SAVING: socially aware vehicular information-centric networking

Abstract

Mobile devices today are constantly generating and consuming a tremendous amount of content on the Internet. Caching of such "massive" data is beyond the capacity of existing cellular networks in terms of both cost and bandwidth due to its connection-centric nature. The increasing demand for content poses fundamental questions like where, what, and how to cache and retrieve cached content. Leveraging the shift toward a content-centric networking paradigm, we propose to cache content close to the mobile user to avoid wasting resources and decrease access delays. Therefore, we present SAVING, a socially aware vehicular information-centric networking system for content storage and sharing over vehicles using their computing, caching, and communication (3Cs) capabilities. The encapsulated 3Cs are exploited first to identify the potential candidates, socially important, to cache in the fleet of vehicles. To achieve this, we propose a novel vehicle ranking system allowing a smart vehicle to autonomously "compute" its eligibility to address the question, where to cache. The identified vehicles then collaborate to efficiently "cache" content between them based on the content popularity and availability to decide what and how to cache. Finally, to facilitate efficient content distribution, we present a socially aware content distribution protocol allowing vehicles to "communicate" to address the question how to retrieve cached content. Implementation results for SAVING on 2986 vehicles with realistic mobility traces suggests it as an efficient and scalable computing, caching, and communication system.

Full text

SAVING: Socially Aware Vehicular
Information-centric Networking
Junaid Ahmed Khan∗† and Yacine Ghamri-Doudane†
∗University Paris-Est, LIGM Lab, Marne-la-Vall´
ee, France
†L3i Lab, University of La Rochelle, France
junaid-ahmed.khan@univ-mlv.fr, yacine-ghamri@univ-lr.fr
Abstract—Mobile devices today are constantly generating and
consuming a tremendous amount of content on the internet.
Caching of such “massive” data is beyond the capacity of existing
cellular networks both in terms of cost and bandwidth due
to its connection-centric nature. The increasing demand for
content poses fundamental questions like, where, what, how to
cache and how to retrieve cached content? Leveraging the shift
towards content-centric networking paradigm, we propose to
cache content close to the mobile user to avoid wasting resources
and decrease access delays. Therefore, we present SAVING, a
Socially Aware Vehicular Information-centric Networking system
for content storage and sharing over vehicles due to their
Computing, Caching, and Communication (3Cs) capabilities. The
encapsulated 3Cs are exploited first to identify the potential
candidates, socially important to cache in the fleet of vehicles. To
achieve this, we propose a novel vehicle ranking system allowing
a smart vehicle to autonomously “Compute” its eligibility to
address the question, where to cache? The identified vehicles then
collaborate to efficiently “Cache” content between them based on
the content popularity and availability to decide what and how
to cache? Finally, to facilitate efficient content distribution, we
present a socially-aware content distribution protocol allowing
vehicles to “Communicate” to address the question, how to retrieve
cached content? Implementation results for SAVING on 2986
vehicles with realistic mobility traces suggests it as an efficient
and scalable computing, caching and communication system.
Keywords—Information-Centric Networking, Vehicular Net-
works, Social-aware Content Distribution, Content Caching, Data
Offloading
I. INTRODUCTION
The recent advances in the communication technologies
along the soaring number of smart mobile devices results in
growth of content demand by lots of consumers in closer urban
proximity each with multiple portable devices. For example,
large number of users on the move in an urban environment
such as passengers in buses, taxis, and vehicles are interested
to watch the video of a latest episode of a hot TV show/drama
or a sports highlights. Provisioning of such popular content to
each user require lots of redundant connections between users
and the service provider, given that the content is requested by
lots of spatio-temporally co-located users with similar social
interests. It is now challenging for the current “connection-
centric” network infrastructure to facilitate content availability
for such large number of mobile users in close proximity
in an urban environment while offering attractive tariff plans
supporting unlimited bandwidth. We advocate to use the re-
cently proposed Information-Centric Networking (ICN) [1] in
[2] and [3] paradigm which cater the issue by decoupling the
content provider-consumer and support in-network caching at
intermediate nodes.
