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On the analysis of human mobility model for content broadcasting in 5G networks

On the Analysis of Human Mobility Model for
Content Broadcasting in 5G Networks
Chun Pong Lau, Abdulrahman Alabbasi, and Basem Shihada
Computer, Electrical and Mathematical Sciences and Engineering
King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
{lau.pong, basem.shihada}
School of Information and Communication Technology
Royal Institute of Technology (KTH), SE-100 44, Stockholm, Sweden
Abstract—Today’s mobile service providers aim at ensur-
ing end-to-end performance guarantees. Hence, ensuring an
efficient content delivery to end users is highly required.
Currently, transmitting popular contents in modern mobile
networks rely on unicast transmission. This result into a
huge underutilization of the wireless bandwidth. The urban
scale mobility of users is beneficial for mobile networks
to allocate radio resources spatially and temporally for
broadcasting contents. In this paper, we conduct a compre-
hensive analysis on a human activity/mobility model and
the content broadcasting system in 5G mobile networks.
The objective of this work is to describe how human daily
activities could improve the content broadcasting efficiency.
We achieve the objective by analyzing the transition prob-
abilities of a user traveling over several places according to
the change of states of daily human activities. Using a real-
life simulation, we demonstrate the relationship between
the human mobility and the optimization objective of the
content broadcasting system.
In the convention, random walk and random way-
point models are used in mobility modeling [1]. These
models follow the stochastic approach in moving direc-
tion, velocity, and independent with the previous status.
However, human mobility is far from being considered
random. It is known that people exhibit a high degree
of spatial and temporal regularity, following a simple
and reproducible pattern such as traveling between home
and work locations [2]. Xu et al. empirically studied
human mobility from the big data of a mobile network
in a metropolitan city with over 9600 cellular towers.
The results reveal that the human mobility has strong
correlations with the mobile traffic. Furthermore, the
mobility and the mobile traffic have regular patterns
and can be linked with social ecology [3]. Therefore,
studying of human mobility models become crucial in
data dissemination in various types of communication
networks. For example, in opportunistic networks, there
is no fix end-to-end path for transmitting data from a
source to the destination. Instead, the data are relayed
by the mobile nodes in a hop-by-hop fashion [4].
In order to reproduce synthetic realistic mobility
patterns close to reality, daily activities of the human
schedule are considered in the mobility models. Ekman
et al. proposed the working day movement model in
[5] by presenting the everyday life of average people,
such as sleeping at home, working in the office, and
evening activities. Issacman et al. proposed WHERE
which model large populations move with different
metropolitan areas from real-world probability distribu-
tions [6]. This model primarily generates synthetic traces
for the people moving between two places. It is scalable
to more locations but with introducing extra complexity.
Zheng et al. proposed the agenda driven mobility model
in [7] emphasizing the social role of a human for making
movement decisions.
The number of mobile subscribers has dramatically
grown during the recent years. The bandwidth demand
for popular media contents such as movies, TV shows,
games, and software updates continue to increase. It
poses a severe network congestion problem for content
delivery in the mobile networks [8]. In the future fifth-
generation (5G) networks, content providers would be
able to deploy their distribution algorithms through the
functionalities of software defined network (SDN) and
network function visualization (NFV) onto the core
network (CN) and the radio access network (RAN) [9].
In the SDN approach, a cloud-based software defined
controller (SDC) receives high-level services policies
from content providers and implements control signal in
the CN and RAN for radio resources allocation, content
distribution schedule, and cooperated broadcasting and
multicasting, such as researches in [10]–[12].
User mobility has been considered for caching and
content delivery system. Poularakis et al. proposed a
distributed approximation algorithm in [13] for deliver-
ing content in a femto-caching architecture. The femto-
caching architecture was proposed in [14] for offloading
the popular large size video contents onto femtocell-like
base station (BS). Lee et al. analyze human mobility
from traces of location-based social networks to develop978-1-5386-3531-5/17/$31.00 c
2017 IEEE
a method to deliver video data by moving people to
static kiosks [15]. Authors in [16] exploit the human
mobility patterns and social tie for caching contents in
the mobile devices for distribution through device-to-
device communication. To summarize, mobile networks
utilize these large-scale universal mobility patterns to
schedule broadcasting to deliver popular contents in the
crowded area for reducing the traffic congestion, radio
resources usage as well as the energy consumption.
