# A Simulation Study of Social-Networking-Driven Smart Recommendations for IoV

Kashif Zia, Arshad Muhammad, Dinesh Kumar Saini  Sohar University, Sohar, Oman

###### Abstract

Social aspects of connectivity and information dispersion are often ignored
while weighing the potential of Internet of Things (IoT). In the specialized
domain of Internet of Vehicles (IoV), Social IoV (SIoV) is introduced
realization its importance. Assuming a more commonly acceptable
standardization of Big Data generated by IoV, the social dimensions enabling
its fruitful usage remains a challenge. In this paper, an agent-based model of
information sharing between vehicles for context-aware recommendations is
presented. The model adheres to social dimensions as that of human society.
Some important hypotheses are tested under reasonable connectivity and data
constraints. The simulation results reveal that closure of social ties and its
timing impacts dispersion of novel information (necessary for a recommender
system) substantially. It was also observed that as the network evolves as a
result of incremental interactions, recommendations guaranteeing a fair
distribution of vehicles across equally good competitors is not possible.

###### keywords:

Social IoV , Agent-Based Model , Discrete-Event Simulation, SIoT

##  1 Introduction

Modern technology has reached to a point whereby electronic devices (handheld,
wearable, sensors, appliances etc.) are commonplace in every facet of our
life. In 1991, Mark Weiser [1] visioned it and described that in the 21st
century, computers will become so ubiquitous that they pervade every area of
our lives and environment. He believed the development will be inevitable, and
that the effects would be far greater than just the physical provisioning of
devices. He believed that the technology would become a fabric of everyday
life, changing the way we interact with our surrounding [2], including both
the environment and the society.

The latest manifestation of “all connected world” is Internet of Things (IoT)
[3]. At the physical level, objects in IoT have embedded processors and
capability to communicate among them using wired or wireless connections [4,
5]. According to the recent predictions by CISCO, 50 billion “things” will be
connect to the Internet by 2020 [6]. Vehicles being an important part of our
lives and first one to be equipped with recent technologies, are rapidly
becoming the first case study of IoT, more appropriately termed as Internet of
Vehicles (IoV) [7, 8].

IoV is more than numerous sensors embedded in modern vehicles which can not
only receive the information from its surroundings but also transmit
information, assisting in navigation and traffic management [9]. A practical
realization of vehicular technologies is Vehicular Ad hoc NETworks (VANETs)
[10], which resulted in important applications regarding handling traffic and
better driver / passengers experience [9]. On the other side, we have
Vehicular Social Networks (VSN) where passengers can exchange data related to
entertainment, social networks and situations [11] However, IoV is more then a
social network of vehicles themselves; in which, vehicles build and manage
their own social network; more appropriately termed as Social Internet of
Vehicles (SIoV).

SIoV is a social network of vehicles, still, it cannot be separated from the
drivers or owners of these vehicles. Hence, when a vehicle is a part of SIoV,
it must build and use the network to achieve owners’ goals. Although, it can
be debated, but the properties of human social network are mapped onto buildup
and evolution of inter-vehicle social network. Then, such a “physical” SIoV is
used as a medium to achieve owners’ goals. The interfacing of the network and
the extend of user (owner of vehicle) satisfaction with respect to goal
achievement is a gray area for the purpose of this paper, but definitely
possible using current personal, vehicular and domestic technologies.

This paper investigates the potential of SIoV by designing an agent-based
model and simulating in various conditions. The model’s conceptualization is
based on basic assumptions about IoT connectivity, principles of social
network evolution, and users profiling. User profiles and activities serve as
an underpinning for the model. Some interesting what-if questions are asked
against a couple of intuitive hypotheses. However, the study revolves around
finding the extend to which SIoV is capable of providing correct
recommendations to their owners in the wake of changing dynamics of the
resources.

The rest of the paper is organized as follows. Related work follows in section
2. In section 3, we present the scenario adopted and research hypotheses
adapted from the scenario. The proposal model is explained in section 4.
Simulation settings and analysis of the simulation results is presented in
section 5. Section 6 provides discussion about the hypothesis and conclusion.

##  2 Related Work

Recently there are several research activities to investigate the
possibilities of embedding social networking concepts into the Internet of
Things (IoT) solutions; resulting paradigm is known as Social Internet of
Things (SIoT) [12]. It is evident from the research that a group of people or
society (sharing the same interest) can offer more accurate information as
compared to an individual. In [13], define SIoT as IoT where things are
capable of establishing the social relationship with other objects
autonomously. Social networking concepts have been applied in several
networking settings such as delay tolerant, peer-to-peer networking [14].
Authors in [14] suggest that objects can establish social relationships based
on object profile, activities, and interests.