Content caching at intermediate nodes is studied for while
using different caching policies based on typical content re-
placement strategies such as first-in first-out (FIFO), Least
Recently Used (LRU) and Least Frequently Used (LFU). This
article highlights to the research community an advanced
dimension of the underlying content caching challenge by
posing the following fundamental questions. First, there is a
need to identify the eligible candidate to cache content by
finding answer to the question Where to cache?, thus finding
the criteria for a node to be an important information hub in
the network. Once such nodes are identified, another question
follows: What and how to cache? regarding decisions based
on content popularity and availability in the network. There is
also a need to decide among them which nodes should keep
which content to avoid redundant caching as well as different
cache replacement policies. Once the content is cached in the
network, the question of How to retrieve cached content? also
needs to be addressed.
To address the aforementioned questions, We present a So-
cially Aware Vehicular Information-centric Networking model
(SAVING) encapsulating them into three classes; Computing,
Caching and Communications (3Cs), where mobile nodes
such as vehicles with their intrinsic processing, storage and
communicating capability can “Compute” their eligibility to
“Cache” and “Communicate” with each other to facilitate
efficient content delivery in a content-centric mobile network.
We define a new notion of the computing class where a
mobile node compute its eligibility to be selected as an
important information hub in order to cache content. Similarly,
the caching class incorporates all the questions regarding the
content popularity and availability in the network including
different cooperative caching schemes once the nodes compute
their social importance in the network. The communication
class involves different content distribution protocols where
nodes communicate to retrieve the cached content from the
important information hubs in the network.
SAVING presents a new concept of finding important
vehicles as a ranking system comprising three novel centrality
schemes, InfoRank [4], CarRank [5] and GRank [6]. Each ve-
hicle first classify different cached information using InfoRank
based on its popularity, availability and timeliness with respect
to the user interest. The vehicle then autonomously computes
its relative importance in the network using CarRank and
GRank. CarRank allows a smart vehicle to rank itself based
on its popularity with respect to the user interests, its spatio-

Figure 1: SAVING System Overview
temporal availability and its neighborhood connectivity as local
vehicle eligibility metric. GRank considers the information
reachability in an urban environment beyond local importance
by allowing a vehicle to consider itself as a global “city-
wide”information hub to cache content in the network. Finally,
we propose a social content distribution protocol where the
novel vehicle centrality schemes are deployed to relay and
retrieve cached content in the network.
The remaining of the article is organized as follows, the
next section provides an overview of SAVING following by
the description of Computing class in Section III discussing
the ranking system to identify important information hubs as
new trend of autonomous computing by smart vehicles. Section
IV describes the Caching class explaining different criteria for
to classify content. In Section V, we discuss Communication
class describing the social content protocol as an example. The
Section VI discuss the Performance evaluation describing the
results from each class. Section VII concludes the article along
some future insights.
II. SAVING SYST EM OV ERVI EW
SAVING aims to provide a novel concept of distributed
content caching and distribution framework to complement
infrastructure network for urban mobile users in order to max-
imize content availability with minimum delays. The named-
data networking concept introduced by the information-centric
networking paradigm is capable to co-exist with the mobility
and intermittent connectivity challenge in mobile networks.
ICN inherent in-network caching and provider-consumer de-
coupling maximize content availability by allowing users to
retrieve content cached at “any” near-by source independent
of the underlying network connectivity. We propose below an
ICN enabled SAVING system by describing a use case for the
location-aware content caching and distribution in an urban
environment.
