=Paper=
{{Paper
|id=Vol-2400/paper-32
|storemode=property
|title=Investigating the Scope of a Thing in a Multiple Internet of Things Scenario
|pdfUrl=https://ceur-ws.org/Vol-2400/paper-32.pdf
|volume=Vol-2400
|authors=Francesco Cauteruccio,Luca Cinelli,Giorgio Terracina,Domenico Ursino,Luca Virgili
|dblpUrl=https://dblp.org/rec/conf/sebd/CauteruccioCTUV19
}}
==Investigating the Scope of a Thing in a Multiple Internet of Things Scenario==
https://ceur-ws.org/Vol-2400/paper-32.pdf
Investigating the scope of a thing in a multiple
Internet of Things scenario
Francesco Cauteruccio1 , Luca Cinelli1 , Giorgio Terracina1 , Domenico Ursino2 ,
and Luca Virgili2
1
DEMACS, University of Calabria
2
DII, Polytechnic University of Marche
Abstract. In this paper, we investigate the scope of a thing in a multiple
IoT scenario. First we introduce the concept of scope in general, and we
illustrate how it has been investigated and applied in social networking.
Then, we define the scope of a thing in a Multi-IoT scenario, modeled
as an extension of a Social Internetworking System, and we propose a
formalization of scope allowing the computation of its values. Finally, we
present a possible application of scope and describe some tests that we
performed for evaluation purposes.
Keywords: Scope; Social Object; Internet of Things; Multiple IoTs; Trust
Degree; Neighborhood of a Thing
1 Introduction
In the Concise Oxford Dictionary [1], scope is defined as “the extent of the area
or subject matter that something deals with or to which it is relevant”. Scope has
certainly some similitudes with several other concepts investigated in sociology,
such as influence, power, centrality, impact, reliability, and so forth. However, it
goes beyond each of these terms and, at the same time, summarizes all of them
and is influenced by each of them.
In the past, scope has been studied in the context of social networks. For in-
stance, in [12], the authors investigate the scope of users and hashtags in Twitter,
whereas in [10, 13–15, 18, 19], the authors propose approaches to computing some
aspects of scope (e.g., influence, trust, reliability) for users and/or hashtags. In
the meantime, the social network scenario has become increasingly complex and
we have passed from social networking to social internetworking [17, 7]. In this
last case, several social networks simultaneously coexist and cooperate through
specific users, called bridges, who join more social networks.
Parallel to the transition from social networking to social internetworking, in
the last few years, we are experiencing the presence of objects that are becoming
Copyright c 2019 for the individual papers by the papers authors. Copying permit-
ted for private and academic purposes. This volume is published and copyrighted by
its editors. SEBD 2019, June 16-19, 2019, Castiglione della Pescaia, Italy.
increasingly smart and social. This phenomenon is revolutionizing the Internet of
Things. As a proof of this, more and more authors are starting to study the be-
havior of things, to talk about their profiles and their social interactions. In fact,
different architectures implementing these ideas are frequently proposed in the
current literature. Social Internet of Things (hereafter, SIoT [4]), Multiple IoT
Environment (hereafter, MIE [5]) and Multiple Internets of Things (hereafter,
MIoT [6]) are only three of the latest architectures with these characteristics.
In particular, MIoT is the latest of them and, therefore, takes into account the
most recent results obtained in the IoT research. A MIoT can be seen as a set of
Internets of Things interacting with each other through specific objects, called
“cross objects”, that belong to more IoTs. The MIoT paradigm represents the
extension of the ideas underlying social internetworking to the IoT.
In spite of this enormous interest that increasingly numerous researchers are
posing on the IoT, to the best of our knowledge, no analysis about the scope of
a thing in a Multi-IoT, or at least in an IoT, scenario has been presented yet.
