=Paper=
{{Paper
|id=Vol-1955/staf-ds-2017-3
|storemode=property
|title=Action Inferences from IoT Devices: a Risk Detection Case Study Applied in Smart Home
|pdfUrl=https://ceur-ws.org/Vol-1955/staf-ds-2017-3.pdf
|volume=Vol-1955
|authors=Abdullah E. Alhejaili
}}
==Action Inferences from IoT Devices: a Risk Detection Case Study Applied in Smart Home==
https://ceur-ws.org/Vol-1955/staf-ds-2017-3.pdf
Action Inferences from IoT Devices: a Risk
Detection Case Study Applied in Smart Home
Abdullah Alhejaili??
a.e.s.alhejaili@lboro.ac.uk,
Department of Computer Sciences,
Loughborough University, UK
Abstract. Internet of Things (IoT) is a recent hot area in both academic
and industrial interests, which has been proposed to address many ap-
plication domains, including e-health, smart car, smart city, and smart
home etc. While IoT paradigm allows to connect heterogeneous objects
(e.g., sensors and/or devices), understanding their relationships can help
to derive smart actions and useful predictions.
In this research, we focus on action derivation from smart home by
analysing Things relationships and their correlations. We consider fire
risk detection problem as a case study to evaluate different critical as-
pects of the approach, including precision, scalability, performance, and
effectiveness in terms of cost and complexity. We are currently working
on building a simulation tool as a base of the study using some of the
cutting-edge technologies, such as OWL ontology, semantic web services
and data mining algorithms.
Keywords: Internet of Things, Smart Home, Home Automation Sys-
tem, Event Recognition, Risk Detection.
1 Introduction
Internet of Things (IoT) is a promising evolution in the next big wave of web and
internet technologies. Under its vision, everyday objects (such as people, services,
devices, and sensors) need to be interconnected and smartly able to communicate
in a constructive and sensible ways to provide perfect services [12]. Concerning
Smart Home (SH) as one of the hottest IoT application domains [8], different
distributed devices inside and outside home (e.g., light bulb, radiator, cooker,
and tab etc.) should be network-enabled (using, e.g., ZigBee or Wi-Fi) [4]. The
connection of these devices as well as their relationships can be exploited to
support many desire and intelligent services, including energy saving, security
and safety, risk and fire protection.
With respect to the latter (i.e., fire protection service), the UK-government
reports [2,1] that the accuracy of the existing solutions for detecting fire inci-
dences is not good enough. They discover that there is approximately 40% of
??
I’m a full time PhD candidate under the supervision of Dr Shaheen Fatima
(S.S.Fatima@lboro.ac.uk). I have officially started on January 2016, and this is
my second year.
II
false-alarm incidents, attended by fire and rescue services. In addition, their re-
port mentions several key reasons behind fire-alarm failure. One of these reasons
is that the detector has a limited range to cover, resulting in many true-negative
cases (e.g., when a real fire incident occur and the smoke did not reach the de-
tectors). These limitations have motivated us to provide an IoT based solution
that can predict for some specific and potential fire incidences in advance. The
idea is to analyse the behaviour of SH entities, focusing on their relationships,
to infer essential actions.
In this research, we are generally interested in detecting unexpected actions
in smart home by analysing Things relationships and their correlations. We seek
to investigate the derivation techniques of smart actions in principle, and then
practically evaluate their precision, scalability, performance, and effectiveness
in terms of cost and complexity. To this end, we apply our research on SH
domain for detecting fire incidences as a motivating case study. Currently, we
are investigating some of the cutting-edge technologies to adopt, such as OWL
ontology, semantic web services and statistical data mining algorithms.
The remainder of this paper is organised as follows. section 2 presents briefly
work related to event based detection techniques. Sections 3 and 4 respectively
present our research questions and proposal, focusing on SH application domain.
Then, section 5 discusses the current status of our work and the plan for the
next activities. Finally, section 6 concludes the paper.
2 Related Work
Existing event-detection approaches fall roughly into one of the two categories:
ontology-based [11,6] and sensor-based [9,3] approaches. For example, [6] propose
an ontology-based and a rule-based reasoning (so-called SWRL) approaches for
risk detection and/or service decision support in SH management. They de-
veloped a prototype tool that monitors all SH environments, including specific
sensor readings that describers neighbourhood behaviours, for providing real-
time suggestions. Their approach is extendable, i.e., flexible for defining a new
or omitting an existing SH’s entity from the system with no time restriction.
This seems a good feature in general, but applying it frequently could affect
detection’s accuracy. In [3], they suggest a SH with the purpose of promoting
safe environment methods, relaying entirely on wireless sensor networks. One of
the notable features, in their protocol, is the possibility of converting old home
to be smart using sensing techniques.
