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Popularity Prediction Caching Using Hidden
Markov Model for Vehicular Content Centric
Networks
Lin Yao∗, Yuqi Wang†, QiuFen Xia∗, and Rui Xu‡§
∗International School of Information Science & Engineering, Dalian University of Technology, China
†School of Software, Dalian University of Technology, China
‡Cyberspace Security Technology Laboratory of CETC, Chengdu Sichuan, China
§China Electronic Technology Cyber Security Co., Ltd, Chengdu Sichuan, China
Abstract—Vehicular Content Centric Network (VCCN) is pro-
posed to cope with mobility and intermittent connectivity issues
of vehicular ad hoc networks by enabling the Content Centric
Network (CCN) model in vehicular networks. The ubiquitous
in-network caching of VCCN allows nodes to cache contents
frequently accessed data items, improving the hit ratio of content
retrieval and reducing the data access delay. Furthermore, it can
significantly mitigate bandwidth pressure. Therefore, it is crucial
to cache more popular contents at various caching nodes. In
this paper, we propose a novel cache replacement scheme named
Popularity-based Content Caching (PopCC), which incorporates
the future popularity of contents into our decision making. We
adopt Hidden Markov Model (HMM) to predict the content
popularity based on the inherent characters of the received
interests, request ratio, request frequency and content priority.
To evaluate the performance of our proposed scheme PopCC,
we compare it with some state-of-the-art schemes in terms of
cache hit, average access delay, average hop count and average
storage usage. Simulations demonstrate that the proposed scheme
possesses a better performance.
Keywords—Popularity Prediction, Hidden Markov Model, VC-
CN
I. INTRODUCTION
Though Vehicular Ad-hoc Network (VANET) can provide
entertainment and road safety services to drivers and passen-
gers, it has difficulties in maintaining end-to-end connections
due to its dynamic network topology and harsh propagation
environment [1]. Therefore, the information communication in
VANET is becoming challenging. IP-based host-centric proto-
cols work awkwardly in mobile environments and functional
patches such as mobile IP have been proven to cause increased
complexity and performance degradation of VANET [2]. CCN
is concerned with contents, not the actual carriers of the con-
tents. It can overstep the inefficiencies of TCP/IP in handling
the node mobility, unreliability of wireless links, and resource-
constrained devices [3]. In particular, the network capacity
becomes gradually limited with the increasing of vehicles. The
in-networking caching property of CCN allows each vehicle
to retrieve contents from the nearby providers.
Recently, Content Centric Network (CCN) has been advo-
cated to address the mentioned challenges and design the next
generation vehicular network called Vehicular Content Centric
Network (VCCN) [2]. A node requests a content by sending
an interest packet consisting the content name. The data
packet will be returned if any other node has a replica of the
corresponding content. Each node forwarding the data packet
can decide whether to cache it based on its cache replacement
strategy. This intra-network caching allows multiple contents
to be distributed through the VCCN to improve the network
performance.
Different from most caching strategies in CCN, a data
packet in VCCN might be returned by a different path instead
of the reverse path of the corresponding interest due to
the vehicle mobility. Moreover, the store-and-forward routing
may bring longer delay on remote access of information if
the requested content is cached at a node farther from the
requester. Due to the high cost of deploying RSUs and their
relatively limited storage capacities, it is impossible to store
all contents on RSUs. In order to reduce communication cost
among nodes, minimize bandwidth usage, reduce access delay
and improve hit ratio, each vehicle or RSU has to potentially
cache contents which are frequently accessed by other nodes.
Obviously, the network performance can be improved if the
requested content can be retrieved from the most convenient
providers.
In this paper, we consider taking advantage of the idea
of cache eviction and present an efficient cache replacement
policy, Popularity-based Content Caching (PopCC). We incor-
porate the future content popularity into our caching decision,
which is predicted with Hidden Markov Model (HMM) by
learning the past traffic patterns. Contents are cached in the
descending order of content popularity. We aim to cache more
popular contents in each node to achieve a higher performance.
