=Paper=
{{Paper
|id=Vol-1491/paper_16
|storemode=property
|title=Multi-level Context Adaptation in the Web of Things
|pdfUrl=https://ceur-ws.org/Vol-1491/paper_16.pdf
|volume=Vol-1491
|dblpUrl=https://dblp.org/rec/conf/semweb/Terdjimi15
}}
==Multi-level Context Adaptation in the Web of Things==
https://ceur-ws.org/Vol-1491/paper_16.pdf
Multi-level context adaptation in the Web of
Things
Mehdi Terdjimi
Université de Lyon, LIRIS
Université Lyon 1 - CNRS UMR5205
F-69622, France
mehdi.terdjimi@liris.cnrs.fr
Abstract. The Web of Things (WoT) aims at connecting things to ap-
plications using web technologies, on top of the Internet of Things. WoT
applications are distributed and gather different levels of abstraction.
They must be scalable and adapt to dynamic changes in their environ-
ment. The question we explore in the scope of my PhD thesis is: how
can we deal with context in WoT applications? Our objective is to enable
scalable multi-level context-aware adaptation for WoT applications. We
intend to build models to describe context and reason about it. First,
we have studied related work to identify a set of contextual levels and
dimensions and have proposed semantic models suitable for several adap-
tation tasks in WoT applications. Second, we designed and implemented
an architecture that distributes some adaptation tasks onto the client
side, to improve reasoning scalability.
1 Introduction
The Internet of Things (IoT) aims at connecting devices (i.e. sensors and actua-
tors) to the Internet, to share information using various protocols. The Web Of
Things (WoT) builds upon the IoT, where connected devices (“things”) rely on
Web technologies and standards to break through silos and allow interoperability
in pervasive applications [15], e.g. by making use of semantics.
In ubiquitous computing, context highly impacts application behavior and is
composed of various pieces of information. Our research question is: how can we
deal with context in WoT applications? And subsequently, how can we model
context in a generic yet efficient way: on one hand, to harvest contextual data
from different sources and build coherent and reliable models? On the other
hand, how to actually perform the adaptation process for different purposes,
required by WoT applications? To answer these questions, we propose in this
thesis the concept of multi-level adaptation.
1.1 Context of the work: the ASAWoO project
In our work as part of the ASAWoO project1 , we propose the concept of avatar
to augment and represent physical objects in the virtual world. The avatar of a
1
http://liris.cnrs.fr/asawoo/
physical objects exposes its functionalities as RESTful resources. In some cases,
some features of the object might be disabled for technical reasons such as phys-
ical constraints, or due to security, privacy, or other policies. The decision to
make features available or not depends on context. To enable adaptation of con-
nected devices to the changes affecting them, appropriate context modeling is
required.
1.2 Challenges and motivations
In this work, we aim at using Web standards, such as service sharing protocols
(through REST architectural style) and semantic Web technologies (RDF, OWL,
SPARQL). The current challenges are:
1. To design generic, semantically-annotated context models for WoT applica-
tions, based on the state of the art, to allow context reasoning and adapta-
tion. This will allow interoperability among heterogeneous contextual data
sources, such as sensors of the object, external information gathered through
Web services and application domain knowledge.
2. To enable multi-level adaptation, as WoT applications cover multiple ab-
stractions, domains and needs. The adaptation would be realized by an ap-
propriate engine, designed in two parts. The first part would be in charge of
the extraction of relevant information (depending on the chosen adaptation
type). The second part would be the adaptation engine itself.
3. To provide a scalable adaptation engine, in view of the increasing number
of heterogeneous connected devices. A modularization of the reasoning steps
would allow their distribution between the avatar and its clients. Each step
could be executed on the client if its computing resources are sufficient.
In Section 2, we propose a state of the art on context modeling, followed by
a state of the art on mobile/client reasoning. We propose in Section 3 a generic,
flexible and scalable context model that constitutes our approach. A multi-level
semantic adaptation process is presented and evaluated, with a method that
locates the reasoning steps between the client and the WoT infrastructure in
Section 4. In Section 5, we discuss the results and give the perspectives and
future work.
2 Related work
In this section, we overview related work in the field of context modeling to help
us build context models. We also study related work in the domain of mobile
reasoning to help us build our adaptation solution.
