Web
Technologies, CoRE Link Format, Request Resolution, Semantic Resource Discovery, Resource Directory.
Abstract: The Constrained Application Protocol (CoAP) is a lightweight and power-efficient Internet standard
specifically designed for M2M communication in the Internet of Things (IoT). CoAP provides a set of
mechanisms for IoT interactions including request/response, publish/subscribe and resource discovery. For
the latter, a Resource Directory (RD) solution is proposed to register and store information about IoT resources
to be queried by users. Such a solution, however, only allows syntactic discovery. In this paper, we extend
CoAP with lightweight semantic-rich information by defining appropriate CoRE link format attributes
describing both IoT resources and user requests. Such an extension is integrated with the RD to facilitate
semantic resources discovery. Implementation and thorough evaluations of the proposed approach show
important performance enhancements when compared with the default RD solution.
As a natural continuity of ubiquitous computing
(Weiser, 1991) and ambient intelligence (ISTAG,
2003), the Internet of Things (IoT) (Atzori et al.,
2010; Suresh et al., 2014) envisages a future Internet
architecture integrating both physical and cyber
worlds by combining sensing and actuation with
digital services. The challenge resides in ensuring
seamless interoperability among a huge number of
constrained heterogeneous IoT devices. Indeed, IoT
devices are typically resource-constrained objects
mainly characterized by limited memory, energy and
processing power. Additionally, such devices
communicates over Low-power and Lossy Networks
(LLNs), such as IEEE 802.15.4 imposing, thus, other
constraints on the reliability and amount of
exchanged data. Hence, seamless integration of such
devices into the Internet requires new lightweight and
power-efficient protocols. Among several
alternatives, the Constrained Application Protocol
(CoAP) (Shelby et al., 2014) is emerging as a
widespread standard that fulfil most of the above-
mentioned requirements.
In CoAP-enabled IoT applications, each device is
seen as an endpoint, exposing sensor readings,
actuating capabilities and internal information as
REST resources (Fielding and Taylor, 2002) that can
be queried by clients. Moreover, CoAP-based
systems usually use a CoRE Resource Directory (RD)
(Shelby et al., 2017) where resource providers
register their available resources for clients to query.
However, the RD solution only allows syntactic and
simplistic data-oriented registration and querying of
resources. For this reason, automatic and intelligent
discovery of required resources among huge
heterogeneous ones remains inadequate with the
native RD. Thus, semantic enhancement of CoAP
and, obviously of RD, is a key aspect for better
representing, storing, organizing, discovering and
providing information generated/consumed by IoT
entities. This challenge can benefit from the semantic
Web technologies (Barnaghi et al., 2012; Bonino and
Procaccianti, 2014) such as Resource Description
Framework (RDF), Ontology Web Language (OWL)
and Protocol And Rdf Query Language (SPARQL).
Extending RD with semantic-rich information
may follow three major steps. First, defining a
comprehensive semantic model describing all the
physical and virtual entities surrounding a device
such as locations, persons, appliances and resources.
Second, extending CoAP to support semantic
resources registration and querying of the RD. Third,
integrating semantics in the RD itself to allow
discovery and ranking of resources, which best match
user requirements and closely meet the specified
quality of service level.
Yachir, A., Djamaa, B., Zeghouani, K., Bellal, M. and Boudali, M.
Semantic Resource Discovery with CoAP in the Internet of Things.
DOI: 10.5220/0006419400750082
In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) - Volume 6: WINSYS, pages 75-82
ISBN: 978-989-758-261-5
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Although some work has been proposed for
semantic modeling of things in the literature, the three
aforementioned steps are either addressed partially or
are at a preliminary stage and need to be studied more
deeply. Accordingly, in this paper, we propose a new
semantic support for CoAP by defining appropriate
CoRE Link Format (CLF) (Shelby, 2012) attributes
describing both IoT devices and user requests. This
work is in the continuity of our previous work
presented in (Yachir et al., 2016a; Yachir et al.,
The rest of this paper is organized as follows.