Given all the above considerations, this paper has the following
contributions:
•To the best of our knowledge, we are the first to adopt the
idea of future content popularity to design and implement
533
2019 20th IEEE International Conference on Mobile Data Management (MDM)
2375-0324/19/$31.00 ©2019 IEEE
DOI 10.1109/MDM.2019.00115
a cache replacement policy for VCCN. Using the fore-
casted popularity, PopCC makes proper cache replace-
ment decision to cache contents in the descending order
of content popularity. When the buffer is full, contents
with lower popularity will be evicted automatically.
•We are the first to learn the recent access patterns of
interests to get the content popularity. To predict it more
accurately, our PopCC integrates request ratio, request
frequency and content priority to calculate the popularity
of an object with a general function. To compute the
content priority, we adopt TF-IDF algorithm [4] and
K-means [5]. TF-IDF algorithm is used to weight the
content names to establish the vector space model and
then K-means is used to cluster the content names to get
the content priority.
The remainder of this paper is organized as follows. In
Section II, we discuss the related work. Problem statement
is given in Section III. In Section IV, we present the details
of our approach. We evaluate the performance of PopCC in
Section V, and conclude the work in Section VI.
II. RELATED WORK
In this section, we review some literature works related to
our proposed method.
Cache Eviction Policies- Polices based on cache eviction
mainly aim to remove contents when new contents are received
due to the limit caching space. Cache eviction policies can
be classified into recency-based ones, frequency-based ones,
recency/frequency-based ones, randomized ones and function-
based ones [6]. In [7], Least Recently Used (LRU) as the
typical recency-based strategy and Least Frequently Used
(LFU) as the typical frequency-based strategy were proposed,
which consider the recently access time and request counts
respectively. TLRU [8] was proposed by combing recency and
frequency. Though recency and frequency information can be
obtained easily from the past Interests, the cache replacement
policies based on these fixed factors lack flexibility and are
difficult to meet the requirements of different workload situ-
ations. Randomized approaches aim to reduce the algorithm
complexity by finding a random content object for replacemen-
t. In [9], Zhou et al. proposed a random replacement policy by
efficiently approximating the hit probability in linear time with
moderate space in a single round. The above algorithms are
easy to implement but they ignore a fact that more popular
contents tend to stay longer in the cache, causing them to
suffer from major performance degradation [10].
Function-based approaches can solve the above challenging
by considering multiple factors to calculate the popularity of
an object with a general function. In [11], a cache replace-
ment policy, named as “Recent Usage Frequency (RUF)” was
proposed to deal with dynamic traffic patterns and alleviate
temporal traffic variations. The popularity is updated based
on the hit ratio and utilized time of each requested content.
PopCache was proposed to evaluate a content object’s Zipf
popularity and only contents with higher popularity were
cached in [12]. In [13], a value-based cache replacement
approach was proposed to calculate the popularity based on
access delay, frequency and aging time. In [14], the neural
network was first adopted to design a cache replacement policy
by integrating the cached contents longevity, frequency access,
the standard deviation of contents access frequency, the last
access to the content and hit ratio. [10] presented a cache
replacement method by incorporating the future content pop-
ularity into the caching decision. [15] proposed a centralized
control caching strategy based on popularity and betweenness
centrality.
VANET- A caching scheme for mobile ad hoc networks
(MANETs) was first proposed in [16], where the cross-layer
design is exploited to improve the caching performance with
the cooperative caching. The available cache replacement
mechanisms for ad hoc network are categorized into coor-
dinated and uncoordinated [17]. In uncoordinated schemes,
the replacement decision is made by individual nodes, while
neighbors cooperate and serve each other’s requests in co-
ordinated schemes. Mobility-aware replacement schemes [18]
were proposed to design the cache replacement by detecting
their movement patterns such as current location and moving
direction, which aims to cache more contents related to future
locations. The replacement policies in [19] predicted the future
regions based on users movement and stored the location
dependent data first.
To improve the network performance, the cooperative
caching seeks to coordinate the contents of each client’ cache
in VANET [20]. In [21], a cache replacement with content
popularity in VCCN was proposed to allow a cluster head
to make caching decisions cooperatively based on the past
popularity and the current cache hit of each content. The head
servers data to its members within the cluster. The LDCC
strategy in [22] applied a prediction model to approximate
client movement behavior such as the future location and
design a cooperative caching replacement. While, most of
other works focused on selecting caching nodes [23][24].