A. Use Case: Location-aware Information Sharing
We consider the case of location-aware content where
interests for information regarding available parking lots, traf-
fic/weather conditions, fuel prices, virtual tours to local attrac-
tions or snapshots/videos of nearby resort areas are generated
by applications targeting vehicles in a given area, regardless of
their IP address. To address this, SAVNG comprises a publish-
subscribe ICN model allowing a mobile node such as a vehicle
in our case to subscribe for the following three roles:
1) Information Provider: An information provider vehicle
acts as the content source to publish content. For example, it
can subscribe itself to publish sensory information collected
from urban streets using the vehicle embedded cameras and
sensors.
2) Information Facilitator: Vehicle responsible to collect,
cache and relay data generated by information provider ve-
hicles as well as forwards the user interest for content to
“facilitate” efficient content caching and distribution.
3) Information Consumer: The vehicle subscribed to
request different content from the information facilita-
tors/providers with in the vehicular network are considered
as information consumers to “pull” content in an information-
centric vehicular network.
The three distinct roles are defined since certain vehicles
can be subscribed only as consumers or providers, therefore
not participating to facilitate other subscribers in the network.
Each ICN enabled vehicle maintains three routing parameters:
•Forwarding Information Base - FIB: It resembles a
routing table which maps content name components
to interfaces. Each vehicle FIB is populated by the
routes discovered using our proposed centrality-based
interest/data forwarding protocol.
•Pending Interest Table - PIT: It keeps track of all the
incoming interests that the vehicle has forwarded but

not satisfied yet. Each PIT entry records the content
name carried in the internet, together with its incoming
and outgoing interface(s).
•Content Store - CS: It is a temporary cache to store
content each intermediate vehicle has received while
forwarding content. Since a named-data packet is
meaningful independent of where it comes from or
where it is forwarded, it can be cached to satisfy future
interests.
An overview of the proposed SAVING system is illustrated
in Figure 1. The “User Vehicle” plays the role of a consumer
vehicle interested for information regarding a location in
particular zone, assuming the city is divided into different
urban zones following lets say, an ICN hierarchical naming
convention. It forwards the interest to an information facilitator
vehicle in range which subsequently facilitate by caching
and providing the desired content. The “Source vehicle” acts
as the information provider by providing the content to the
information facilitators responsible for the content delivery in
the network.
III. COMPUTING - WH ERE TO CACHE ?
The identification and selection of suitable vehicles to
cache content among the fleet of thousands vehicles poses
an economic and bandwidth challenge along the inherent
issue of mobility and intermittent connectivity. The challenge
exists in finding the right set of vehicles available at the
right time and place for efficient data collection, storage and
distribution through low-cost inter-vehicle communications.
We believe that of all the vehicles, only a set of appropriate
vehicles can be considered important based on their daily
commute while considering the popularity of its frequently
visited neighborhoods. A vehicle can consider an information
or location as popular if it observes an increase in the number
and frequency of user interests for the associated content.
We define a novel concept of computing by allowing
mobile nodes with sufficient processing, storage and com-
munication capabilities to perform autonomous computing. A
ranking system is presented as an example of such autonomous
computing where the mobile nodes decides its user relevant
importance in the network. Thus, we address the question
of where to cache by identifying important mobile nodes as
distributed information hubs in the network.
A. Information Hubs Identification
The self decision making of mobile nodes is leveraged to
identify mobile nodes, important to declare themselves as in-
network information hubs. To do so, we propose two ranking
schemes, CarRank as a local vehicle centrality and GRank,
a global vehicle centrality scheme allowing vehicles to rank
themselves independent of the infrastructure network. Each
vehicle finds its centrality Cvat the time instant tk+1 from
the known information in the current time-slot, where tkis
the time instant at the beginning of the time-slot tk.