Although the mobility models are widely adopted in the
content delivery systems, there is a lack of statistical
analysis in this area.
In this paper, we focus on analyzing the synthetic
human activity-based mobility models and demonstrate
how the future 5G mobile network could utilize this
information for massive content delivery. We describe
a typical model of human activity and mobility model
and the optimization objective function for a content
broadcasting system. The contribution of this work is
first to statistically evaluate the human activity mobility
model. We begin by deriving the probability of a user
starting an activity. Followed by the probability of a user
to end an activity. Then, connecting the probability of a
user being in a location at a given time to derived proba-
bilities. It followed by deriving the total expected number
of users within a location. Then, the content delivery
system exploits this information for the decision making
to achieve the optimization goal which is disseminating
a content to all of the subscribers with the least amount
of radio resources in an optimal timing. We support our
study by conducting a real-life simulation incorporated
with a real geographical location and realistic schedules
to demonstrate the human movement and the proper
timing for content distribution.
The rest of this paper is organized as follows. Sec-
tion II presents human mobility model and the content
broadcasting system in 5G mobile networks. The statis-
tical modeling is explained in Section III. Section IV
describes the details of the simulation setup and result.
Finally, the paper is concluded in Section V.
In this section, the human mobility model is described
followed by the efficient content broadcasting system.
A. Human Activity Mobility Model
The mobile users are categorized into different sets
of user groups G={g1, ..., gG}according to their
occupation, living habit, and behavior for generating
individual mobility traces with a degree of randomness,
while representing the realistic environment. The human
states mobility model makes use of the human daily life
routine for each user group which is composed of a set
of states V={v1, ..., vV}.The users within the same
user group have similar daily routines from the same
Symbols Descriptions
G={g1,..., gG}A set of user groups
V={v1,..., vV}A set of mobility states
uIndex of a user
viIndex of an activity state
viActivity of user uat state vi
viLocation of user uat state vi
viStaying Duration of user uat state vi
viUpper bound of the duration Dvi
viLower bound of the duration Dvi
viStarting time of user ubeing at state vi
viUpper bound of state starting range of vi
viLower bound of state starting range of vi
viEnding time of user ufinishing at state vi
vi,vj(t)Transition probability from vito vjat time t
B={b1,..., bB}A set of base stations
C={c1,..., cC}A set of contents
Nc,b,t Number of subscribers of content cin BS bat time t
tNumber of active cells of content cat time t
set of states with same activity stages and state starting
range, but different staying locations and durations.
Each state viconsists of the following components
for each user u, an activity stage Au
vi, a staying location
vi, a staying duration Du
vi, a state starting range and a
set of transition probabilities.
1) Activity Stage: An activity stage Au
viis the name of
a daily activity such as ‘Sleeping’ and ‘Working’. A se-
ries of activity stage forms a life routine for a user group.
Each user group has different sets of activity stages in
their corresponded mobility model. For example, a group
of office staff has a ‘Working in office’ stage while a
group of students has a stage ‘At school’.
2) Staying Location: A staying location Lu
viis ran-
domly chosen from a various set of places, depends
on the activity and the user group. There is static
and dynamic spatial information for a mobile user in
different states. For instance, home and work locations
are static. These places remained unchanged for a mobile
user in this model. On the other hand, the dining and
recreation locations are dynamic. The mobile user may
visit different places for dining and leisure on various
days. These locations are randomly chosen from a set
viof related locations within a reasonable distance.
The selection process of the location is independent of
the other state parameters.
3) Staying Duration: The staying duration Du
each state viis a random variable which following a
truncated normal distribution with a lower bound tDl
and an upper bound tDu
vi. Each state has individual mean
µDvi, variance σDvi, and truncation boundary for the
staying duration according to the activity stage and user
group. For example, the staying duration of a ‘Sleeping’
state of an adult may have a mean of 7 hours with a larger
variance whereas 10 hours for a child with a smaller
4) State Starting Range: The state starting range
consists of a pair of lower bound tSl
viand upper bound
vifor controlling the start of a state. It is independent
of the staying duration. A state starts if and only if the
starting time Su
viis within this range.
In addition to the components mentioned above, the
starting time and the ending time of a state for a user
are introduced. The ending time of a state is defined as
the sum of the starting time and the staying duration.
