a study on retrieval method in internet of...
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A Study on Retrieval Method in Internet of Things
Sungrim Kim1
, Joonhee Kwon2
1
Department of Internet Information, Seoil University,
28, Yongmasan-ro 90gil, Jungnang-gu, Seoul Korea
2
Department of Computer Science, Kyonggi University,
San 94-6, Yiui-dong, Yeongtong-ku, Suwon-si, Kyonggi-do, Korea
kwonjh@ kyonggi.ac.kr
Abstract. The Internet of Things is increasingly attracting attention of
academic and industry researchers. Information retrieval method helps users to
quickly find relevant information from lots of data. However, it requires a new
retrieval method considering the characteristics of Internet of Things
environment. This paper presents the new retrieval method in Internet of Things
environment. Our method adopts the social relationship in Internet of Things. It
considers not only relationships between human beings but also them between
human beings and things, things and things. We propose a levelized tiered-
things structure and algorithm based on the social relationship among objects.
Our method improves performance of top-k retrieval by adjusting the value of k
according to a user’s attention level.
Keywords: Internet of Things, retrieval method, tiered-things structure
1 Introduction
The Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the
scenario of modern wireless communications. The basic idea of IoT is the pervasive
presence around us of a variety of things or objects [1]. The retrieval methods are
capable of helping user to easily find information among a large amount of
information by determining what is of attention to a user [2]. It is important to
incorporate the IoT characteristics into the retrieval process. However, the traditional
retrieval methods have not taken into account the IoT characteristics while making
retrieval. As IoT is expanding, there have been only few researches on retrieval
method in IoT. Another new IoT retrieval method researches are necessary.
We design a new retrieval method in IoT environment. We adopt the tiered-things
structure based on social relationship among objects. We propose a levelized tiered-
things structure, where the structure is an enhanced structure from [3]. Our method
improves performance of top-k retrieval by adjusting the value of k according to a
user’s attention level. When a user finds top-k documents while paying little attention,
only some top documents of them are retrieved, not all of them.
The remainder of the paper is organized as follows. First, in Section 2, we describe
background and related researches about retrieval method and IoT. In Section 3, we
Advanced Science and Technology Letters Vol.142 (SIT 2016), pp.88-91
http://dx.doi.org/10.14257/astl.2016.142.16
ISSN: 2287-1233 ASTL Copyright © 2016 SERSC
explain our proposed retrieval method in IoT environment. Finally, section 4 will
conclude the paper.
2 Background and Related Works
The IoT is the internetworking of physical devices, and network connectivity that
enable these objects to collect and exchange data. It refers to a world where physical
objects and beings, as well as virtual data and environments, all interact with each
other in the same space and time [1],[4],[5]. Recently, new applications and research
challenges in numerous areas of IoT are getting started.
The idea that the convergence of the IoT and the Social Networks worlds is
possible, or even advisable, is gaining momentum. Social IoT (SIoT) combines the
IoT with the social networks. Social networks essentially consist of a representation
of each user, his/her social links, and a variety of additional services. SIoT has social
relationships between human beings and things, things and things, things and their
owners. In SIoT, there are five social relationships - Parental object relationship
(POR), Co-location object relationship(C-LOR), Co-work object relationship (C-
WOR), Ownership object relationship (OOR) and Social object relationship (SOR)
[6],[7].
The traditional retrieval methods concern themselves with documents, queries, and
their relations to each other. Tiered index based method is known as efficient top-k
retrieval method. When using tiered indexes, we search for a document in the first
tier. If we fail to get k results from it, search falls back to tier2, and so on[8].
3 Retrieval Method in Internet of Things
This section presents an enhanced information retrieval method in social Internet of
Things environment. We adopt the tiered-things structure of [3], where the structure is
based on social relationship between objects.
Our method improves performance of top-k retrieval by adjusting the value of k
according to a user’s attention level. When a user finds top-k results while paying
little attention, only some of more important and relevant results are retrieved, not all
of them.
Our adjusted value of k is denoted by k(i). In Equation (1), k(i) is the levelized k
value, when current level value is i and the maximum number of level values is l. In
our levelizing scheme, the degree of attention is denoted by a level value. The higher
the attention is, the higher the level value is.
k(i) = k/l * i (1)
We propose a levelized tiered-things structure, where the structure is an enhanced
structure from [3]. The tiered-things are based on social relation value, SR. In this
paper, a level dictionary is proposed. The level dictionary is composed of
Advanced Science and Technology Letters Vol.142 (SIT 2016)
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‘LevelValue’ and ‘MaxTier’, where the former is current level value and the latter is
maximum tier number for this level value.
Table 1 shows our retrieval algorithm using the levelized tiered-things structure.
When a user is in level ‘L’, our method finds documents owned by things in tier 1.
When the size of the result set is less than k(L), query processing falls back to tier 2,
and maximum tier number for level value L in level dictionary.
Table 1. Retrieval algorithm using levelized tiered-things structure
Algorithm.
Begin
Input
Q : query
Tiered-Things : tiered things
k : the number of result to be retrieved
LD : Level Dictionary
L : number of level
Output
ResultSet : top-k result document set
Method
tier = 1; MaxLevel = maximum value of LevelValue in LD;
ResultSet = { };
while (tier <= LD[L].MaxTier)
{
for (each j ∈ Tiered-Thingsx )
ResultSet = ResultSet ∪ resultSetj(Q);
if ( | ResultSet | < k/MaxLevel*L )
tier ++;
else
break;
}
return topK(ResultSet);
End.
4 Conclusion
The IoT is a concept that encompasses various technologies. The IoT links objects
and/or people anytime, anywhere. Retrieval method is the activity of obtaining
resources relevant to an information need from a collection of resources. The IoT is a
new promising technology made from a variety of technology, brining changes in
retrieval method. However, the existing retrieval method does not guarantee good
results in IoT environment, because it does not consider IoT characteristics.
This paper presents the new retrieval method in IoT. Firstly, we propose a
levelized tiered-things structure based on the social relationship among objects. The
social relationship is one of the most important characteristics of IoT. The tiered-
things structure is known as efficient top-k retrieval method. By integrating them, our
Advanced Science and Technology Letters Vol.142 (SIT 2016)
90 Copyright © 2016 SERSC
structure enables users to get relevant information more efficiently. Secondly, we
propose an algorithm using the structure. When users find top-k results, our algorithm
adjusts the value of k according to a user’s attention level.
When a user finds top-k results while paying little attention, only some of more
important and relevant results are retrieved, not all of them. It improves performance
of top-k retrieval in IoT environment.
References
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Copyright © 2016 SERSC 91