Section 2 discusses related work applying semantic
web technologies in IoT. Section 3 provides an
overview of the proposed semantic model in our
previous work. Section 4 defines appropriate
attributes for mapping between the designed semantic
model and CoAP. This is followed by the description
of the proposed framework for device registration and
interrogation via the resource directory (RD). Section
6 is devoted to assess the performance of the proposed
mechanisms. The paper concludes in section 7.
Various research work have been proposed recently
to improve interoperability between heterogeneous
IoT devices using semantic web technologies. In
(Yuan et al., 2013), a general tree-based metadata
model is proposed to describe IoT entities along three
clusters of information: resource, service and context
information. In (Taccari et al., 2015), IoT entities and
features characterizing an earthquake scenario are
described using earthquake emergency management
ontologies (Spalazzi et al., 2014). In (Wang et al.,
2015), a framework for multisource heterogeneous
information fusion in the IoT is designed. The
collected sensor data are modelled using the Semantic
Sensor Network (SSN) ontology (Compton et al.,
2012). In (Sun and Jara, 2014), the authors propose a
semantic model for IoT information organizing where
object and event layers are represented using a
semantic link network model. In (Jara et al., 2014),
the aspects of the Semantic Web of Things (SWoT)
are presented and discussed along with analysing
their impact on the performance of the IoT resources.
In (De et al., 2011), based on the SENSEI project and
the SSN ontology, a semantic annotation framework
for IoT components is proposed.
In (Ruta et al., 2013), a novel framework for
SWoT based on a backward-compatible extension of
CoAP is proposed. In (Kovatsch et al., 2015), a
semantic description of IoT devices, based on the
RESTdesc format (Verborgh et al., 2012), is designed
to deal with self-configurable service composition in
resource-constrained environments using CoAP. In
(Yachir et al., 2016b), a service-oriented, user-
centered and event-aware Framework for service
discovery and selection in IoT is proposed. In (Ayari
et al., 2015), a semantic approach is proposed for
robots interaction with humans. In (Han and Crespi,
2017), a service provisioning architecture for smart
objects with semantic annotation is proposed to
enable the integration of IoT applications into the
Web. In (Urbieta et al., 2016), an adaptive service
composition framework for IoT-based Smart Cities is
proposed. In (Roffia et al., 2016), a publish-subscribe
architecture, based on a generic SPARQL endpoint,
is designed for interoperability in IoT.
The works discussed above show that various
semantic models are proposed to describe things, but
they lack three important factors. First, some of the
proposed semantic models deal only with sensors
capabilities using SSN ontology without addressing
actuators and the more general notion of thing
ignoring its relationships with either the physical and
digital worlds. These relationships are important to
identify the entities to which a device is attached or it
might control, the space where is located and services
(context, event and/or action) it might provide.
Second, the notion of quality of service (QoS) is taken
into account partially without including either user
preferences, the quality of the physical device or the
quality of its hosted software resources. Finally,
integration of the proposed models in CoAP and RD
is not considered or is at a preliminary stage. Indeed,
the mapping from concepts in the semantic model to
CoAP protocol is not clearly defined. As a result, RD
still performs simplistic resource discovery without
semantic matching of user requirements and the
specified quality of service parameters.
This section gives a brief overview of our proposed
semantic model for IoT (Yachir et al., 2016a), which
describes both IoT resources and user requests along
with their resolution.
3.1 Resource Description
An IoT resource includes the physical device and its
hosted software services. Accordingly, its description
WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems
comprises two main parts: device description and
service description, as shown in Figure 1 bellow.