Certainly, several aspects someway related to the scope have been considered in
the IoT or, in limited cases, in the SIoT scenario. As an example, in [16], the
authors investigate information diffusion in a narrowband IoT with the goal of
optimizing information flow at network level. In [3], the authors investigate the
adoption of context-aware information diffusion to alert messages in 5G mobile
social networks. Context-aware information is collected from different devices
deployed in the environment. An interesting approach to content dissemination
in the Internet of Vehicles (IoV) is described in [21]. Here, the authors analyze
how to combine the information coming from the physical layer with the one
regarding the social layer to perform a rapid content dissemination in IoV net-
works. In [11], the authors present an IoT application in the context of smart
cities, a scenario in which an IoT system can reach large scale dimensions. [11]
also introduces the concept of IoT hub. This aggregates the information coming
from related devices and, therefore, contributes to improve the interoperability
between things in a urban-scale IoT system. Furthermore, different approaches
on recommender systems and services in IoT have been proposed in the litera-
ture; an overview of them is presented in [8]. In particular, in [9], the authors
propose a multi-agent recommender system for the IoT aiming at producing
a set of significant suggestions for a user with specific characteristics. Here,
things are represented through bit vectors, called thing descriptors, managed
by cyber-agents. Things can be linked together and, then, can be managed by
neighbor cyber-agents. In [20], the authors propose an approach that integrates
the concept of social networks of users and Internet of Things. It merges infor-
mation coming from social networks of users and correlation networks of things
by learning shared latent factors. To perform this task, it exploits a technique
for probabilistic matrix factorization.
All the approaches mentioned above are extremely interesting; furthermore,
several other related approaches proposed in the past to evaluate the relevance
or the impact of a node in the Internet of Things could be mentioned. However,
none of them has been conceived to operate in a complex scenario consisting of
multiple IoTs, which interact with each other through smart and social objects
that simultaneously belong to more of them and act as “bridges”.
In this paper, we aim at providing a contribution in this setting by proposing
an approach to computing the scope of a thing in a MIoT. Specifically, after
having provided an overview on the MIoT paradigm (Section 2), we present a
definition of scope along with a formalization allowing the computation of the
corresponding values (Section 3). Then, we illustrate an application of scope in
the context of smart cities, where it can play a key role (Section 4). Thereafter, we
present some tests conceived to understand its main features (Section 5). Finally,
we draw our conclusions and have a look at some possible future developments
of our ideas (Section 6).
2 The MIoT paradigm
In this section, we provide an overview of the MIoT paradigm, described in detail
in [6]. A MIoT M consists of a set of m Internets of Things. Formally speaking:
M = {I1 , I2 , · · · , Im }
where Ik is an IoT.
Let oj be an object of M. We assume that, if oj belongs to Ik , it has an
instance ιjk , representing it in Ik . The instance ιjk consists of a virtual view
(or, better, a virtual agent) representing oj in Ik . For example, it provides all
the other instances of Ik , and the users who interact with this IoT, with all
the necessary information about oj . Information stored in ιjk is represented
according to the format and the conventions adopted in Ik .
A MIoT M can be also represented by means of a graph-based notation. In
particular, a graph Gk = hNk , Ak i can be associated with an IoT Ik of M. In
this case:
– Nk is the set of the nodes of Gk ; there is a node njk for each instance ιjk ∈ Ik ,
and vice versa. Since there is a biunivocal correspondence between a node and
an instance, in the following we shall use these two terms interchangeably.
– Ak is the set of the arcs of Gk ; there is an arc ajqk = (njk , nqk ) if there exists
a link from njk to nqk . A label can be associated with ajqk ; it stores the set
tranSetjqk of the transactions performed from ιjk to ιqk in the past.
Finally:
M = hN, Ai
Here:
Sm
– N = k=1 Nk ; Sm
– A = AI ∪ AC , where AI = k=1 Ak and AC = {(njk , njq )|njk ∈ Nk , njq ∈
Nq , k 6= q}.