In [9] introduce an exciting idea of Social Internet of Vehicle (SIoV) using
existing VANET’s technologies such as vehicle-to-vehicle, vehicle-to-
infrastructure and, vehicle-to-internet communications and presents a
vehicular social network platform following cyber-physical [15]. SIoV uses
social relationships between physical objects to exchange and stores different
types of information such as safety, infotainment, comfort etc. as a social
graph. It provides online or offline information for intelligent transport
system [16], online allows safe and efficient travel of the vehicles while
offline data ensures smart behavior of the vehicles.

[17] presented an exciting idea of Vehicular Social Networks (VSNs) that a
large number of people spend 2-3 hours daily on the road to commute between
home and the office. These users use the same roads on a daily basis provides
an excellent opportunity to form periodic virtual mobile communities. Three
primary reasons for VSN formation are for entertainment (based on people’s
interest to discuss music, movies etc), utility(local event’s and/or Point of
Interest (POI) such as hotel, shopping center) and emergency (road accidents
or assistance during critical conditions). Authors presented RoadSpeak a VSN-
based system allows driver to join Voice Chat Groups (VCGs) along the popular
highways and roadways to facilitate communication among the car occupants.

[18] presented a model for optimal route choice to helps drivers to decrease
travel time and reduce the traffic congestion [19] by utilizing a Vehicle
agent-based in Edge and cloud to allow drivers to negotiate with others known
as Virtual Vehicle (VV) [7]. Each driver has corresponding VV, and parts of
driver’s knowledge, which can replace the driver to make a decision in cloud.
Using bargaining routing approach for the optimal route calculation. They set
up a source node, target nodes and m parallel links, known to all players. The
players decided to take the route and realize the cost incur on their route.
Maybe due to the selfish behavior of the VV not cooperate with others and in
case of cooperation with others to decrease the cost of travel.

In [20] presented a Social Drive (crowdsourcing-based VSN) system for green
transportation by integrating vehicular On-Board Diagnostics (OBD) module,
cloud computing, and social networks and incorporates rating mechanism about
the driver’s fuel economy. Using a mobile application to promote awareness of
their driving behaviors regarding fuel economy. There are numbers of other
crowdsourcing based applications for green transportation such as UbiGreen
[21], GreenGPS [22], and Cyberphysical bike[23].

According to predication in [24] by 2050, 70% of world population will live in
the cities. The concept of smart cities paving the way for Smart and Connected
Communities (SCC) defined as a group of people who are living in the same area
such as city or town. Authors argue that IoT can provide a ubiquitous network
of connected devices and smart sensors for SCC and big data analytics [25] has
potential to make a move from IoT to real-time control for SSC. Authors
presented TreSight as a case study for smart tourism in the city of Trento,
Italy, a context-aware recommendation system to provide personalized
recommendations. Using data from OpenData Trentino regarding POI, weather,
etc. with additional data collected using CrowdSensing with a wearable
bracelet.

In this paper, we incooperate social relationship among the objects (vehicle
refer as a object), i.e. social relationship may be established between these
vehicles while there owner’s visiting the same PoI. At the start, relationship
may be a ‘weak tie’ later converted to ‘strong tie’ depends on the number of
times these objects meet each other. Generating user profiles based on the
recommendations vehicles may receive from others in the social network. User
weekly plan may be changed as per these recommendations by
providing/suggesting an alternative PoI, which could be higher or equal
expectation of the user. From the above given scenario, an agent-based model
is designed.

##  3 Scenario and Research Hypotheses

We usually spend most of our time during the weekdays to commute between work,
home, school, and shopping. Drivers use number of applications such as
navigators [26] to reduce the travel time or to choose between different
available options. Moreover, in recent years, Location Based Social Networks
(LBSN) [27], i.e. Facebook Places, Googlemaps etc have gained popularity,
where users can share their physical locations, experiences, and ratings etc.
Due to these developments, recommender or recommendation systems have gained
popularity in recent years where big data is driving force to provide context-
aware recommendations to their users.