Figure 1: Proposed semantic model for device and service
Firstly, the device description part is structured
around four main entities: space, person, appliance
and the device itself. Device is the key entity that
takes control of the three other entities. It refers to a
physical object that can be a sensor or an actuator and
it has four main direct relationships: a device may be
worn by a person or embedded on an appliance. It can
also control an appliance and obviously, it is located
at a given space. Moreover, each entity is described
in its own reference ontology. Accordingly, four
reference ontologies are distinguished: Device
Reference Ontology (DRO), Space Reference
Ontology (SRO), Person Reference Ontology (PRO)
and Appliance Reference Ontology (ARO). Property
attributes of such ontologies are defined in a well-
known ontology denoted as a Reference Ontology
(RO). This ontology plays the role of a global
dictionary where entities/services share vocabulary.
Secondly, the service description part is
structured around a core concept called ambient
service (as), which represents a software resource
provided by a device. An ambient service can provide
output parameters, observed parameters as well as
effects with some quality of service level. Output and
observed parameters are annotated in the reference
ontology (RO) whereas the produced effect is
represented as an RDF statement, in a Subject-
Predicate-Object structure. Statements are triplet that
consist of an Entity (“subject”), a Property
(“predicate”), and the value of the Property
(“object”), where this value can be another entity, an
attribute or a literal. Regarding the quality of service
(QoS), we distinguish the quality characterizing an
ambient service (SQoS) and the quality of the device
(DQoS) hosting such a service.
3.2 Request Description and Resolution
A user request is described by four main components
as follows: requested subject, requested subject
property, required QoS level and required QoS
parameters. Requested subject is formalized as a
concept, a sub concept or an individual from one of
the aforementioned reference ontologies. Requested
subject property is a specific characteristic
formulized as an ontological property of the requested
entity. Required QoS level specifies a minimum score
threshold required for the quality of service. To
compute a score of an ambient service, user should
specify in its request the relative importance accorded
for each required QoS parameter.
Candidate ambient services that satisfy functional
requirements (i.e. requested subject and requested
subject property) are inferred using the Request
Resolution Rule (R3) depicted in Figure 2 using
SPARQL. The inferred services are then evaluated
and ranked according to the quality of service
including SQoS, DQoS and user’s preferences. In this
paper, the R3 rule is implemented in the RD side and
the proposed semantic model is mapped to the CoAP
protocol and implemented under Contiki OS.
Figure 2: Request Resolution Rule (R3) described in
CoAP follows the REST architectural style for
making data and resources accessible. In fact, every
resource in CoAP is identified by a URI (Uniform
Resource Identifier). Clients may access resources via
synchronous request/response interactions, using
HTTP-derived methods GET, PUT, POST, and
DELETE. Furthermore, CoAP provides a mechanism
for registering, discovering and advertising resources
that a given CoAP server is making available. Such
SELECT ?device ?as WHERE
{ ?device :hasRelationshipProperty :Subject . ?as
:isProvidedBy ?device.
?as :hasOutputParameters : Parameter }.........(a)
SELECT ?device ?as WHERE
{ ?device :hasRelationshipProperty :Subject . ?as
:isProvidedBy ?device.
?as :hasObservedParameters : Parameter }......(b)
SELECT ?device ?as WHERE
{ ?device :hasRelationshipProperty :Subject . ?as
:isProvidedBy ?device.
?as :hasEffects ?eff. ?eff :hasSubject :Subject.
?eff :hasObject :Parameter}..............................(c)
Semantic Resource Discovery with CoAP in the Internet of Things
discovery protocol uses the CoRE Link Format (CLF)
specification as default, where a client can access the
reserved /.well-known/core URI path on the server
with the POST method to register a resource, or with
GET to discover available ones. GET requests can
include specific attributes in the URI-query field to
filter a particular resource to retrieve. Some standard
attributes are defined in (Shelby et al., 2017). This
work reuses existing standard attributes as much as
possible along with defining others, when necessary,
to fulfil the requirements of mapping our semantic
model to CLF.
4.1 CLF based Resource Description
The proposed semantic device description in CLF
contains both standard and new added attributes. For
instance, the standard attribute endpoint (ep) is used
for device name while a new attribute called entity
(ent) is used to map the three main other entities of
the model, namely person, space and appliance.