2.1 Context modeling
Former definitions of context are generic and define several dimensions such as
Location, Environment, Time, Activity, and User (Schilit and Theimer [26], Pas-
coe [21], Dey [10], Schmidt [27]). Context models used in IoT applications are
close to the former, but adapted to particular needs [22]. Some of them focus
on physical aspects by using context dimensions related to the presence, the
activity and the state of entities (i.e. people and devices) in some location, at
a certain time [25, 11, 27, 35, 8, 1]. Some works use network context to provide
efficient routing and disruption-tolerance [13, 18, 20, 32, 23]. In the field of social
computing, context models have been designed for different purposes. To facil-
itate user interactions with the application [6, 33], or to improve organization
within multi-agent systems [4, 3, 2, 5]. There are also works that separate ap-
plication architecture information and business logic [16, 7, 19, 12], or the device
and its physical properties [31, 30]. Another popular usage of context is related to
content adaptation, which could be media [34] or, more recently, linked data [9].
The designed context models are specific to the application. But none of them
completely rely on the Web, nor perform adaptation on multiple abstraction
levels. Thus, as far as we know, there is no multi-level context model for WoT
applications.
2.2 Mobile reasoning
When it comes to reason about context to perform adaptation, client-side rea-
soning may be a solution to address scalability concerns that arise with high
numbers of simultaneous requests. But even if client processing resources aug-
ment at a fast pace, they remain heterogeneous and in some cases, too limited to
execute heavy calculation processes. Thus, adaptivity and flexibility depending
on the client’s resources are necessary.
The following approaches aim at optimizing the reasoning process for resource-
constrained devices. Different ways have been envisioned, from axiom-template
rewriting (Kollia and Glimm [17]), to the Triple Pattern Fragments approach
(TPF) which relies on intelligent clients that query TPF servers to address the
problem of scalability and availability of SPARQL endpoints. However, the use
of a server is always necessary. Concerning mobile reasoners, some are based on
first-order logic (FOL) (KRHyper [28]), or description logics (DL) (Mine-ME
2.0 [24], EL+ Embedded Reasoner [14]). But KRHyper is not designed for DL,
and [24, 14] do not provide a web client access. An approach to embed a rea-
soner in mobile devices is to rely on web standards and run it in a web browser
in Javascript. EYE2 is a Node.js3 -compatible reasoner, limited to FOL, which
only runs on the server-side. Based on the JSW Toolkit, OWLReasoner4 allows
client-side processing of SPARQL queries on OWL 2 EL ontologies. As far as we
know, it is the only full-Javascript OWL 2 EL reasoner that can be used offline
in a web client, though its SPARQL engine is limited to basic rule assertions.
2
http://reasoning.restdesc.org/
3
https://nodejs.org/
4
https://code.google.com/p/owlreasoner/
3 Approach
Our approach is based on the research questions raised in Section 1. First we plan
to model each abstraction level based on the dimensions identified in the state
of the art. As our context model will be described in an ontology, we consider
an abstraction as a context state corresponding to a graph part. An example
is depicted in Figure 1. In this example, the question “Which communication
protocols can be used?” will be queried on the graph which contains the state
(Location: home, Security: Level 1, Time: Evening). In some cases, a di-
mension has no use in the abstraction (e.g. the Gender in the Communication
level). Secondly, we plan to model the context of the WoT application state, in
order to provide adaptation of the reasoning task. To do this, we evaluate our
implementation in Section 4 to identify which parameters to model.
Fig. 1. Example illustrating our approach
The overall method is: 1) to generate graphs from the context models, 2)
to query these graphs in SPARQL, in order to retrieve each possibility given a
context state, and 3) to add a rule engine that drives the reasoning process. Each
step would be separated, to be deferred in the client side. Thus, we propose in
the next Section an implementation to reach these goals.
4 Implementation and evaluation
We implemented an architecture that allows the modularization of the reasoning
steps and client-side code migration. We proposed in [29] the HyLAR prototype5
(for Hybrid Location-Agnostic Reasoning), which is a lightweight, modular and
adaptive architecture developed in Javascript for hybrid client/server side rea-
soning, based on OWLReasoner. It allows client-side processing of SPARQL
queries on OWL 2 EL ontologies, and consists in the separation of JSW modules
that perform ontology classification (JSW Classifier), ontology and SPARQL
query parsing (JSW Parser) and reasoning (JSW Reasoner)6 . JSW modules are
5
The prototype is available at http://dataconf.liris.cnrs.fr/owlReasoner/
6
Originally, OWLReasoner rewrites the aBox into a relational database and SPARQL
queries into SQL queries. This choice is not ours and will not be discussed here.
packaged as Node.js modules and AngularJS7 services. This way, they can be
executed on either the server or client. On the client side, the reasoner modules
can be embedded either in a regular angular service, or in a web worker. As
the main service is totally agnostic about the location of the reasoning modules,
security and privacy concerns arise, but these aspects are subject to future work.