Let Eu
viis the ending time of user ustaying at the state
vi, and it is formulated as,
vi, (1)
where Su
viis the starting time of state viof user u. We
assume, the starting time of initial state Su
v1is a constant
at t=t0.
5) Transition Probability: The transition probability
vi,vj(t)is the probability of transiting from state vi
to vjat time t. It is a function of time depends on the
ending time of the current state i, the starting range of
state j, and a state selection probability ρi,j .
The transition from a state ito a future state jcan be
triggered when the ending time of the current state iis
within the range of the starting time in state j.
Studying the human mobility model is essential for
understanding the daily mobility patterns for content
broadcasting. In the following subsection, the content
broadcasting system is introduced to utilize the user
mobility information for making delivery decisions.
B. Content Broadcasting System in 5G Mobile Networks
We consider a mobile network that has a set of BS B=
{b1, ..., bB}deployed in a certain region. Let tbe the
time segment. A set of contents C={c1, ..., cC}arrive
at the system as a Poisson process. In the region, there is
a set of mobile users U={u1, ..., uU}. A subscriber is
a mobile user who subscribes to content which has not
yet been delivered. Let qu,c be a binary variable where
qu,c = 1 if a mobile user uis a subscriber of a content
c. Let qu,c,b,t be a binary variable where qu,c,b,t = 1 if a
subscriber of a content cis associated with BS bat time
segment t. The total number of subscribers of content c
in BS bat time tis equal to,
Nc,b,t =X
An active cell for a content is defined as a BS that has
at least a single active subscriber. The number of active
cells of content cat time tis denoted as ac
[Nc,b,t >0],(3)
where the notation [.] is the Iverson bracket. If the
condition in the square bracket is fulfilled, the number
is 1, while 0 otherwise.
An active cell ac
timplies that there is at least one
active subscriber of a content cwithin the cell coverage
area at time t. This definition can be changed as per
the network engineering demands. The constraint can
be relaxed through replacing the 0on the right side
of the inequality in (3) by a threshold. From the BS
point of view, if the radio reception levels of the mobile
users are good, broadcasting content in a cell to all
subscribers yield the most efficiency. It requires only a
single radio transmission instead of multiple duplicated
transmission comparing to unicast transmissions. From
the network point of view, perceiving a time with the
minimum number of broadcasting transmissions will be
the most efficient way to deliver a content, i.e., using
the minimum amount of radio resource for transmitting
to all of the subscribers. Therefore, our objective is to
search for a time segment tthat minimizes the number of
active cells ac
tfor delivering the content c. The original
problem is formulated as follows,
subject to tc
a< t < tc
where tis the time segment, which is an integer. The
variables tc
aand tc
eare the arrival and expiry time of
content c. The model parameters are summarized in
Table I. We model this problem by considering the
human activity states as random events; then we used
conditional probability theorems to find an expression
for the minimum number of active cells. This analysis
is provided in the next section.
The primary objective of the aforementioned delivery
system is to broadcast the content to all users with the
minimum number of transmissions, i.e., the minimum
number of active cells. For this purpose, we need to find
a time and optimal locations that have the maximum
number of subscribers for a content. Therefore, we
calculate the expected number of users at a time tin
location L.
The building blocks of these calculations start with
finding the probability of a user starting the state jat
time t, which can be calculated based on the staying
duration and starting time of that user at previous states.
We then find the probability of ending a state jat time
t. It follows that we incorporate the selection of location
lin the starting and ending time probabilities. Then, we
calculate these probabilities for all users, at all possible
time segments. Followed by finding the optimal time
which guarantees a minimum number of active cells.
A. Probability of starting a state at a given time segment
In order to calculate the probability of a user ustarting
a state jat time t, we have recognized five events which
contribute to the starting point, described as follows,
R: The ending time of previous state iis in between
time tand t1.
Z: The ending time Eu
viof previous state imust
be larger than the lower bound of the starting time
vjof the current state jand lower than the upper
bound of starting time tSu
X: For every previous state i, the staying duration
viis lower and upper bounded by tDl
viand tDu
Y: The current searching time t, must be lower than
the maximum staying duration in previous state i.
W: There is a positive transition probability from
state ito state j.