1) Local Information Hubs - CarRank: In the time evolving
vehicular network topology, it is non-trivial to use the vehicle
contact frequency and duration to decide its importance due
to the rapid changes . To overcome this, we propose CarRank
which simultaneously considers three novel albeit essential
parameters, the information importance, the vehicle spatio-
temporal availability and its network connectivity. The user’s
interest satisfaction for a content is also considered as a key
metric for a vehicle’s importance as it regularly responds to
user interests. We integrate the social-awareness paradigm by
allowing vehicles conform to large number of user interests
for content in the network. The interests are assumed to be
generated and received from the neighboring vehicles using
multi-hop interest forwarding. We consider the following local
parameters known to the vehicle for analytically finding its
importance:
•Information importance: Information importance mea-
sures vehicle relevance to users for a particular con-
tent, i.e. The interest-response frequency is a vital
factor to classify a content’s importance. A vehicle
associated to contents related to popular locations is
considered as an important information hub in the
network.
•Spatio-temporal availability: It reflects the social-
behavior based on the vehicle’s habitual routes as
a factor of the daily commute. Spatial availability
reflects the vehicle’s recursive presence in an area,
while temporal availability refers to its relevance in
time for a location.
•Neighborhood importance: Neighborhood importance
shows vehicle topological connectivity in order to be
capable to distribute information. An easily reachable
and well connected vehicle in a network topology can
act as an efficient facilitator.
The vehicle ranking algorithm “CarRank” is used for the
identification of information facilitator vehicles to find the
vehicles responsible as information hubs in the network. The
vehicle first classify the information associated to it taking
into consideration the relevance to the users interest. It then
considers the associated information popularity to find its
relative importance in the network using CarRank algorithm
as its vehicle centrality:
LCv(tk+1) = θ×LCv(tk) + (1 −θ)×[αf v
I(tk+1)
+βf v
T,X (tk+1 ) + γf v
Γ(tk+1)] (1)
where fv
I,fv
T,X and fv
Γare the importance functions for the
information, vehicle spatio-temporal availability and vehicle
neighborhood respectively. Each function’s contribution is nor-
malized by the terms α, β and γ, where α+β+γ= 1, where
θ∈[0,1] allows the vehicle to increase its importance with
respect to the previous time-slot. The impact of each parameter
differs with respect to different applications. For example, if
the vehicle is located in a better connected neighborhood, it can
easily spread information. Therefore, the corresponding vehicle
weights the information importance along the neighborhood
more than the spatio-temporal availability.
2) Global Information Hubs - GRank: Inspired from the
concept of communicability in complex networks [7], GRank,
a global vehicle centrality scheme allows a vehicle to use a
new stable metric named “Information communicability” to
rank different locations in the city and rank itself accordingly.
Using GRank, the vehicle finds each location reachability and

popularity taking into consideration the user interest satisfac-
tion related to the location. It also considers its mobility pattern
between different locations in the city along its availability
in each location. Vehicles available in popular locations in
the city qualify as important information facilitator vehicles
with higher vehicle centrality score in the network. We can
identify popular locations in the city with the maximum global
centrality with respect to all information facilitators. However,
popularity of locations depends on several factors such as the
information-type depending on the application requirements as
well as time of the day. Similarly, we can use the maximum
location importance to identify popular neighborhoods for a
longer time span.
The vehicle centrality function at the time instant is given
as the average information global centrality for all associ-
ated locations. For a vehicle, the vehicle global centrality
GCv(tk+1)for the next time instant tk+1 is updated as the
Exponential Weighted Moving Average (EWMA) function of
the current and previous global centrality as shown in the
relation below:
GCv(tk+1) = θ×GCv(tk) + (1 −θ)×fv
G(tk+1),(2)
where θ∈[0,1] is the tuning parameter which allows the
vehicle to adjust its importance with respect to the previous
time-slot, GCv(tk)is the vehicle global centrality at the
beginning of the current time-slot and fv
G(tk+1)is the vehicle
global centrality computed at the end of the current time-slot.
The difference between both CarRank and GRank can be
explained by the fact that each interest specifies two satisfac-
tion deadlines Imax and Imin, where Imax ≥Imin indicates
the maximum and minimum threshold time to provide the
corresponding content. Thus in case the interest cannot be
satisfied by a local facilitator vehicle (CarRank based) in the
vicinity by an initial threshold Imin, the interests can be
forwarded to more globally central vehicles (GRank based)
till Imax; the maximum interest deadline to avoid bandwidth
and time utilization.