Realization of a recommendation system enabled by the capabilities of SIoV is
an exciting topic. It becomes achievable due to recent industrial endeavors.
For example, In CarStream project [28], a technological integration is
performed to provide data-driven services. Over 30,000 chauffeured driven cars
are connected, the system collects a variety of data such as “vehicle status,
driver activity, and passenger-trip information”. The user demand is generated
by combining parameters such as pickup point, pickup time, arrive time and
destination. Motivated from these data fields, our model scenario utilizes the
concepts of ID, Time and Duration, to describe a Point of Interest (PoI) from
vehicle or user perspective, where ID is the identity of the destination, Time
is the time to reach the destination, and Duration is the time to stay at the
destination.

User profiling (motivated from [29]) is used to generate a plan in which a
user has to visit some PoIs on a weekly basis. Each user has an expectation,
which is a static personal trait. If the quality of service provided by a PoI
at the time of a visit is not up to the user’s expectation (user experience),
the PoI is considered as suspended for that user. Vehicles connect with each
other if they are at the same PoI, thus evolving a social network of vehicles
with repeated encounters. The question asked in this paper is: to what extent
SIoV is capable of providing the required PoIs recommendations to the users if
a user has most of PoIs suspended due to poor quality and/or high expectation.

Before, stating the exact hypotheses, it is worth noting that the vehicles are
not different from users. The interface between a vehicle and its owner (user)
is assumed to be seamless so that a vehicle know about its owner’s plan and
also if the current experience about a PoI was good or bad. Vehicle is also
capable of changing owner’s plan without owner approval. To avoid unnecessary
complexity, all these simplifications are made to keep our focus on the
evaluation of the following research hypotheses:

  * 1.

Hypothesis 1: Since connectivity potentially transforms into interaction
(information sharing), an increased connectivity degree provides more prospect
of having novel information; in short, a novelty in information is supported
by triadic closure [30].

  * 2.

Hypothesis 2: Since information sharing should increase the user experience,
early the information sharing starts, the better would be the user experience;
in short, novel information sharing is directly proportional to how early the
information sharing starts.

  * 3.

Hypothesis 3: Due to intrinsic random nature of individual plans, a fair
distribution of resources should be guaranteed as a result of novel
information sharing.

An agent-based model and simulation is used to verify these hypotheses.

##  4 Model

The basic purpose of the vehicles is to visit PoIs given in the plan at their
prescribed time. The three strategies adopted are:

  1. 1.

AsPlanned

  2. 2.

Blacklist

  3. 3.

Replace (without and with triadic closure)

The description (section 4.2) of these strategies follows section 4.1.

###  4.1 Model Flow

#### Vehicle / User Plan

Plan is a matrix of five columns. The first column contains the $ID$ of a PoI,
second column is the $time$ to visit that PoI. The third column is the
$duration$ of the visit. The fourth column stores numeric representation of
last visit $experience$ of the user (vehicle) about that PoI. The fifth column
represents if this PoI is $suspended$ or not. A list of random PoIs are chosen
along with corresponding visit times and visit durations, which are also
random. One simulation iteration is equal to one hour. Initially, a random
weekly plan, constituted by three components (scheduled visits) is generated,
which executes on weekly basis.

#### Vehicle States

The transition system of vehicles’ behavior is divided into five states. If
$state=0$, the vehicle is located at user’s home. A vehicle will transit to
the next state ($state=1$) if it is $time$ to execute a component of the plan.
This is done through check_outbound* procedure. If $state=1$, the vehicle
moves to the desired location along with transiting to the next state
($state=2$). This is done through move_location procedure. If $state=2$, and
the $duration$ to stay at the current location is expired, the vehicle will
transit to the next state ($state=3$). This is done through check_inbound
procedure. If $state=3$, the vehicle will communicate with its neighbors (in
proximity). This is done through communicate procedure. After communicating,
the vehicle will move back to home and resets its state to $0$. This is done
through move_home procedure. Figure 1 presents a graphical view of vehicles’
state transitions. In the following, a description of each of these procedures
is given.

Figure 1: Vehicle state transitions to be performed at each iteration (= one hour) of the simulation by all the vehicles . ID | Time | Duration | Experience | Suspended?
---|---|---|---|---
1059 | 17 | 2 | 1 | false
1054 | 122 | 4 | 1 | false
1053 | 50 | 2 | 1 | false
Table 1: Plan matrix Initialization (vehicle 569).