Event Rlimits the ending time of previous state iis
within the time segment tas follows,
vit. (5)
Event Zensures that the ending time of the previous
state iis within the range of starting time of the current
state j. It is formulated as,
Event Xdescribes that the staying duration Du
viis a
truncated random variable which is upper bounded by
viand lower bounded by tDl
vi, which is,
Event Yindicates the current time t, should be less
than the maximum staying duration of previous state i
in order to have a valid transition from state ito current
state j, which is formulated as,
Finally, event Wis a positive transition probability
from state ito state j. This probability excludes all of
the possibilities for transiting from previous state ito k
other than the current state j.
W:mvi,vj(t)= 1 X
i,j,k∈{1,...,V }
The probability of a user ustarting a state jat time
tis a combination of the aforementioned events, and it
is formulated as follows,
vj=t}= Pr{R, Z, X, Y, W }.(10)
The probability Pr{Su
vj=t}is the probability of
intersection between all events, X, Y, Z, W. Hence, we
can reformulated using conditional probability facts to
the following expression,
j∈{1,...,V }
Pr{R, Z|X, Y }Pr{X}I{Y}mvi,vj(t).
Note that event Y does not contain any random variable.
Hence, we express the intersection with event Y as
an indicator function, I(Y) = 1 when Y is true and
I(Y)=0when Y is false. It follows that using (6)-(9),
expression (11) is expanded as in (12).1
1Note that we convert the event Z from its original definition in (6)
to Z:tSl
vj.because we already bound Su
viby t1
and t.
B. Probability of ending a state at a given time segment
We calculate the probability of the ending time of
state vjat time tsince it is necessary to obtain the next
state probabilities. The ending probability of state vjis
formulated as follows,
vj=t}= Pr nSu
Pr nSu
vjoPr nDu
Pr nSu
vjoPr nDu
Then, we let nvj=tdu
vjand δvj= Pr{Du
vj}2. Expression (13) is reformulated as,
Pr nSu
Recall that the staying duration Du
vjat vjis independent
from the starting time Su
vjof vj. It follows that the
value of Pr nSu
vjois in similar form
of (12).
C. Probability of a user staying in a specific location
within a period of state
The joint probability of ending a state vjwith the
possibility of being at different locations is expressed
Pr nEu
vj=t, Lu
Pr nEu
where Lu
vjis the random variable that spans all possible
locations for the same state, i.e., Lu
vj. Recall our assump-
tion that the user selects the location of the activity in
state vjindependent from the starting and ending time of
that activity. Hence, the probability in (15) is calculated
as follows,
Pr nEu
vj=t, Lu
Pr nEu
Pr nSu
In similar lines, we link the probability of starting a
state vjat a time twith the set of locations Lu
vj, from
(12), as in (17).
D. Expected number of users at each time tin each BS
In this subsection, we calculate the expected value of a
number of users at specific time and location. We begin
by finding the probability of a single user ubeing in
location lat time t, using the probabilities found in (16)-
(17), as follows,
2Recall that Du
vjis a Normal distributed random variable, with
mean and variance µDvjand σDvj, hence, the probability of δvj=
Pr nDu
vjois known.
j{1,...,V }
Pr ht1Su
vit, tSl
vi, t tSu
vimvi,vj(t),j[1, V ].
j∈{1,...,V }
Pr ht1Su
vi, t tSu
vimvi,vj(t),j[1, V ].
Pr nSu
vj=t, Lu
Pr nSu
Pr nt1Su
vj=l, tDl
vi, t tSu
vimvi,vj(t),j[1, V ].
Pr nuvj|t, Lu
vj=lo= Pr nSu
vjt, Eu
Pr nSu
vj=eu, Lu
Pr nEu
where suand euare the starting and ending time
deterministic values of the random variables Su
vj, respectively, and in notation they are replaced by t
at (12) and (13).
Utilizing the probability of each user being in location
lat time t, expressed in (18), the expected numbers of
users is obtained as,
E{NU(t, l)}=X
Pr nuvj|t, Lu
vj=lo. (19)
E. Search minimum number of active cells at a time t
in a time range
The time segment tat which the minimum number
of active cells is enough to serve all subscribed users
for a content can be found by solving the following
optimization problem,
t= arg min
I(E{NU(t, l)}> mu), (20)
where muis the threshold of the minimum number of
user to declare that a BS lis active.
In this section, a real-life simulation is presented to
illustrate the relationship between the human mobility
and the optimization objective, which is searching for a
time segment with the minimum number of active cells.