Algorithm 1 CarRank
INPUT: Information association graph G(V,X,E) :
OUTPUT: Cv(tk+1): Updated CarRank for the next time-
slot tk+1
for each vehicle v∈Vdo
for each associated content x∈Xvdo
Compute associated information importance
Compute mutual information with respect to the
content
end for
for each neighbor vehicle Γv∈Vdo
kv
Γ←average neighbor degree
Cv
Γ(tk)←neighbor centrality
end for
Find spatio-temporal availability
Compute neighborhood importance
Update vehicle centrality
end for
return LCv(tk+1)
Algorithm 2 GRank
INPUT: G(V,X,E) : information association graph , Infor-
mation global centrality, GRank in previous time-slot
OUTPUT: Updated GRank for the next time-slot at time-
instant tk+1
for each vehicle v∈Vdo
for each associated location xi∈Xvdo
find information communicability Cv
xixj,∀xixj∈X
for each vehicle neighbor Γv∈Vin range do
receive neighbor communicability CΓv
xixj, neigh-
bor centrality CΓv,
end for
compute neighbors communicability function fΓv
xi
find information centrality function fv
xi, then loca-
tion importance ρv
xi
compute information global centrality Gv
xi
end for
compute fv,Cv
end for
return GCv(tk+1)
IV. CACHING - WH AT AND HOW TO CACHE?
In this section we discuss a novel approach for content
cache management by classifying the cached content impor-
tance with respect to the intended user. Lets assume vehicles
encountering each other in a vehicular network constantly
receives interests for content from neighboring vehicles re-
garding different information. Some of such information can
be of more importance to the intended users in the network
which the vehicle can easily recognize from the amount of user
interests received for it. Therefore, a vehicle can consider an
information popular if it observes an increase in the number
of user interests for the associated content. We assume that
it is capable of recording the time and position each time
it responds with the desired content to a user interest. Thus,
SAVING incorporates a novel distributed algorithm InfoRank
with the concept of enabling a mobile node to rank important
information associated to it based on the satisfied user interests
and the information validity scope.
A. Interest Satisfaction Frequency
We define interest satisfaction frequency as the frequency
of user interests satisfied in the previous time-slot as the ratio
of the number of successful responds in the previous time-slot
and the total successful responds for the content associated to
the vehicle. Thus, the vehicle regularly updates each content
importance value depending on the interest satisfaction fre-
quency. We assume that each vehicle is capable to record the
time and position each time it responds as the content provider
to a user interest. Interest for each content specify the temporal
scope of information validity, For instance, road congestion
information is only valid during congestion. Therefore, it
should be ensured that the information importance is not
substantially augmented after the desired deadline.

B. Information Timeliness
The information timeliness τis the measure of the tem-
poral information validity scope which can be adjusted by a
tuning parameter depending on the application needs (E.g. 1
hour for accident information validity). If there are no active
interests and the average interest validity time has passed, the
information importance adapts an exponential delay since the
information is of less importance in the network. However, τis
set to unity for content to be always available in the network.
The content importance depends on its importance at the
beginning of the time-slot. If it is not responded in the previous
slot, then the content importance is not increased unnecessarily.
We also consider the percentage of time the vehicle itself acted
as the original source for any content. InfoRank is updated
regularly to ensure the content relevant to vehicle retain its
value in case the vehicle does not respond in the previous
slot. The interest later in time could finally route to the vehicle
which maintains its value as the original source for particular
content. A tuning parameter decides the importance value with
respect to the associated content in cache. For all contents
associated to a vehicle, we also consider the ratio of missed
interest to the total interests received by the vehicle. Missed
interest provides the vehicle reliability regarding successful
respond to the incoming interests.