#### check_outbound*

The plan matrix contains three components (scheduled visits), which are
randomly set at the start of the simulation. Table 1 shows an example of this
matrix for a sample vehicle, with ID $569$. At hour $17$, this vehicle has to
visit PoI $1059$ for a duration equal to $2$ hours. The check_outbound*
procedure, at iteration $17$ would identify it and the state of the vehicle
would change from $0$ to $1$. Similarly, at their turn, PoI $1054$ and $1053$
will also be visited. By default, the experience of the user regarding the
recent visit to a PoI is set to default positive extreme equal to $1$. As
shown in Figure 1, the procedure check_outbound* has three varieties which
correspond to three different strategies of selection of a PoI. Here, we have
explained strategy of selecting a PoI according to the initial plan, named as
check_outbound_As_Planned (replacing * by the strategy used). Obviously, this
strategy is static, just executing the plan as it is.

#### move_location

A vehicle moves to the PoI selected in check_outbound* procedure. The current
$quality$ offered by the PoI with a random variation of $\pm 25$ % is stored
in the plan matrix as experience of the user. For example, vehicle $569$ has
to visit PoI $1059$ at iteration 17 (also see Figure 2). The progression curve
of $quality$ offered by PoI $1059$ is shown in 3. At iteration 17, the value
is $0.092$ which is transformed into $experience$ of the vehicle and stored
into plan matrix as $0.0909$ (see updated matrix of Table 2).

Figure 2: A view of one fourth of the simulation world. Vehicle with yellow circle is vehicle $569$, and PoI with red circle is PoI $1059$. Figure 3: Quality progression of PoI 1059 of first two weeks. ID | Time | Duration | Experience | Suspended?
---|---|---|---|---
1059 | 17 | 2 | 0.0909 | false
1054 | 122 | 4 | 1 | false
1053 | 50 | 2 | 1 | false
Table 2: Updated plan matrix (vehicle 569).

#### check_inbound

If visit duration of a vehicle at a PoI is exhausted, the state of the vehicle
transits into $3$. After completing 2 units of duration at PoI 1059, vehicle
569’s state changes to 3 at iteration 20.

#### communicate

The vehicles co-located at the same PoI network with each other to form ties.
Initially, it is a weak tie. With repeated encounters, a weak tie becomes a
strong tie. Each vehicle has a data structure, which contains this
information. For each vehicle which has formed a tie with another vehicle, the
array of information is constituted by a tuple “time of the latest encounter,
how many encounters?, strong tie?”. If number of encounters with the same
vehicle reaches to a threshold, a weak tie changes into a strong tie.

For example, vehicle 259 at iteration 20 encounters two more vehicles at PoI
1059. It is evident from Table 3, both these vehicles are encountered for the
first time (how many encounters? = 1) and have established a weak tie with
vehicle 259 (strong tie? = false). With repeated encounters at same or other
locations, a weak tie will change into a strong tie, if the number of
encounters are equal to the threshold.

Vehicles Data of vehicle 569
---
984 | time of latest encounter | 20
how many encounters? | 1
strong tie? | false
691 | time of latest encounter | 20
how many encounters? | 1
strong tie? | false
Table 3: Vehicles Data (vehicle 569).

It is worth noting that communication is not used in first two strategies.
Hence, in first two strategies, after completion of duration to stay at a PoI,
a vehicle moves back to its home.

#### move_home

A vehicle moves back to its home, resetting its state from 3 to 0.

###  4.2 Strategies

#### AsPlanned

implemented through check_outbound_AsPlanned as explained in the last
subsection.

Algorithm 1 Check_outbound_blacklist Algorithm

1:$i\leftarrow$0

2:while ($i<3$) do

3: ${t}\leftarrow$plan[i,1]

4: ${q}\leftarrow$plan[i,3]

5: ${s}\leftarrow$plan[i,4]

6: if $(t=current\\_time\&\&s=false)$ then

7: if $(q>expectation)$ then

8: $state\leftarrow 1$

9: $select\ ID\ of\ this\ row\ as\ destination$

10: else

11: $plan[i,4]\leftarrow true$

12: end if

13: end if

14: $j\leftarrow$0

15: while $j<3$ do

16: $qq\leftarrow$plan[j,3]