AI is the set of the inner arcs (hereafter, i-arcs) of M; they link instances
(of different objects) belonging to the same IoT. AC is the set of the cross arcs
(hereafter, c-arcs) of M; they link instances of the same object belonging to
different IoTs. A node connected to at least one c-arc is called c-node; otherwise,
it is called i-node.
In M, an object oj has associated a set M Dj of metadata. Our metadata
model refers to the one of the IPSO (Internet Protocol for Smart Objects) Al-
liance [2]. Specifically M Dj consists of three subsets, namely: (i) M DjD , i.e., the
set of descriptive metadata; (ii) M DjT , i.e., the set of technical metadata; (iii)
M DjB , i.e., the set of behavioral metadata. All details about these metadata can
be found in [6].
Now, we can define the set tranSetjk of the transactions activated by ιjk in
Ik . Specifically, let ι1k , ι2k , · · · , ιwk be all the instances belonging to Ik . Then:
[
tranSetjk = tranSetjqk
q=1..w,q6=j
In other words, tranSetjk is given by the union of the sets of the transactions
from ιjk to all the other instances of Ik .
3 Scope definition
In this section, we present the definition of the scope of an instance ιjk in an
IoT Ik and of the scope of an object oj in a MIoT. For this purpose, we must
introduce some preliminary concepts.
The first of them is the one of neighborhood of an instance ιjk in Ik . Specif-
ically, the neighborhood nbh(ιjk ) of ιjk is defined as:
nbhjk = out nbhjk ∪ in nbhjk
where:
out nbhjk = {nqk |(njk , nqk ) ∈ AI , |tranSetjqk | > 0}
and
in nbhjk = {nqk |(nqk , njk ) ∈ AI , |tranSetqjk | > 0}
In other words, nbhjk comprises those instances directly connected to ιjk
through an incoming or an outgoing arc, which shared at least one transaction
with ιjk . In the following of this paper, we are interested only to out nbhjk ; as
a consequence, when we will use the term “neighborhood”, we will implicitly
mean “out neighborhood”.
Now, we need to introduce the neighborhood of level t of an instance ιjk in its
IoT Ik . It is an extension of the concept of out nbhjk and is defined as follows:
out nbhtjk =
out nbhjk if t = 0
{ιrk |ιrk ∈ out nbhqk , ιqk ∈ out nbht−1
jk , ιrk ∈
6 out nbh w
jk , 0 ≤ w < t} if t>0
The concept of out nbhtjk will be extremely important later. In the mean-
time, we introduce a new concept, namely the one of minimum path πjqk from
an instance ιjk to an instance ιqk ∈ nbhtjk . πjqk is defined as the succession
{ι0k , ι1k , . . . , ιtk } of instances such that ι0k = ιjk , ιtk = ιqk , ιwk ∈ out nbh(w−1)k
for 1 ≤ w ≤ t.
After this, we must introduce the definition of the Trust Degree T Dqjk of an
instance ιqk in the instance ιjk in Ik . It can be defined as the fraction of the
transactions sent by ιjk to ιqk that have been requested by ιqk or that ιqk did
not request but it has considered so interesting to repost or to elaborate them3 .
In order to formalize T Dqjk , we must introduce:
– the set repostedqk of the transactions received by ιqk of Ik and reposted by
it;
– the set elaboratedqk of the transactions received by ιqk whose contents it
elaborated for its purposes;
– the set requestedqk of the transactions explicitly requested by ιqk .