To summarize with an example, Assume a vehicle visiting
an area at some future time-slot place an interest for the
content regarding that area. This interest is propagated to
potential facilitator vehicle. Each vehicle upon receiving the
interest message checks its cache to find a match regarding
the desired content. In case the interest could not be satisfied,
it is forwarded to neighboring vehicles. In case a match is
found, it responds to the interest message by providing the
corresponding content where each vehicle compute its cached
information importance by finding its respective InfoRank
score. Once the information importance is agreed between
different facilitators, collaborative caching between nodes (i.e.
P2P networking) can be ensured by mutually respecting a
social norm to avoid redundant content caching in the network.
V. COMMUNICATION - HOW TO RET RI EV E?
In this section we discuss the efficient retrieval of the
cached content in the network by focusing on the communica-
tion aspect. For this reason, we present an idea of social aware
content caching and distribution scheme where the consumer
“pulls” content of interest cached at important information
facilitator vehicles in the network. We use the above mentioned
two novel vehicle centrality schemes to identify important
information facilitator vehicles based on cache management
for content suggested by InfoRank scheme.
A. Content Distribution Protocol
The centrality-based content distribution protocol leverage
the facilitator centrality to forward consumer interests for
content as well as route the content from the corresponding
information providers. The provider as well as the consumer
search for a near-by information facilitator vehicle using its
centrality score to forward interest/content. We propose a
hybrid content distribution protocol with an ICN inherent pull
Figure 2: Facilitator Discovery Process
based content retrieval for the consumer and a push based
approach for the provider to publish content.
Information Facilitator Discovery: The facilitator discov-
ery process allows a vehicle to search in its vicinity the
highest centrality facilitator vehicle using the FACILITATOR()
function. It compares the facilitator centrality score of all the
neighboring vehicles and returns the best facilitator central-
ity vehicle among the vehicles in range for a vehicle. The
PROVIDER() function assigns a vehicle to be the provider
vehicle to publish the content for the consumer vehicle. The
publishing of content by the provider can be either solicited or
non solicited. In the case of solicited interest, the provider can
publish content destined for the vehicle with a near-by infor-
mation facilitator using PUBLISH() function. Similarly, a non-
solicited publish with a near-by facilitator can be performed by
an information facilitator discovery process initiated anytime
by the information provider. The CONTENT() function is used
for the content availability check at each intermediate vehicle
Content Store (CS).
Figure 2 depicts the social content distribution proto-
col. Consumer vehicle vgenerates interests for content as
INTEREST() towards the best ranked facilitator in the vicin-
ity. The facilitator discovery process continues to search for
the content at each intermediate relay vehicle by constantly
discovering the next best ranked vehicle in the vicinity of each
intermediate relay vehicle. Thus, each relay vehicle becomes
the responsible vehicle to facilitate the content. If it is unable
to find the content in its CS, it performs a facilitator discovery
to find a vehicle with higher facilitator centrality score and a
Pending Interest Table (PIT) entry is created. The process is
repeated at each intermediate facilitator till either the desired
content is found or there are no more facilitators to discover.
The convergence of the facilitator discovery process is two-
fold, the first obvious convergence occurs when the desired
content is available at the corresponding facilitator. In this case,
the content is published at the consumer vehicle following
a reverse path to the initial requester using breadcrumbs
left in the PIT at each intermediate node. The intermediate
vehicles subsequently populates the corresponding Forwarding
Information Base (FIB) entry for the content. In case the
content is not available and there are no further facilitators to
discover, the responsible vehicle declares itself as the content

provider to publish content at the consumer vehicle.
VI. PERFORMANCE EVALUATIO N
The performance of the SAVING is validated by a set of
simulations under a realistic mobility scenario using traces
from Cologne, Germany as an accurate mobility trace available
for Vehicular Environment [8]. The number of vehicles in each
region vary at different time of the day. We analyze up to 2986
vehicles in the entire simulation duration with one second of
time granularity. The Cologne city center is simulated for one
hour by clustering the 6x6km2The number of regions can
vary between different cities depending on the size, though we
divide Cologne into 36 neighborhoods. The urban roads with
vehicle communication range around 300m is considered. The
Nakagami path loss model in combination with Log-distance
propagation model to cater for the impact of buildings and
other obstacles.