Fig. 2. OWLReasoner’s reasoning steps
The goal of the evaluation we propose in [29] is to identify the parameters
affecting the reasoning task’s processing time, and to find an optimal config-
uration. We calculated the overall reasoning process request-response times in
three situations: full server-side, full client-side and hybrid (server-side parsing
and classification, and client-side query processing). For the latter two variants,
client-side parts are evaluated both with and without web worker. We assume
that scripts and ontologies are available on the server. Each scenario is evaluated
with these initial parameters and tools:
– Ontology sizes: ontology A has 1801 class assertions and 924 object property
assertions, and B has 12621 class and no object property assertions)8 . These
datasets are conference keywords gathered from DataConf9 .
– Network status: requesting locally or in high latency conditions (around
150ms, using Clumsy 0.210 ).
– Client capabilities: a Dell Inspiron (i7-2670QM CPU @ 2.20GHz, which also
hosts the Node.js server), a Nokia Lumia 1320 (Snapdragon S4 @ 1700 MHz)
and a Samsung Galaxy Note (ARM cortex A9 Dual-Core @ 1,4 GHz).
In Tables 1 and 2, [Q] is the time for the client’s request to reach the server,
[P] is the processing time and [R] is the time for the server response to reach the
client. Some parts of these steps/patterns are considered immediate and noted
in the result tables as not applicable.
7
http://www.angularjs.org
8
Due to OWLReasoner query engine limitations that does not currently allow query-
ing individuals nor data property assertions, our evaluations are limited to class and
object property assertions.
9
http://dataconf.liris.cnrs.fr/
10
http://jagt.github.io/clumsy/
Ontologies A / B [R0] [Q1] [R1] [Q2] [R2] [Q3] [R3]
Remote server 334 54 110 / 275 119 / 120 167 / 647 146 / 154 61 / 85
Table 1. Network delays (in ms)
Ontologies A / B [P2] (no worker) [P2] (worker) [P3] (no worker) [P3] (worker)
Inspiron (Chrome) 790 / 27612 764 / 26464 28 / 101 24 / 88
Lumia (IE) 1989 / 54702 1883 / 53801 156 / 198 144 / 185
Galaxy Note (Firefox) 2954 / 81255 2872 / 79752 465 / 2988 440 / 2872
Server (Node.js) 780 / 20972 n/a 35 / 37 n/a
Table 2. Classification [P2] and reasoning [P3] times (in ms)
5 Discussion and future directions
5.1 Analysis of evaluation results
As expected, Table 2 shows that the server has the best results for the classifi-
cation processing time and can use caching. Even if the raw ontology is faster to
load than the classification results, loading scripts and data on the client is much
faster than performing the same classification step on each client. Thus, it makes
no sense to migrate heavy calculations onto clients, rather than pre-calculating
them on the server and caching results. More generally, for M clients and N
queries/client, we calculate each configuration calculation times as follows11 :
– Full server-side: P 2server + M × N × (Q3 + P 3server + R3)
We group network (Q, R) and application (P) statuses as the full process is
server-side.
– Full client-side: M × (R0 + Q1 + R1) + P 2client + N × P 3client
Q, R and P fully depend on the client. They are therefore difficult to estimate
in comparison to the full-server configuration.
– Hybrid: P 2server + M × (R0 + Q2 + R2) + N × P 3client
Client P estimation is easier as it only concerns the query-answering process.
We identify the following parameters affecting the reasoning task: the number
of clients (M) and queries per client (N), the network status (Q and R), and the
ontology size and computing resources (P).
5.2 Lessons learned and future directions
There is no optimal configuration, and choosing a location for each step is a
complex task as it is context-dependent. In our evaluation, we identified differ-
ent parameters that affect processing times. They are crucial for the adaptation
process and to respond to our research questions: how can we model the con-
text 1) to allow the inclusion of the context elements needed by the application
and 2) to perform the adaptation process for different purposes, specific to WoT
applications? Our future work includes taking users’ privacy into account, and
11
Server-side classification (performed once and then cached) and client-side calcula-
tions (performed in parallel) are only counted once.
describing our context model in an ontology. Graphs corresponding to a partic-
ular context state would be generated and queried. We plan to reuse an existing
adaptation engine that suits our needs for the reasoning process.
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