III. PROBLEM STATEMENT
In VCCN, each node contains three key data structures:
CS (Content Store), PIT (Pending Interest Table), and FIB
(Forwarding Information Base) in Table I. CS is used to
cache the contents forwarded by the node, including content
name, the corresponding content category, content priority and
content popularity. PIT records the interests that have been
forwarded but not yet replied. FIB is a table for routing the
incoming interest packets based on name prefixes. When an
interest is received, each node updates the arrival time and
discards it to avoid the duplicate storage if it has existed in
the PIT; Otherwise, it adds an item including the content name
and arrival time into its PIT. Then, it checks its own CS. If
the content is found in its CS, the corresponding data will be
replied; Otherwise, it forwards the interest if the request is
received for the first time. When receiving a data packet, each
node makes its own decision on whether to cache it or not
based on the caching replacement policy.
534
In this paper, our goal is to allow contents to be queried and
delivered efficiently among different nodes. As different ve-
hicles have different moving trajectories and receive different
interests, they may not cache the same contents. For example,
a vehicle node which usually drives nearby a shopping mall
may help others forward more interests on some promotional
information of goods. How to determine the popular contents
so that they can be accessed with the best efficiency? How
to exploit the recent access pattern to evaluate the content
popularity accurately and design the cache replacement policy?
TABLE I
DATA STRUCTURE MAINTAINED ATEACH NODE
(a) Content Store
Notation Description
name Content name
content Cached content
category Content name category
priority Content priority
popularity Content popularity
(b) Pending Interest Table
Notation Description
name Content name
time Time of the latest request
(c) Forwarding Information Base
Notation Description
name Content name
uids List of forwarding nodes
IV. POPULARITY-BASED CONTENT CACHING (POPCC)
In this paper, we aim to propose a Popularity-based Content
Caching (PopCC) strategy. We first give an overview of
PopCC, and then present our algorithm in details.
A. Overview
As vehicles move between infrastructures, each node includ-
ing RSU and vehicle always receives different interests from
different users. As usual, each interest has strong relationship
with the data consumer’s hobby, current location, etc.. For
example, one vehicle near a shopping mall may receive a large
number of requests on some coupons for certain goods during
some period. After this vehicle drives away, similar requests
will drop sharply. This example shows the popular contents are
always changing in each node. In order to react quickly to the
change of popular contents and provide better service for users,
we predict the content popularity by analyzing the inherent
statistics of incoming interests from the following parameters.
The frequently used notations are listed in Table II.
Definition 1 (Request Ratio): γ(ci)is defined as the ratio
between the number of interests for the same content ciand
that of all the requests received in a slice,
γ(ci)= n(ci)
ck∈C n(ck),(1)
where n(ci)is the number of requests for ci.
TABLE II
FREQUENT NOTATIONS
Notation Description
CThe set of content
ciThe i-th content in C
γk(ci)The request ratio of ciin the k-th slice
fk(ci)The request frequency of ciin the k-th slice
t(ci)The last time of receiving ci
ρk(ci)The content priority of ciin the k-th slice
PciThe popularity of ci
Definition 2 (Request Frequency): f(ci)is defined as the
average frequency of interests for ciin the past mslices.
f(ci)= 1
n(ci)n(ci)
k=1
1
t(ci)k+1 −t(ci)k
,
f(ci)= 1
mm
j=1(f(cj)),
(2)
where t(ci)k+1 and t(ci)kare respectively the time of the last
two requests and f(ci)is the frequency in the j-th slice.
Definition 3 (Content Priority): ρ(ci)is defined as the
priority of ciduring a slice,
ρ(ci)= Ni
NT
,(3)
where Niis the number of interests in the category which ci
belongs to, and NTis the total number of interest in all the
categories in the current slice.