Table 1 presents the proposed mapping of our
semantic model in CLF. The mapping reuses standard
attributes as much as possible. It may give new
semantics to some standard attributes such as rt in a
way that is backward compatible. The mapping also
introduces a few new attributes necessary to describe
the semantics of our model. All attributes, their
necessity and meanings are discussed below.
ep := endpoint (mandatory). The name of the
registering device, unique within that domain. Its
maximum length is 63 bytes.
et := endpoint type (mandatory). The URI of the
device domain conceptualization named in our
model as Device Reference Ontology (DRO);
rt := resource type (mandatory). The functional
parameters of the ambient service provided by the
device in et. As mentioned in section 3, a
functional parameter can be an output/observed
parameter or an effect. Hence, rt is formatted to
support both. First, an output/observed parameter
is described in rt as follow:
where Service indicates the name of the ambient
service; Output indicates the name of the output
parameter; and Output_RO indicates the reference
ontology where the output parameter is described.
Second, an effect is described by six fields in rt as
where Service and Effect indicate the name of the
ambient service and the effect respectively;
Subject and Subject_RO indicate respectively the
name and the reference ontology of the subject of
the effect. Finally, Predicate and Object indicate
the predicate name and the object of the effect.
obs := observable (optional). Indicates whether
the described output parameter in an rt field is
observable. So, obs is combined with rt
describe an observed parameter. A resource is
observable if its obs equals 1.
ent := entity (mandatory). Indicates the name of
the entity (space, appliance and/or person) which
has a relationship with the device referenced in ep
and et. Accordingly, ent is structured on three
fields as follow:
where Space is the name of the location where the
device is situated; Appliance is the name of the
appliance controlled by the device. Embedded
indicates whether the device is embedded on the
appliance or not. Finally, Person is the name of
the person wearing the device. It should be noted
that a given device could be only on one of the
three flowing states at the same time: worn by a
person; embedded on an appliance or free.
entro := entity reference ontology (mandatory).
Indicates the URIs of the domain
conceptualization of the entities specified in the
ent field. In other words, it contains the three
above mentioned reference ontologies namely:
SRO, PRO and ARO. Accordingly, entro is
structured as follow:
sqos := service QoS (optional). Contains the
quality of service (QoS) parameters of the
ambient service referenced in rt. It is structured on
n fields such as n is the number of QoS
parameters. Each field is divided on three sub
fields indicating the name of the quality
parameter, its current value and a flag (min/max)
indicating whether the parameter is to maximize
(min/max=1) or to minimize (min/max=0). Thus,
sqos is structured as follow:
dqos := device QoS (optional). Contains the
parameters of quality of service (QoS) of the
device referenced in ep and et. dqos has exactly
the same structure as sqos where the key word
dqos is used instead of sqos.
rt= "Service|Output|Output_RO"
entro= "SRO
: max/min|…|sqos
WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems
Table 1: Resource Description Mapping to CLF.
Concept Concept
Device name Endpoint (ep)
Device Reference
Endpoint Type
Ambient Service
Resource Type
Observable Observable
Entity Names Entity (ent)
Added attributes
Entity Reference
Service QoS
Device QoS
Having introduced and detailed the necessary
mapping attributes, let us see the corresponding
semantic description of a device named
Imote2Sensor” located in the “Kitchen” and
characterized by energy level and reliability as dqos
parameters. This device provides a service
getTemperature” having “temperature” as an output
parameter that can be observed. In addition, the
provided service is characterized by a response time
and energy cost as sqos parameters. The CoAP
message corresponding to such description is:
4.2 CLF based Request Description
Having presented the proposed mapping of our
semantic resource description into the CoRE Link
Format carried in CoAP messages, this section
follows the same approach to map user requests.
Hence, as mentioned in section 3.1, a user request is
described by four main components, namely:
requested subject, requested subject property,
required QoS level, required QoS parameters.