A. Setup
1) Location: A small town located in Thuwal,
Makkah Province, Saudi Arabia, is considered in the
simulation. It is a moderate density living compound that
facilitates both working and living environment. In the
simulation, there are about 2000 townhouses and 80 two-
story apartment buildings. Each townhouse populates a
family or 3-8 people and an apartment building populates
Fig. 1. The simulation area with buildings in the following coloring,
green: townhouses, cyan: apartment buildings, blue: university campus,
magenta: recreational and dining areas, yellow: primary and secondary
schools. 45 base stations with their name and sectors are shown. The
black straight and dotted color lines are the cell boundaries.
about 20-40 people. In the compound, the university
campus is the major working area for the residents and
three schools for primary and secondary school students.
Furthermore, there are six buildings for recreation, din-
ing, and shopping. For the mobile network, it is a typical
hexagonal cell deployment with about 540 meters inter-
BS distance. There are 45 BSs are deployed in the
simulation area and named as a 4-digit number from
1001 to 1045. Each BS consists of three 120 degree
sectors and each sector is considered as a cell with a
5-digit number name. The sector name is constructed by
extended one more digit from the BS name to the right
most digit, such as 10011(northeast), 10022(southeast),
and 10033(west) for three sectors of BS 1001. In total,
135 cells are deployed in the 9.57 km2simulation area.
A map of the simulation with the BS deployment and
cell boundaries is shown in Figure 1.
2) Mobility: In the simulation, a daily life of a user
is modeled with random locations and durations. The
Day 1
Day 2
Day 3
Day 4
Day 5
Number of Mobile Nodes
Number of users of Group 1 in each cell
(a) Staff
Day 1
Day 2
Day 3
Day 4
Day 5
Number of Mobile Nodes
Number of users of Group 2 in each cell
(b) School Students
Day 1
Day 2
Day 3
Day 4
Day 5
Number of Mobile Nodes
Number of users of Group 3 in each cell
(c) University Students
Day 1
Day 2
Day 3
Day 4
Day 5
Number of Mobile Nodes
Number of users of Group 4 in each cell
(d) Dependents
Fig. 2. Number of users of each group in five selected cells
model first randomly selects a home and a work location
for certain user. These locations are static for a user
throughout the simulation period. Then, the durations
of staying are randomly generated following various
truncated normal distributions. Furthermore, a user has a
certain probability of visiting different recreational and
dining places. Four groups of mobile users with different
daily mobility patterns employed in the simulation are
described in the following.
Staff: A model for the movements of office staff
is adopted for 2200 users. There are 75% of users
live in townhouses and 25% of users live in apartment
buildings. Their office locations are static and randomly
chosen in university campus buildings. The daily mobil-
ity patterns start by staying at home at the midnight until
morning. Then, users start moving to offices and stay
until lunch hours. After an average one-hour lunch break,
users go back to offices until evening. A percentage of
people will go to the recreational and dining areas after
work. Finally, users return to home in the evening.
School Students: This group includes 1400 primary
and secondary school students who live in townhouses.
Starting at midnight, the mobility of this user group is
similar to the others, staying at home until morning.
At 7am-7:30am, all of the school students go either to
the primary or secondary school areas and stay until
3-3:30pm. After school, school students start to travel
around the community actively. In the evening, they
return to home. This group of users are significantly
synchronized to travel and stay in the school period.
University Students: There are 900 university students
live in apartment buildings. The mobility patterns start
from the midnight, while most of the university students
stay in apartments until morning. In the morning and
afternoon, university students move between the campus
buildings and stay for classes and activities. In the
evening, students may go to recreational and dining areas
or go home. The major difference between adult staff
and university students is that university students have
the higher mobility to move inside the campus and a
shorter average staying period.
Dependents: There are 1700 dependents in the sim-
ulation. They have a fixed home location but without
a fixed working location. Their staying locations and
durations are more random and unpredictable than the
other groups. In general, the mobility patterns start from
midnight while users stay at home, until morning. Then
users travel and stay randomly in the area.
B. Results
These four user groups have distinct mobility patterns.
From the mobile network operator perspective, these
movements generate various daily periodic patterns re-
garding the number of users in each cell. For instance,
the cells covering the university campus area has a
larger number of users in working hours. The cells
covering primary and secondary schools have a signif-
icant decrease in users after the school hours. Figure 2
shows the number of users of each user group in the
five selected cells over a five-days simulation period.