Among the three factors, request ratio and request frequency
for the same contents are obtained from the statistics of incom-
ing interests. Content priority is calculated during the training
stage. And PopCC works with the following procedures:
1) During the training stage of HMM, each node makes s-
tatistics on the number of requests and contained content
name in each interest. Once the training is over, all the
statistics will be reported to the nearest RSUs. To ensure
the accuracy and reliability of training, all statistics need
to be synchronized between RSUs.
2) Each RSU is responsible for determining the content
priority defined in Eq. (3). Each RSU first adopts TF-
IDF to establish the vector space model of content
names. All the content names in the model are clustered
with K-means method. Based on the number of elements
in each cluster, the priority of contents in each cluster
is obtained.
3) During the prediction stage, each node calculates the
request ratio and request frequency for the same contents
according to Eq. (1) and Eq. (2).
4) Each node establishes HMM to calculate the future
popularity of each content based on the request ratio,
request frequency and content priority. Then, contents
are cached in the descending order of content popularity.
If the cache space is full, the contents with lower future
popularity will be evicted.
535
B. Calculation of Content Priority
This module aims to give a brief introduction on how to
obtain content priority, which works in the following two
procedures:
1) Getting VSM with TF-IDF:To analyze the collected
content names in C, we adopt an algebraic model, Vector S-
pace Model(VSM), to transform the text document containing
names into vectors of identifiers,
V(d)=((t1,W
1),(t2,W
2),(t3,W
3),··· ,(tn,W
n)),
where tirepresents the feature vector of ci,Wirepresents the
weight of ciin the text document dand V(d)is the vector
representation of a text document d.
Then, TF-IDF is adopted to calculate calculate Wi.TF-
IDF, short for term frequency-inverse document frequency, is
a numerical statistic that is intended to reflect how important a
word is to a document in a collection or corpus. It is often used
as a weighting factor in searches of information retrieval, text
mining, and user modeling. TF measures how frequently a
term occurs in a document and IDF measures how important
it is. The TF-IDF value increases proportionally to the
number of times a word appears in the document.
Wi=TF
i×IDFi,
where IDFiis calculated as lg( N
ni+1 )with Nthe total number
of names and nithe number of texts including ciin d.
For multiple documents, the text vector need to be normal-
ized processing by the weight function,
Wik =TF
ik ×lg( N
ni+1 )
M
k=1(TF
ik ×lg( N
ni+1 ))2
,
where Wik represents the weight of the k−th feature vector
in diand Wi=(Wi1,W
i2,··· ,W
iM )with Mrepresenting
the number of different content names.
2) Clustering names with K-means: In this section, we use
K-means to cluster the content names in VSM and content
names with more similarities are clustered into one cluster,
sim(ci,c
j)= n
k=1(Wik ×Wjk)
(n
k=1(Wik )2)×(n
k=1(Wjk)2),(4)
where sim(ci,c
j)is the similarity between ciand cj.
We follow the steps below to complete the clustering:
Step 1: We first determine the number of categories (K)
based on the weight of the contents which is bigger than the
threshold ω, and set these corresponding content names as the
initial cluster centroids.
Step 2: For any other name except centroids, we calculate
the similarity between it and each cluster centroid by Eq. (4),
and assign it to the cluster with the highest similarity.
Step 3: We recalculate the mean of feature vectors in each
cluster and set it as the new cluster centroid.
Step 4:Steps 2 and Step 3 are repeated until there is no
feature vector left.
Step 5: After clustering is over, the content priority is
calculated according to Eq. (3).
C. Popularity Prediction
Each node adopts HMM [25] to predict the content pop-
ularity. All contents are cached in the descending order of
the predicted popularity. If the buffer is full, the contents
with lower future popularity will be evicted. First, several
terminologies are introduced:
Definition 4 (Content Popularity): P(ci)is defined as the
predicted popularity of ciaccording to recent access patterns.
Definition 5 (Factor Sequence): A factor sequence {F1,F2,
···,Fk,···} is made up of a series of triples, where Fkis a
triple γk(ci),f
k(ci),pr
k(ci)in the k-th slice.
By applying a Forward-Backward Algorithm to train HMM,
we can predict P(ci)with HMM by exploiting the recent
access patterns to generate the factor sequence. HMM can
be denoted by λ=(π, A, B), and the correlative variables are
introduced as follows:
π={πi}, the initial hidden state probabilities, where πi=
P(Si).