If ιqk belongs to out nbhjk , T Dqjk can be expressed as:
|tranSetjqk ∩ (requestedqk ∪ repostedqk ∪ elaboratedqk )|
T Dqjk =
|tranSetjqk |
Starting from this definition and from the concept of out nbhtjk , we can pro-
ceed with the transitive closure of T Dqjk in such a way as to extend it to the case
in which ιqk is indirectly connected to ιjk . In particular, the general definition
of T Dqjk is as follows:
|tranSetjq ∩(requestedq ∪repostedq ∪elaboratedq )|
k k k k
|tranSetjq |
if ιqk ∈ out nbhjk
k
T Dqjk = Qt
T D if ιqk ∈ out nbhtj , t > 0,
w=1 ((w−1)w)k k
πjqk = {ι0k , ι1k , · · · , ιtk }
The next step regards the definition of the concept of Impact Degree IDjk
of an instance ιjk in Ik . It is defined as the average of the Trust Degrees that all
the instances belonging to out nbhjk have in ιjk . It can be formalized as follows:
P
ιqk ∈out nbhjk T Dqjk
IDjk =
|out nbhjk |
We are now able to define the Scope Sctjk of level t of an instance ιjk of Ik .
Specifically, Sctjk is defined as the weighted sum of the Impact Degrees of the
3
Clearly, it might happen that an unrequested transaction of tranSetjqk is not con-
sidered interesting by ιqk . In this case, ιqk neither posts nor elaborates it.
instances belonging to out nbhtjk , where the weights are the trust values that
these instances have in ιjk . This sum is then averaged by the number of the
instances belonging to nbhtjk . Formally speaking:
P
ιqk ∈out nbhtj T Dqjk · IDqk
Sctjk = k
|out nbhtjk |
Now, we can define the Scope Sctj of level t of an object oj in the MIoT. It is
obtained by averaging the Scope of level t of its instances in the corresponding
IoTs. Specifically, let Instj = {ιj1 , ιj2 , · · · , ιjl } be the instances of oj in the IoTs
of the MIoT. Then:
t
P
t ιjk ∈Instj Scjk
Scj =
|Instj |
From the definitions of T Dqjk , IDqjk , Sctjk and Sctj it emerges that each of
these parameters belongs to the real interval [0, 1].
4 Applications
In a scenario characterized by the pervasive diffusion of increasingly intelligent
and social objects, our approach can have a large variety of applications. Two
very interesting ones regard smart cities and shopping centers. Due to space
limitations, we describe in detail only the first one.
Consider some public areas (such as parks, squares, shopping centers, etc.) in
a (smart) city, and assume that a set of people actively visits these areas. Each
area is equipped with several smart objects for monitoring weather, air quality,
traffic conditions, level of noise, etc., along with several actuators, such as smart
lamps or information hubs provided as online services. Each person may be
provided with several smart devices, such as smartwatches, smartphones, other
wearable devices, and so forth. Persons and places can interact with each other
through the corresponding smart objects.
Such a scenario can be modeled through a MIoT M consisting of a set
{I1 , I2 , · · · , Im } of IoTs, each representing a public area. The set of the objects
of M comprises the smart objects installed in the public areas and the set of
personal devices of people visiting them. If an object oj of the MIoT is active in
the k th public area, it has an instance ιjk in the IoT Ik . Clearly, when a person
with a smart object oj moves around different public areas corresponding to
different IoTs, oj will have different instances, one for each IoT.
Each visitor of an area is generally interested to a certain kind of activity; for
instance, she could be a fitness runner. The final goal of the MIoT is supporting
people to get the best experience from their activities. In this setting, scope can
play a role in reaching this objective. In the following, we report some possible
usage scenarios.
Assume that a person wants to go out for a run. First, she needs to choose the
best area for the run, based on weather conditions, traffic and other parameters
that she considers relevant. To carry out her choices, she can check data provided
by the sensors of each public area of her interest, the information hubs or other
trusted runners. The choice of the data sources to consult is usually related to
the corresponding trustworthiness and the easiness of getting desired information
from them. These two properties are clearly strictly correlated to the scope of the
source; indeed, this scope can be seen as a “summary” of these two parameters
and some other related ones, such as accuracy, reputation, impact, etc. Once the
person has performed her choice, she can send information to the MIoT in such
a way as to serve, in her turn, as information provider for the community.