In the proposed mapping of Table 2, Requested
subject maps to the newly introduced link format
attribute entity (ent), whereas requested subject
property is mapped to the resource type (rt) attribute.
Moreover, required QoS parameters are mapped to
the defined sqos and dqos attributes in CoRE link
format. Finally, required QoS level is mapped to a
semantic threshold (sr) attribute similar to that
defined in (Ruta et al., 2013).
Table 2: User Request Description Mapping to CLF.
Required QoS
Semantic threshold (sr)
subjet param.
Resource Type (rt)
Observable (obs)
Required QoS
Service QoS (sqos)
Device QoS (dqos)
Using these attributes, a user requesting the
temperature” of the “kitchen” with high energy
level, very high reliability, a medium response time
and a low energy consumption as QoS preferences
can retrieve good matches above a threshold of 0.6 by
issuing the following CoAP query:
Having presented our model for extending CoAP’s
resource discovery with semantics, we have designed
and implemented a framework integrating the
proposed semantic model along with its
representation in CoAP. The architecture of such a
framework, shown in Figure 3, is structured around
five main components namely: Resource Directory
(RD), Device, Border Router, User Interface and
Reference Ontologies Server (ROS). RD, Device and
Border Router components are implemented under
Contiki 3.0 OS using Cooja simulator whereas User
Interface and ROS are implemented respectively in
standards web browser and web server.
Figure 3: Framework for Resource Description, Discovery
and Retrieving.
entro=" Space.owl;
dqos="Energy_level:70:1|Reliability:0.6:1"; rt="
coap://adressRD? ent="kitchen”; rt=”temperature”; dqos=
"Energy_level: high | Reliability: very high"; sqos=
"Response_Time:medium | Energy_Cost: low" ; sr=”0.6”
Semantic Resource Discovery with CoAP in the Internet of Things
Firstly, an RD is a web entity used as a repository
that stores information about web resources (devices
and services) and implements two main REST inter-
faces POST_Handler and GET_Handler. The first in-
terface is dedicated for resources registration whereas
the second one is dedicated for lookup of those resour-
ces using the R3 rule with SPARQL (see Section 3.2).
Secondly, a device in the proposed framework can
be a sensor and/or an actuator with resources
encapsulated as ambient services. A device can send to
the RD either its description via POST or a request
through a GET message. Thirdly, the Border Router is
a node that relies a PC or a Smartphone to the network
through a Serial Line Internet Protocol (SLIP)
(Romkey, 1988) or any available network interface.
Inside the constrained network, packets are routed
using RPL (Winter, 2012) or any routing protocol
deployed within the network.
Fourthly, Reference Ontologies Server (ROS) is a
web server that stores and provides access to all the
reference ontologies, namely: ARO, DRO, PRO, SRO
and RO. Information from such ontologies can be
retrieved or updated using the SPARQL language. An
example scenario using concrete ontologies is given in
(Yachir et al., 2016a).
Finally, user interface is developed based on the
Copper plugin (Copper CoAP, 2016), which is a CoAP
agent for Firefox. In this work, we have developed two
new plugins/apps for Google Chrome and Android OS
allowing seamless interactions between users and
objects using smartphones. Such applications are
developed using HTML5/CSS3, Bootstrap, JQuery
and Chrome APIs. The developed application provides
dedicated interfaces for both device registration and
user request specification. The developed application
and a simple use case scenario implemented in Contiki
OS using Cooja are reported in the following link:
6.1 Experimental Model and
Performance Metrics
The proposed semantic model over CoAP is abbrevia-
ted SemRD for Semantic Resource Directory. To eva-
luate the performance of such a model, we imple-
mented it in Contiki OS. To put results into context,
SemRD was compared with the standard RD solution.
A scenario comprising a simulated network composed
of thirty (30) emulated Sky motes and a resource
directory was used in our evaluations. The topology is
depicted in Figure 4. Each mote can play the role of
either a client requesting available resources from the
RD/SemRD or a provider registering its resources at the
RD/SemRD or both roles. In all cases, a node is
considered aware of the RD before accessing it.