17: if $(qq>=expectation)$ then

18: $plan[j,1]\leftarrow current\\_time$

19: $state\leftarrow 1$

20: $select\ ID\ of\ this\ row\ as\ destination$

21: $j\leftarrow$3

22: else

23: $j\leftarrow$j++

24: end if

25: end while

26:end while

#### Blacklist

implemented through check_outbound_Blacklist. From a vehicle’s plan, the
method of selection of a visits works as follows. If the vehicle has to visit
a PoI at the current hour and that PoI is not already blacklisted (if
blacklisted then do not visit any location), then the quality of current PoI
will be evaluated against vehicle’s $expectation$. If quality is greater than
expectation, the vehicle will transit to state 1, ready to visit the current
PoI. Otherwise, the PoI will be suspended in the plan matrix. For example, at
the 17th hour of the second week, the last experience of vehicle 569 about PoI
1059 was 0.0909, which is well below its expectation (0.465640). Hence, PoI
1059 will be suspended in vehicle 569 plan (see Table 4). Further, an
alternate PoI would be sought that is not yet blacklisted to be executed in
the current hour. If no such PoI is found, the vehicle would not visit any
location (state remains 0). Algorithm 1 provides pseudocode of the procedure.

ID | Time | Duration | Experience | Suspended?
---|---|---|---|---
1059 | 17 | 2 | 0.0909 | true
1054 | 122 | 4 | 0.5684 | false
1053 | 50 | 2 | 0.3325 | false
Table 4: Plan matrix update at 17th hour of second week (vehicle 569).

#### Replace

implemented through check_outbound_replace. In this procedure, all those PoIs
that have been suspended would be replaced by other PoIs which are not
suspended. The information about these PoIs would come from “friends” (having
strong ties) of a vehicle. The whole suspended row in plan matrix of the
vehicle would be replaced by a not-suspended row attained from a friend.
Algorithm 2 provides pseudocode of the procedure replace. After replacement,
check_outbound_Blacklist procedure would be called to select a PoI. Replace
has two varieties

Algorithm 2 Replace Algorithm

$i\leftarrow$0

2:while ($i<3$) do

${t}\leftarrow$plan[i,4]

4: if $(s=false)$ then

$i\leftarrow i++$

6: else

$j\leftarrow 0$

8: $c\leftarrow length[vehiclesdata]$

while ($j<c$) do

10: $t\leftarrow friend\ j\ in\ the\ list\ of\ friends\ with\ strong\ tie$

$fplan\leftarrow plan\ of\ f$

12: $k\leftarrow 0$

while ($k<3$) do

14: $ss\leftarrow fplan[k,4]$

if $(ss=false)$ then

16: $plan[i]\leftarrow fplan[k]$

$k\leftarrow 3$

18: $j\leftarrow c$

else

20: $k\leftarrow k++$

end if

22: $j\leftarrow j++$

end while

24: end while

end if

26:end while

  * 1.

Replacement without triadic closure: Replacement of blacklisted PoIs can be
performed without considering the internal social networking dynamics, as
mentioned above.

Algorithm 3 triadic closure

$nof\leftarrow number\ of\ friends\ of\ this\ vehicle$

if $(nof>1)$ then

3: $fv\leftarrow random[0-nof-1]$

$sv\leftarrow random[0-nof-1]$

if $(fv!=sv)$ then

6: if $fv\ has\ strong\ tie\ with\ this\ vehicle$ then

if $sv\ is\ not\ friend\ of\ fv$ then

$make\ sv\ friend\ of\ fv$

9: $create\ strong\ tie\ between\ two\ vehicle$

end if

if $fv\ is\ not\ friend\ of\ sv\ \textbf{then}$

12: $make\ fv\ friend\ of\ sv$

$create\ strong\ tie\ between\ two$

end if

15: end if

end if

end if

  * 2.

Replacement with triadic closure: Replacement of blacklisted PoIs can also be
performed while considering the internal social networking dynamics. One of
the most basic and most important dynamics of social networks evolution is
“triadic closure”. The triadic closure property states that, if a node a in a
social network has strong ties with two nodes b and c, then, there is a
likelihood that b and c would becomes friends themselves some times in the
future. Algorithm 3 provides pseudocode performing triadic closure just before
application of Algorithm 2.

##  5 Simulation and Results

The model is created in NetLogo [31], a multi-agent programming environment. A
grid of cells of size $75\times 75$ is used to populate agents. There are
three types of agents, two static and one mobile. The static PoI and Home
agents and randomly placed in the environment. The count of PoIs is 15,
available for 500 homes. Vehicles are mobile agents which are $10\%$ more than
homes; thus, ensuring attachment of more than one vehicles to a home. A
vehicles are attached randomly to a home. The users (owners of the vehicles)
are abstract entities encapsulated within vehicles.