In SH, different detection activities are typically implemented, each to reach a
specific goal. For instance, a well-SH system should provide healthcare services
by monitoring resident’s movement or body condition. Whiles, other features
such as safety-service would require monitoring different entities in a different
mechanism such as observing daily habits (e.g., cooking) of home residents. [5]
classify broadly user activities for event-detection into four types:
– Single user sequential activities, where a single user performs only one ac-
tivity at a time.
III
– Multi-user sequential and simultaneous activities, where more than one user
perform the same activity, e.g. drinking tea together.
– Multi-user collaborative activities, where multiple users perform different
activities cooperatively to achieve the same gaol, e.g. more than one user are
cooking together.
– Multi-user concurrent activities, where multiple users perform different ac-
tivities independently aiming to different gaols, e.g. one user is watching TV
while the other is cooking.
To our knowledge, there are two closely relevant proposals to ours that con-
sider explicitly fire detection problems [10,7]. These proposed solutions we aware
of are principally fire alarm or video based system. They are mostly devoted to
address different fire accidents based on their real occurrence, but not factually
beforehand. The fundamental techniques behind the current solutions are rang-
ing from conventional devices, relying on, e.g., smoke or temperature measure-
ments, to the high smart image processing solutions, such as flickering colours or
video analysis system. Despite this, the ability of maximising home protections
based on in-advance risk detection has not been addressed yet.
3 Research Hypothesis and Questions
SH supports integrating different devices and systems to be managed by a single
control unit. It allows automating some actions without setting a timer or making
an explicit request. Rather than monitoring or controlling home environment,
SH needs to be more intelligent for generating smart actions, such as detect-
ing unobvious fire risk cases or notifying effectively for future expectations in a
dynamic way. Our main hypothesis is that “following the IoT paradigm by us-
ing the concepts and innovative technologies (such as OWL ontologies, semantic
web services, statistical machine learning algorithms) would make home infras-
tructure more interactive, smart and aware in providing better/robust services”.
To shed lights on our objectives behind this hypothesis, we list two conceptual
questions that may indicate the contributions we seek to make as follows:
– What kinds of smart buildings an action prediction approach like we propose
is useful for, and on which kinds of Thing’s relationships this approach is more
effective?
– What are the technical pros and cons of using IoT paradigm, including, com-
plexity, scalability, performances, and usability?
Generally, these questions can be addressed by building up SH’s simulation
that allows to compare different risk scenarios with the ability of identifying the
key pros and cons of the approach. Having such simulation would also help to
explain whether following the IoT paradigm for smart home risk detection is
beneficial as compared to the other alternative approaches.
IV
4 Proposed Approach and Preliminary Work
To address the research questions of our research, we intend to examine two
well-known techniques: (1) Ontology based structure to represent home model
in a logical way, allowing to design entities, sensors, and their complex relation-
ships. And (2) Machine learning to maximise home services by enabling some
entities controlling automatically another entity without explicitly defining pre-
request for every action. These two methods are to fulfil the main requirements of
building a framework architecture for our research. This architecture would rely
principally on three main components: a workflow simulation to simulate home
entities in terms of generating input/output reading, controlled by a generic in-
terface; a database schema designed by OWL-Ontology for storing all data; and
an adapter to integrate our tool with a machine learning tool for encoding the
recoded data as well as collecting inferred actions.
Fig. 1: Our informal SH model on the top and the defined top level ontology classes with (is-a)
relation between them on the bottom
Progress so far. Fig. 1 describes briefly a suggested model for our SH. It
consists of several connected entities to the local network, and each entity has
a unique id such that its status (e.g., on/off) can be checked. Sensor S3, as an
example, is used to detect the presence of people inside the kitchen. Here, if the
V
Fig. 2: An example of a training data set on the top (consisting of 11 classes and 28 instances), and
generated decision tree from it on the bottom
Cooker is off and nobody insides the kitchen, no risk can be detected unless
something unusual occur, which can be detected by smoke or heat alarm. Even
though it is highly recommended for people to stay in the kitchen while cooking,
sometime people may forget to switch off their cooker before they leave. In re-
sponse to this recommendation, if SH is capable for analysing the relationships
between, e.g., Cooker and S3, optimising fire risk detections in terms of alerting
as early as possible for any expected risk can be achieved. Consequently, house-
holders can take, in advance, further actions, e.g., going back to the kitchen or
switching off the Cooker remotely, etc.