For the sake of this evaluation, we developed an
application running the two RD solutions above UDP
in a constrained 6LoWPAN network in Contiki. At the
routing layer, the RPL protocol is deployed to ensure
routing between RDs and other network nodes. At the
link layer, simulations are conducted both with the
ContikiMAC (Dunkels, 2011) Radio Duty Cycling
(RDC) protocol and without using RDC (NullRDC). Si-
mulation configuration parameters are given in Table 3.
Figure 4: Simulated network topology.
To be able to draw conclusions on the time/cost
performance of evaluated approaches, Registration
time for devices, Response time for requests, Average
Response size and the Radio duty cycle, as a proxy of
energy consumption, were measured when varying
the number of clients and providers. Registration
(response) time is measured as the time spent from
sending a description (request) until receiving a
confirmation (response), averaged over all successful
registrations (requests). The packet size is measured
at the routing layer and averaged over all sent packets
per node. The network duty cycle, as an indicator of
energy consumption, is measured using Contiki's
power profiler (Dunkels et al., 2011).
Table 3: Simulation configuration parameters.
Parameter Value
Number of nodes 31
Type of nodes Wisemote
Network 300m × 300m
Simulation time 600 secondes
Routing protocol RPL
Transmission range 60 m
RDC / MAC / adaptation
ContikiMAC Null RDC /
OS Contiki 3.0/Cooja
WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems
6.2 Results and Discussion
Figure 5: Performance when varying provided resources
(number of clients = 5).
Figure 6: Performance when varying number of clients
(number of resources = 16).
As we can see in Figure 5.a, the average registration
time of a resource increases with the number of
provided resources in the network. This increase is
more visible in networks deploying RDC. When
comparing the two approaches, SemRD takes more
time in resource registration. This is because SemRD
packet size is bigger than that of RD, due to the
embedded semantic attributes. However, when it
comes to the response size (Figure 5.b), SemRD
showed a clear amelioration, with responses of about
half the size of that of RD, thanks to the embedded
semantics that allowed fine-grained filtering of
responses. This in turn translated into a decrease in
response time (results not showed for space reasons).
Figure 7: Energy consumption (duty cycle).
Figure 6.a and 6.b present the average registration
and response times of the evaluated solutions when
varying the number of clients. These figures show a
similar behavior to that of Figure 5. Indeed, while the
registration time is fairly independent from the
number of clients (Figure 6.a), SemRD registered
higher registration times than RD because of the
additional semantic attributes. This, however, has the
advantage of lowering response times, achieved by
SemRD, because of minimizing their sizes as can be
seen in Figure 6.b. It should be noted, from this
figure, that response times of both SemRD and RD
increase with the number clients.
Finally, Figure 7 presents energy consumption
topography of SemRD and RD when varying both
providers and clients. As can be seen in this figure,
nodes’ duty cycles are equitably balanced (similar
color-heat per node) for both RD and SemRD. The
higher heats (yellow to red) observed in some nodes
can be due to their location (i.e. neighboring the RD
node). Overall, Figure 7 clearly show the load
balancing aspect of both approaches along with the
lower duty cycle (lower energy consumption).
This paper proposed a concrete lightweight mapping
and implementation of our previous semantic model
(Yachir et al., 2016a) in CoAP. This mapping is
achieved by defining appropriate CoRE Link Format
attributes describing both IoT devices/resources and
user requests. An RD-centered framework was also
designed to facilitate IoT resources publication and
retrieving using appropriate user interfaces
communicating through semantic-enhanced CoAP.
Simulation results have shown the performance of
such mechanism when compared with the default RD
Future work consists on more simulations and
testbed experiments to validate the model at a larger
scale. Enhancing the CoAP mapping along with
considering other description formats is also planned.
Currently, the authors are actively working to
integrate, in the proposed semantic model, the
publish/subscribe mechanism.
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