The basic purpose of the vehicles is to visit PoIs as per plan at their
prescribed time. The three strategies adopted (as explained before) are
evaluated based on three parameters:

  * 1.

quality-index: When a vehicle visits a PoI, it experiences the quality offered
by that PoI at that time. In one week, a vehicle has to visit three PoIs, that
is why, the quality-index is the average of three experiences of the vehicle
at each hour. The quality-index of the whole simulation space is then an
aggregation of all these experiences divided by vehicles count.

  * 2.

connectivity-index: When two vehicles are at some PoI at the same time, they
connect with each other, irrespective of type of tie they may have (weak or
strong). The connectivity-index of the whole simulation space is an
aggregation of all current (at this hour) connections divided by vehicles
count.

  * 3.

PoI-utilization: The population of vehicles at a PoI at an hour determines its
current utilization. Derived from it is the standard deviation of utilization
of all PoIs.

Figure 4: Comparison of Strategies ($th_{strong-tie}=5$): Average quality of
PoIs experienced by vehicles averaged across 100 simulation runs.

The graph shown in Figure 4 presents time series of average quality of PoIs
experienced by all the vehicles for a span equal to twenty weeks and averaged
across 100 simulation runs. A comparison between four strategies reveals the
following:

  * 1.

Blacklist: Once a PoI is blacklisted, it is not visited by a vehicle anymore.
Less number of visits are performed hence reducing the average quality-index,
due to the reason that after the first week, only those PoIs are visited which
satisfy users / vehicles expectation.

  * 2.

AsPlanned: Although, the above strategy (Blacklist) presents an extreme case,
it can be argued to be the case. But, continuously visiting the same PoIs,
which is not up to user expectations is less likely to happen. However, due to
randomness in quality parameter of PoIs, the PoIs that did not perform
according to the expectations previously, may perform well this time. This is
what exactly is revealed in the simulation results, in which, after the first
week, quality-index fluctuates around the middle (0.5).

  * 3.

Replace: The replace strategy replaces a blacklisted PoI that a vehicle wants
to visit by a PoI recommended by a friend. Since a friend is a strong tie, the
value of threshold (repeated encounters) is important. A higher value of
threshold ($th_{strong-tie}=5$), such as 5 in this case, would delay
replacement process. In this case, the process is so delayed that no vehicle
has new information for others as all PoIs are already blacklisted.

  * 4.

Replace with Closure: The triadic closure provides novel information about
uncommon PoIs. That is the reason, most of the time, this strategy competes
with the AsPlanned strategy.

Figure 5: Comparison of Strategies ($th_{strong-tie}=5$): Average connectivity
of vehicles averaged across 100 simulation runs.

The graph shown in Figure 5 verifies the above findings relating the quality
index with the average degree of vehicles. The connectivity of strategy
replace with closure is by far higher than the other three strategies.

Figure 6: Comparison of Strategies ($th_{strong-tie}=2$): Average quality of
PoIs experienced by vehicles averaged across 100 simulation runs. Figure 7:
Comparison of Strategies ($th_{strong-tie}=2$): Average connectivity of
vehicles averaged across 100 simulation runs.

With decrease in encounter threshold of strong tie ($th_{strong-tie}=2$), the
Replace strategy starts competing with Replace strategy with triadic closure.
It happens in patches through, where connectivity generally increases jumping
up from one phase to another, consequently, increasing the quality index in an
incremental way as well (See Figure 6 and Figure 7). Overall, in terms of
quality index replace and replace with triadic closure for $th_{strong-tie}=2$
works much better than $th_{strong-tie}=5$.

Figure 8: Comparison of Strategies ($th_{strong-tie}=5$): Average standard
deviation of PoIs utilization across 100 simulation runs. Figure 9: Comparison
of Strategies ($th_{strong-tie}=5$): Number of vehicles not visiting any PoI
(one simulation run per strategy).

Another exciting aspect that is studies is utilization of PoIs. Although there
is no safeguard against it in the model itself, it is undesirable to have high
asymmetry in PoIs utilization, which is achieved through standard deviation of
PoI utilization (SDU). The graph shown in Figure 8 compares four strategies.
It can be seen that SDU of strategy blacklist is least because there are no
visits after week 1, evidenced by the graph shown in Figure 9. The strategy
replace also has a low SDU after first few weeks, but this is also due to no
visits. The strategy replace with closure has an erratic behavior, sometimes
high and sometimes low. But contrary to the above, it is evident (from Figure
9) that low SDU is not due to no visits, but, due to the symmetric utilization
of PoIs which are not blacklisted. No doubt, high SDU is due to extreme
asymmetry in PoIs utilization.