We have tried out to check conceptually the validity of our proposal, using
the extracted dataset, see Fig. 2. In principle, this dataset describes the learning
inputs such that the classes (i.e., defined in Fig. 1) appear as columns, and
each instance, representing reading data, appears as a row. In this preliminary
experiment, we assume that the 28 instances (see, Fig. 2) are already extracted
manually by defining the behaviours of the entities, i.e., modelled in Fig. 1. For
example, (rows from 1 to 21) describe some obvious cases that no risk event
needs to be generated. They cover the cases when the Cooker is either off (rows
1 - 17), or in-use as normal by someone in the kitchen, indicated by S3 (rows
18 - 21). The rest of the rows cover some expected risk cases, including the
absence of householders in the kitchen while the Cooker is ON (rows 22 - 27),
or somebody already in but the utilisation of the Cooker exceeds the normal
average time (row 28). The latter can be a target to fainting cases, especially for
elderly people, in which it increases the level of their protection. Nevertheless,
we intend to develop a home simulation system to generate interactively many
instances for evaluating a variety of different scenarios. The bottom part of Fig. 2
illustrates the result obtained by RandomTree algorithm, graphically visualised
as a decision tree of 7 nodes. This decision tree allows to determine whether a
risk event must be triggered based on the current real-time reading data.
We have conducted another simple experiment on the same dataset (see
Fig. 2), applied by Apriori algorithm, to illustrate a different useful type of
learning output. Listing 1.1 describes the result obtained, which represents asso-
VI
ciation rules between some entities. Such results can help in understating many
or probably all risk cases, but more importantly, it can enhance the learning
decision by avoiding false-positive actions. For example, rule (Cooker=False ==>
Action=no) states explicitly that no action is required if the Cooker is off. Cur-
rently, this action could not be inferred by our dataset as no instance representing
it. However, the association rules can be used as a validation (e.g., testing the
accuracy of the inferred decision tree) by filtering out any false-positive action,
generated imprecisely with the condition (e.g., Cooker==False ).
Listing 1.1: Learning output which describe association rules between entities
1. Cooker=F a l s e => S e n s o r 3=F a l s e . . .
2. Ahmed smartphone=F a l s e => S e n s o r 3=F a l s e . . .
3. d u r a t i o n=none => S e n s o r 3=F a l s e . . .
4. d u r a t i o n=none => Cooker=F a l s e . . .
5. Cooker=F a l s e => d u r a t i o n=none . . .
6. Cooker=F a l s e => A c t i o n=no . . .
7. d u r a t i o n=none => A c t i o n=no ...
8. Cooker=F a l s e d u r a t i o n=none => S e n s o r 3=F a l s e ...
9. S e n s o r 3=F a l s e d u r a t i o n=none => Cooker=F a l s e ...
10. S e n s o r 3=F a l s e Cooker=F a l s e => d u r a t i o n=none ...
5 Research Plan and Current State
Table 1 outlines our research progress as well as the main activities with time
line that are expected to be made by December 2019. The fourth and fifth rows
of the table present the current status of the work.
# Description of task From To
1 Gaining some deep knowledge about IoT concepts and/or approaches to pick up completed
some interesting research questions.
2 Proposing a conceptual solution, for the problem discussed in Sec. 3, that generally completed
relies on the derivation of smart actions by analysing behaviours of IoT devices.
3 Investigating some supporting tools for logical structure and data mining analysis. completed
4 Completing the initial SH model using OWL as a logical database structure, and 80% completed
Java/C# for implementation. We plan to use Protg 5 to design the entire entities of
our suggested SH’s model and their relationships. This would promote extracting
significant actions by relying on SPARQL queries.
5 Developing a discrete-event SH simulation for generating input/output data that 10% completed
describes the behaviour of SH’s entities.
6 Developing an interactive graphical prototype tool that combines both the SH Jul 17 Sep 17
simulation and OWL database schema. This tool should provide a flexible way
to define examples of resident’s behaviours as well as SH’s devices in a workflow
manner, allowing to evaluate different fire risk scenarios.
7 Analysing and evaluating the results (obtained from step #6), and then writing Oct 17 Dec 17
up of a publishable document.
8 Optimising our prototype tool to include some data-mining algorithms for making Jan 18 Mar 18
precise decisions to the cases that are not defined in the workflow. Here, the plan is
to adopt Weka 3.0, which supports an API interface for processing, evaluating and
visualising all learning steps, starting from data preparation step to the analysis
of obtained learning outputs.
9 Analysing and comparing the results with (from row #8) and without (from row Apr 18 Jun 18
#6) using data-mining algorithms to assess their impacts on derived actions.
10 Modifying the first version of our SH model to represent different home character- Jul 18 Sep 18
istics. This is to evaluate the impact of applying our approach on different kinds
of buildings.
11 Surveying the existing non-IoT related approaches to give impression on how much Oct 18 Dec 18
adopting IoT paradigm for SH risk detection is beneficial.
12 Thesis writing up Jan 19 Dec 19
Table 1: Research progress and expected activities
VII
6 Conclusion
In consonance with computable existing fire-protection solutions, our IoT ap-
proach will be conceptually modelled to be complementary to the recent ad-
dressable risk detection devices for validation and evaluation only. Therefore, to
generalise the main contributions of this approach, we will investigate empirically
how our derivation technique of unexpected/smart actions can be customised to
suit different IoT applications and specifications.
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