A={aij }, the transition probabilities between the hidden
states Siand Sj, where aij =P(Sj|Si).
B={bj(k)}, the probabilities of the observable states Ok
in the hidden state Sj, where bj(k)=P(Ok|Sj).
ζt(i, j)=P(Si,S
j|O1O2...OT,λ), the probability of transit-
ing from the hidden state Siat the time tto the hidden state
Sjat the time t+1, given the model λand the observation
sequence.
ηt(i)=P(Si|O1O2...OT,λ), the probability of the hidden
state Siat the time t, given the model λand the observation
sequence.
We can obtain the accurate model by calculating the correl-
ative variables in Eq. (5),
πi=ηt(i),
aij =T
t=1 ζt(i, j)
T
t=1 ηt(i),
bj(k)=T
t=1,Okηt(j)
T
t=1 ηt(j).
(5)
where πirepresents the expected number of times that the
content stays at the hidden state Siat t,aij represents the
probability of transition from Sito Sj, and bj(k)represents
the probability of observing the state Okwhen the hidden state
is Sj.
In PopCC, Fkrepresents the observe state Okin the k-
th slice and the predicted popularity in (k+1)-th slice is the
hidden state Sk+1.
V. P ERFORMANCE EVALUATION
In order to evaluate the performance of our caching scheme,
we conduct our simulations over the Opportunistic Network
Environment (ONE) simulator [26]. In our design, there are
300 nodes, including 30 RSUs and 270 users (50 people, 100
buses, and 120 taxis), distributed in the map. All nodes move
following the Working-Day-Movement Model in ONE with a
daily routine. A person drives a car with a chance of pv,or
536
s/he must take the bus or taxi to reach different destinations.
Buses follow the Route-Based Movement model and taxis run
by the Random Waypoint Model in ONE. All nodes have the
same caching buffer size, movement speed range, transmission
range and data rate. We list important simulation parameters
in Table III.
TABLE III
SIMULATION PARAMETERS
Parameter Description Value
Caching Buffer 1000MB
Request Interval [50minutes, 100minutes]
Message TTL 10minutes
Simulation Time 15weeks
RSU Number 30
RSU Transmission Range 500m
RSU Transmission Speed 10Mbps
Vehicle Speed [7m/s, 10m/s]
Vehicle Transmission Range 50m
Vehicle Transmission Speed 2Mbps
pv0.3
A. Performance Metrics
Our main comparisons are made between the proposed
PopCC scheme, and reference schemes TRLU [8], CRCP [21]
and PopCaching [10]. In PopCC scheme, we incorporate the
future content popularity into our caching decision, which is
predicted with Hidden Markov Model (HMM) by learning
the past traffic patterns including content priority, request
ratio and request frequency. Thus, we choose TLRU which
considers the past request time patterns and combines recency
and frequency to make replacement scheme. Meanwhile, we
choose the predictive caching scheme CRCP and PopCaching
as references. CRCP allows the cluster head to combine the
past popularity with the current cache hit of each content
so as to make decisions cooperatively. In PopCaching, the
method combining region partition and access pattern update
is designed to predict the content popularity. The following
metrics are used to compare these schemes:
•Success Ratio:The ratio of queries that successfully
obtain the requested contents.
•Average Access Delay:The average delay of obtaining
responses in successful queries.
•Average Hop Count:The average hop count between the
requester and provider in successful queries.
•Average Storage Usage:The average storage usage of all
the caching nodes in the network.
In the remaining sections, we provide a number of studies
to evaluate the performance of our proposed scheme.