A. Simulation Scenario
We simulate a urban vehicular network using the ndnSIM
(http://named-data.net/techreports.html) to integrate the Named
Data Networking (NDN) communication model. The simula-
tion scenario implements the following applications:
1) Consumer: Consumer vehicles are the potential users
planning to visit an area. Each consumer vehicle generates an
interest for a content associated to a location in the city, which
is routed to provider vehicles.
2) Provider: We define a vehicle to be the content provider
in the network for the areas visited in a time-slot before
the consumer interest generation time. The areas visited are
considered as locations associated with the provider.
3) Facilitator: Vehicles satisfied incoming requests gener-
ated form consumers regularly computes their centrality score
to consider themselves as information facilitators. Similarly,
constant content forwarding and cache hits also counts towards
the facilitator centrality score.
We associate each vehicle with a set of different location-
dependent content as its cached content. Each vehicle is
enabled to randomly generate interest with varying frequency
at different time intervals for different (predefined) content
as consumer. The interest profile characteristics is two-fold.
First we evaluate an information using InfoRank considering
its popularity based on the number, frequency and spatio-
temporal validity of generated interests for the content. Then,
considering the cached content importance, we imply our
ranking scheme CarRank and GRank to evaluate the interest
profile for the associated vehicle.
We assume the interests follow a Zipf distribution, where
we observe frequent interests for content regarding popular
locations.
B. Simulation Results
For better performance analysis of the proposed SAVING
system in different simulation scenarios, we compare it with
the state of the art social-aware routing schemes. Such schemes
typically rely on centrality schemes such as Degree, Closeness,
10 20 30 40 50 60
0
10
20
30
40
50
60
70
80
90
100
Time (minutes)
Success Rate (Percentage)
CarRank+GRank
Degree
Closeness
Betweeneess
Eigenvector
Figure 3: Success rate conparison for the consumer interests
satisfied over time using different centrality schemes for con-
tent distribution
10 20 30 40 50 60
Time (minutes)
0
10
20
30
40
50
60
70
80
90
100
Average Success Rate (Percentage)
CarRank+GRank
MS-LOR
Bubble-Rap
Social-Unaware
Figure 4: Success rate comparison of SAVING for the con-
sumer interests satisfied over time withs social-aware DTN
schemes and a social-unaware variant for content distribution
Betweenness and Eigenvector centrality. Therefore, we per-
form a comparative analysis of the proposed vehicle centrality
based routing with the benchmark centrality schemes with the
following performance metrics:
•Success rate for satisfying consumer interests in the
network
•Aggregated content store (cache) hit rate at the infor-
mation facilitators
1) Success Rate: Success rate refers to the percentage of
the generated consumer interests successfully satisfied over the
entire simulation duration.
(a) Benchmark Centrality Schemes Comparison: The
proposed vehicle centrality based content distribution is com-
pared with the state of the art centrality schemes as benchmark.
Figure 3 shows the percentage of consumer interests for
different locations successfully satisfied by the corresponding
information facilitators/providers. We observe that forwarding
the interest towards a socially important vehicle using CarRank

10 20 30 40 50 60
0
10
20
30
40
50
60
70
80
90
100
Time (minutes)
Cache Hits (Percentage)
CarRank+GRank
Degree
Closeness
Betweeneess
Eigenvector
Figure 5: Comparison result of the percentage of the cache hits
over time at the information facilitators selected using different
centrality schemes for content distribution
and GRank as a metric results in more number of successful
interest satisfaction. The vehicles identified by the proposed
vehicle centrality metric satisfied around 40% of interests
compared to other centrality metrics despite high mobility
and intermittent connectivity. It is because typical centrality
schemes only takes into account physical topology towards
computing a node importance in the network, ignoring the
satisfied user interests.