A similar activity flow may happen in several other circumstances, in which
there is a decision to made, e.g., when a user must choose the best shopping
center where she can buy a given object, the best cinema where she can see a
movie, etc.
In all these cases, data regarding user choices can be coupled with those
registered during the activities she performed therein (e.g., data coming from
personal smart wears) in such a way as to confirm the correctness of the choice
or, on the contrary, to alert the other users of the evaluation errors. For instance,
imagine a scenario in which a person verifies that the weather was actually too
cold for the clothes she had selected; interestingly, this information could be
automatically detected and sent by the sensors in her smart wears. In this case,
the scope of the smart wears is useful to understand how extended and how
strong is its capability of influencing the decision of the other users. In other
words, the scope of an object oj in this scenario determines how many users are
impacted by the data sent by it and how much strong this impact is.
It is worth pointing out the relevance of scope in this context. As a matter
of fact, knowing the objects with the highest impact in the MIoT allows the im-
provement of the efficiency and the effectiveness of the information disseminated
through the network. At a higher abstraction level, some smart objects of the
MIoT could assume the role of reliable information hubs for the whole MIoT if
their scope is particularly strong and large over time.
5 Experiments
In order to perform our experiments, since real MIoTs with the dimension and
the variety handled by our model do not exist yet, we constructed a MIoT
simulator. This tool starts from real data and returns simulated MIoTs with
certain characteristics specified by the user.
The MIoTs created by our simulator follow the paradigm described in Section
2. Our simulator is also provided with a suitable interface allowing a user to
“personalize” the MIoT to build by specifying the desired values for several
parameters, such as the number of nodes, the maximum number of instances of
an object, and so forth.
To make “concrete” and “plausible” the created MIoTs, our simulator lever-
ages a real dataset. It regards the taxi routes in the city of Porto from July 1st
2013 to June 30th 2014. It can be found at the address http://www.geolink.pt/
ecmlpkdd2015-challenge/dataset.html. Each route contains several Points of
Interests corresponding to the GPS coordinates of the vehicle.
We partitioned the city of Porto in six areas and associated a real IoT with
each area. Our simulator associates an object with a given route recorded in
the dataset and an object instance with each partition of a route belonging to
an area. It creates a MIoT node for each instance and a c-arc for each pair
of instances belonging to the same route. Furthermore, it creates an i-arc be-
tween two nodes of the same IoT if the length of the time interval between the
corresponding routes is less than a certain threshold tht . The value of tht can
be specified through the constructor interface. Clearly, the higher tht the more
connected the constructed MIoT.
The MIoT for this experiment, which we constructed through our simulator,
consisted of 1256 nodes. This number of nodes is much higher than the ones
presently characterizing real MIoTs. However, we preferred to construct such
a large MIoT because we think that, with the enormous development of the
IoT, in the future there could exist MIoTs consisting of thousands of nodes. On
the other side, we did not adopt larger MIoTs because they required excessive
computation times without providing more knowledge on scope than the one we
could have acquired with a MIoT of about 1000 nodes. The six IoTs of the MIoT
had 128, 362, 224, 280, 98 and 164 nodes, respectively. The constructed MIoT is
returned in a format that can be directly processed by the cypher-shell of Neo4J.
The interested reader can find the MIoT adopted in the experiments described
here at the address http://daisy.dii.univpm.it/miot/datasets/scope.
We carried out all the tests presented in this section on a server equipped
with an Intel I7 Quad Core 7700 HQ processor and 16 GB of RAM with the
Ubuntu 16.04 operating system. To implement our approach we adopted:
– Python, as programming language;
– Neo4J (Version 3.4.5), as underlying DBMS.
In these experiments, we aimed at investigating the trend of the scope against
the neighborhood level t. In particular, for each instance ιjk of the MIoT, we
computed Sctjk when t increases from 1 to the diameter of Ik . After this, we
grouped the instances of our MIoT in several ways (one for each test), based on
some specific rationales, and we computed the variation of the average values of
the scope for each group.