B. Effect of Content Size
In Fig. 1(a) and Fig. 1(b), we compare success ratio and av-
erage access delay under different content size. As the content
size increases, each node caches fewer contents, causing lower
success ratio and higher access delay. Our PopCC has the best
performance with the highest success ratio (up to 10.62%
gain) and least access delay (up to 19.33% drop), because
10 20 30 40 50
Content Size(MB)
0.22
0.27
0.32
0.37
Success Ratio
PopCC
TLRU
CRCP
PopCaching
(a) Success Ratio
10 20 30 40 50
Content Size(MB)
1.2
1.5
1.8
2.1
Access Delay(hours)
PopCC
TLRU
CRCP
PopCaching
(b) Average Access Delay
10 20 30 40 50
Content Size(MB)
0
150
300
450
600
Storage Usage(MB)
PopCC
TLRU
CRCP
PopCaching
(c) Average Storage Usage
Fig. 1. Effect of Content Size
PopCC has taken into account request patterns to predict
the content popularity. Among the four schemes, TRLU only
considers the request recency and frequency without predicting
the content popularity, making it possess the least success
ratio and largest delay. CRCP shows better performance than
PopCaching on success ratio and average access delay when
the content size is larger than 40MB, because the amount
of cached content decreases with the content size. There is
relatively less impact on these two mechanisms, PopCaching
and CRCP, because PopCaching makes the replacement policy
based on the context learning mechanism and the cluster heads
in CRCP are responsible for caching contents. Fig. 1(c) shows
the effect of content size on average storage usage. PopCC
needs a slightly more storage space, because it stores more
interests to compute the three factors, while only the request
recency and frequency are necessary in TLRU. Though CRCP
and PopCaching predict the future popularity, they do not
require statistics for the past interests.
0.2 0.4 0.6 0.8 1
Caching Node Ratio
0.18
0.24
0.3
0.36
Success Ratio
PopCC
TLRU
CRCP
PopCaching
(a) Success Ratio
0.2 0.4 0.6 0.8 1
Caching Node Ratio
1.2
1.7
2.2
2.7
Access Delay(hours)
PopCC
TLRU
CRCP
PopCaching
(b) Average Access Delay
0.2 0.4 0.6 0.8 1
Caching Node Ratio
1.8
2.1
2.4
2.7
Average Hop Counts
PopCC
TLRU
CRCP
PopCaching
(c) Average Hop Counts
Fig. 2. Effect of Caching Node Ratio
C. Effect of Caching Node Ratio
In Fig. 2, we compare success ratio, average access delay,
and average hop counts among the four schemes under dif-
ferent caching node ratio. In Fig. 2(a), the success ratio of
the four schemes improves with the caching node ratio. As
discussed before, PopCC performs the best overall by combing
537
the request patterns to predict the content popularity. TLRU
does not predict the content popularity, causing the lowest
success ratio. Fig. 2(b) and Fig. 2(c) show that the average
access delay and average hop count decrease with the ratio of
caching nodes and PopCC outperforms the best.
100 1,000 10,000 100,000
Content Number
0.19
0.28
0.37
0.46
Success Ratio
PopCC
TLRU
CRCP
PopCaching
(a) Success Ratio
100 1,000 10,000 100,000
Content Number
1.2
1.6
2
2.4
Access Delay(hours)
PopCC
TLRU
CRCP
PopCaching
(b) Average Access Delay
Fig. 3. Effect of Content Number
D. Effect of Content Number
Fig. 3 shows the success ratio under different content
number. It can be seen that the success ratio decreases with
the number of contents, because a larger number of content
types increase the difficulty of cache hit. It shows that PopCC
always achieves the best performance among the four schemes
in terms of the success ratio and the average access delay
though the number of contents increases by a 100-fold.
VI. CONCLUSION
In this paper, we propose PopCC, a cache replacement
policy based on the popularity predicted for VCCN. Com-
bining the three content factors of frequency, ratio, and pri-
ority, PopCC adopts HMM to predict the content popularity.
We have presented our extensive simulations on the PopCC
scheme and compared its performance with several other
competing schemes. It has been shown that PopCC can gain a
better performance in success ratio and average access delay.
In our future work, we plan to optimize our algorithm to
minimize the energy consumption and design some incentive
policy to encourage each vehicle to actively caching content.
ACKNOWLEDGMENT
This research is sponsored in part by National Key Research
and Development Project of China (2017YFC0704100) and
the National Natural Science Foundation of China (contrac-
t/grant numbers: 61772113, 61872053, and 61802047).
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