Figure 10: Comparison of Strategies ($th_{strong-tie}=2$): Average standard
deviation of PoIs utilization across 100 simulation runs.

The inactivity of replace strategy is compensated by reducing from
$th_{strong-tie}=5$ to $th_{strong-tie}=2$. Compare strategy replace with
strategy replace with closure in the graph shown in Figure 10. Overall, again,
in terms of PoIs utilization, replace and replace with triadic closure for
$th_{strong-tie}=2$ works much better than $th_{strong-tie}=5$.

##  6 Discussion and Conclusion

Figure 11: Comparison of Strategies ($th_{strong-tie}=5$): Connectivity and
PoI utilization at iteration 800. (a) Replace, (b) Replace with Closure.
Figure 12: Comparison of Strategies ($th_{strong-tie}=2$): Connectivity and
PoI utilization at iteration 800. (a) Replace, (b) Replace with Closure.

In a dynamically changing quality of service provisioning by PoIs, the
vehicles with blacklisted PoIs in their plan can utilize their strong ties to
have novel information about some PoIs those are not blacklisted at a
particular time. It is evident from connectivity comparison between replace
and replace with closure in the simulation screenshots shown in Figure 11, in
which connectivity increases from (a) to (b). Increase in connectivity is
responsible of increasing the quality index as depicted in Figure 4 and 5.
Same is true when threshold is decreased from $th_{strong-tie}=5$ to
$th_{strong-tie}=2$ (see Figure 12 and corresponding graphs in Figure 6 and
7). These results verify Hypothesis 1.

However, a delay in the information sharing, particularly without triadic
closure would not help. This is evident from inability of replace strategy to
perform well if $th_{strong-tie}=5$, whereas, it performs reasonably well if
$th_{strong-tie}=2$. This verifies Hypothesis 2. In combination, the following
can be concluded:

“Novelty in information is directly proportional to how early the information
sharing starts, however, triadic closure is capable of producing novel
information even if information sharing starts late.”

In Figure 11 and 12, the size of PoIs (blue circles) depicts their
utilization. For $th_{strong-tie}=5$ and $th_{strong-tie}=2$ both, when
comparing (a) with (b), the variation in sizes in less in (a) and more in (b).
Hence, SDU is less in case of replace and more in replace with closure. It is
also evident from Figure 8 and 10. A cross comparison among Figure 8 and 10
reveals that for lesser threshold, SDU is even less. Moreover, at $th_{strong-
tie}=5$, replace with closure works better at the early stages of the
simulation when compared with replace. This is opposite in case of
$th_{strong-tie}=2$, replace works better at the early stages of the
simulation when compared with replace with closure.

Overall, SDU is less in the early stages of the simulation and then it becomes
erratic, fluctuating between extremely high values to extremely low values.
Therefore, Hypothesis 3 is conditionally verified and can be stated as:

“Fair distribution of PoIs is guaranteed at the start but not later.”

This brings us to the first outlook of the study: “How can we guarantee a fair
distribution of PoIs even in the later stages of the proceedings?” The fact
that blacklisted PoIs goes out of the system once and for all, and that they
have no means to announce their updated quality even if it is high, it is
natural to provide a mechanism of PoIs becoming part of the systems again. In
real-life this can be related to the concept of memory and historical
affiliation. Due to human ability to forget, and retain a long lasting
relationships even after recent unpleasant experiences, a model extension
incorporating these changes makes sense.

Another aspect of investigation in the future is finding a way to stop rapid
fluctuations in SDU. The rapid fluctuations in SDU happen from herding of most
vehicles to a few PoIs (resulting in extremely high values of SDU) to no
activity (resulting in extremely low values of SDU) or vice versa. To average
out these extreme cases, we intend to incorporate a cooperation strategy
between two vehicles, alternating a PoI utilization between them, so that no
PoI is loaded to its extreme.

In conclusion, closure of social ties and its timing plays an important role
in dispersion of novel information. As the network evolves as a result of
incremental interactions, recommendations guaranteeing a fair distribution of
vehicles across equally good competitors is not possible, particularly at the
later stages of interactions.

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