(b) Socially aware DTNs and Social Unaware Schemes
Comparison: We also compare the success rate of SAVING
with two relevant socially-aware DTN routing schemes MS-
LOR [9] and Bubble-Rap [10] as well a variant without
considering social awareness. BubbleRap uses a hybrid metric
based on community and betweenness centrality where MS-
LOR uses a three-layer social metric based on degree central-
ity. The social-unaware approach implements interest flooding
in which each consumer re-broadcasts interest to all of its
neighbors except the one from which it received. Figure 4
depict the results from the comparative analysis. We observe
that using CarRank and GRank based routing yield a suc-
cess rate around 40%-50% where social-aware DTN schemes
achieve a maximum of 38% at 50 minutes from ML-SOR.
An interesting observation made is the stability of SAVING
unlike other schemes. It is because thy fundamentally rely on
centrality measures which assume a static graph topology with
respect to time. Moreover, the host-centric nature instead of
content-centric approach with no in-network caching support
limits DTN capability to maximize content availability. Thus, a
content distribution based on adapted metrics (such as CarRank
and GRank) better cope with the dynamic nature of vehicular
network.
2) Cache-hits: We evaluate the ICN built-in feature of
in-Network caching at intermediate nodes at the selected
facilitator vehicles. For this purpose, we compute the cache hit
rate at the facilitator vehicles. A second successful response by
a vehicle for the same content is considered a cache hit. The
cumulative cache hit rate is computed for the entire simulation
duration. Figure 5 shows the cache hit rate for the facilitator
vehicles identified by each centrality scheme. The vehicles
identified by our proposed vehicle centrality scheme yield a
higher hit rate than all the other schemes. This is because we
consider content popularity as a key factor, thus, the vehicle
containing important information responds and subsequently
cache more frequently compared to other vehicles.
We also observe that the vehicles identified using betwee-
ness centrality follows our proposed scheme yielding better
cache hit rate due their frequent availability as intermediate
bridges at most of the shortest paths, thus allowing them to
cache more content. Moreover, the intermediate facilitators
identified by our vehicle centrality scheme cached more impor-
tant content due to their better neighborhood connectivity and
spatio-temporal availability in the network. This proves that in-
network caching offered by ICN along the proposed vehicle
centrality scheme overcomes the mobility and intermittent con-
nectivity constraints in vehicular network for efficient content
distribution.
VII. CONCLUSIONS AND OPE N RESEARCH ISSUES
We proposed SAVING as an alternate solution to leverage
smart vehicles with their caching, computing and communi-
cating capabilities to facilitate content availability for an urban
mobile user with minimum content access delay. SAVING is a
socially-aware Vehicular Information-centric system focusing
the research community interest towards the application of
combining socially aware content distribution scheme with the
information-centric networking paradigm. We explored possi-
ble answers to the fundamental questions of where, what and
how to cache content in mobile networks under an increasing
growth of mobile traffic. Moreover this article highlighted
another perspective by equally considering the efficient content
retrieval by caching at vehicles. The suggestion for vehicles
can also be generalized for all sort of mobile nodes depending
on the node computing, caching and communication (3Cs)
capabilities.
Open research issues include efficient social aware routing
strategies, flexible and scalable naming scheme for novel
applications and the possibilities to support high bandwidth
consuming content video streaming in content-centric network-
ing paradigm. However each of the 3Cs still lacks explo-
ration by the current research requiring intelligent algorithms
for nodes to make real-time decisions regarding the cached
content. Similarly the need for distributed cache management
schemes with collaborative content replacement strategies with
redundancy avoidance for the daily massive content generated
needs to be thought about. Thus, we invite the research
community to explore the new trends exploiting socially-aware
network computing, caching and communication in a content-
centric approach to overcome the limitations of the existing
connection-centric approach.
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