As a first task of this activity, we computed the variation of the average
values of the scope for each IoT of the MIoT. This is equivalent to say that the
instances of the MIoT were grouped in the corresponding IoTs (one group for
each IoT). The obtained results are reported in Figure 1. From the analysis of
this figure, we can observe that, in each IoT, the scope decreases quite quickly.
Indeed, it is extremely high when t = 1 in all the IoTs. When t = 2, the scope
is high for the largest IoTs, whereas it has an intermediate value for the other
ones. In any case, the scope becomes very low when t is greater than or equal to
4 for small networks and when t is greater than or equal to 5 for the large ones.
As a second task, we computed the variation of the average values of the
scope for the whole MIoT. This is equivalent to say that we had a unique group
Fig. 1. Variation of the average scope for each IoT of the MIoT against the neighbor-
hood level
comprising all the instances of the MIoT. The obtained results are reported
in Figure 2. From the analysis of this figure, we can conclude that the scope
presents a trend similar to the one of the largest IoTs of Figure 1. In particular,
it is very high for t = 1; it is high for t = 2; it has an intermediate value for
t = 3, whereas it is very low for t > 5.
Fig. 2. Variation of the average scope for the whole MIoT against the neighborhood
level
As a final task, we subdivided all the instances of the MIoT in two groups
containing i-nodes and c-nodes, respectively. Then, we computed the variation of
the average values of the scope for the two groups. The final goal of this task was
to verify if i-nodes and c-nodes showed different behaviors as far as their scope
is concerned. The obtained results are reported in Figure 3. From the analysis
of this figure, we can observe that the scope decreases for both i-nodes and c-
nodes. However, the trends are different. Indeed, the decrease is much smother
for i-nodes than for c-nodes. In particular, for c-nodes, the decrease is very steep
because the scope is less than 0.2 already for t = 3. This can be explained by
considering that, analogously to what was made in all the past approaches, our
definition of neighborhood (which plays a key role in our definition of scope)
considers as neighbors of a node only other nodes of the same IoT. In other
words, it takes only i-arcs into account. Actually, we believe (and the results of
Figure 3 represent a confirmation) that it is worthwhile to investigate the role
of c-arcs in the computation of the neighborhood of a node and we plan to make
this investigation in the future.
Fig. 3. Variation of the average scope for the i-nodes and the c-nodes of the MIoT
against the neighborhood level
As for the investigation of the values of the scope for objects, we observe
that they are obtained by averaging the values of the scope of the corresponding
instances. As a consequence, it does not make sense to perform the first and the
final tasks of the previous activity. The only task that makes sense is the second
one; in this case, the variation of the average values of the scope of objects for
the whole MIoT is reported in Figure 4. As we could have expected, this trend
is very similar (or, better almost identical) to the one of Figure 2.
6 Conclusion
In this paper, we have seen that social internetworking and the Internet of Things
are becoming more and more contiguous and are giving rise to several social
and/or multiple IoTs paradigms. In this new scenario, we have introduced the
concept of scope of a thing in a MIoT, along with a formalization allowing the
computation of the corresponding values. Then we have illustrated one possible
application along with some experiments aiming at evaluating the variation of
scope against the value of the neighborhood level.
Fig. 4. Variation of the average scope for the objects of the MIoT against the neigh-
borhood level
This paper should not be considered as an ending point. Instead, it could
be the starting point of many researches in this field. Indeed, there are several
future related investigations that could be made in this context. First, we would
like to analyze the role of possible constraints on network nodes or arcs in the
definition of scope. Then, we plan to study the role of scope in the detection of
anomalies and, even more, for understanding the extension and the importance
of the damage caused by them. Finally, we would like to analyze the exploitation
of scope in predictive maintenance, that is currently one of the most important
research issues in the manufacturing industry.
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