Each cell covers a particular type of buildings. Cell
10011 covers two schools and some staff housing. Cell
10053 covers apartment buildings only, where mostly
occupied by university students and a few staff. Cell
10063 covers staff housings only. Cell 10143 covers
half of the university campus and the campus diner.
Cell 10183 covers a recreational and dining building.
In Figure 2a, the number of staff increases steadily in
Cell 10143 starting in the morning and reaches the peak
roughly at noon on a daily basis. Furthermore, Cell
10183 shows two peaks are observed daily. The first
lower peak is the lunch hours and the second peak is
the evening time before midnight when the people are
seeking for recreation or dining. In Figure 2b, the Cell
10011, where the secondary school located, shows a
significant sharp increase of school students from no
users to over 900 users during the school hours. In
Figure 2c, Cell 10143 and 10183 have similar patterns
observed in the staff, but with a different number of
users. In the Cell 10053, which cover one-fourth of
the university student apartments, the peak numbers of
university students appear in the night daily. In Figure
2d, the dependents have no static locations to travel or
stay. Therefore, the numbers of users in Cell 10053 and
10063 are chaotic. However, the cells covering the dining
area, Cell 10143 and 10183, have distinct daily patterns
as described in Figure 2a. Figure 3 shows the aggregated
number of users in these five selected cells. It clearly
Day 1
Day 2
Day 3
Day 4
Day 5
Number of users
Number of users in each cell
Fig. 3. Number of total users in five selected cells
Day 1
Day 2
Day 3
Day 4
Day 5
Number of active cells
Number of active cells
Staff School S. University S. Dependents All Users
Fig. 4. Number of active cells
shows each cell has different periodic patterns and peak
hours according to its coverage area.
Recall that the period of having the minimum number
of active cells is the best timing for broadcasting contents
to the user groups regarding using the minimum radio
resources. In Figure 4, the numbers of active cells for
each user group are illustrated. Three phenomena can
be observed in this figure. First, in most of the time, the
average number of active cells for staff, school students,
and dependents are ranged from 55 to 62, but the
university students have a lower number of active cells
compare to the others. It is because those three groups of
users are mainly living in the low-density housing area.
When users go home in the evening, they spread evenly
over a large area. For the university students, they live
in the higher density apartment buildings and this area
is located closely to the campus buildings. In general,
they are more congregated in the evening and moves
within a closer area than the other users. Therefore, the
average number of active cells of university students is
less than the others. Second, the periods of having the
minimum number of active cells for the school students
are longer than the other groups. School students arrive
at the school on time and stay in the school for several
hours every day. It is easier to look for a time segment
for delivering contents to school students in the school
area. However, although the minimum number of active
cells for staff is relatively small compare to its average, it
has only a short period in a day to achieve the minimum.
In contrast, the university students have a longer period
on maintaining the minimum number of active cells
in the early morning when students are mainly in the
apartments. Finally, since the mobility patterns of depen-
dents group were mostly random, the minimum number
of active cells of dependents are large comparing to
other groups. In summary, among these four user groups,
searching a time segment for broadcasting content to the
school students are relatively easier than the other users,
and consuming the minimum amount of radio resources.
In this paper, we conduct a comprehensive analysis
on the human activity mobility model tied with an
efficient content broadcasting system in 5G networks.
The analysis was conducted by using the concept of
random events and associated conditional probabilities. It
shows the relationship between the human mobility and
the optimization objective of the content broadcasting
system. A real-life simulation is presented to indicate
the connection further. In the future, it is essential to
evaluate the time complexity of the statistical analysis
to investigate the cost of predicting an optimal solution
for the delivery system.
[1] T. Camp, J. Boleng, and V. Davies, “A survey of mobility
models for ad hoc network research,” Wireless communications
and mobile computing, vol. 2, no. 5, pp. 483–502, 2002.
[2] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Under-
standing individual human mobility patterns,” Nature, vol. 453,
no. 7196, pp. 779–782, 2008.
[3] F. Xu, Y. Li, M. Chen, and S. Chen, “Mobile cellular big data:
linking cyberspace and the physical world with social ecology,
IEEE Network, vol. 30, no. 3, pp. 6–12, May 2016.
[4] A. Munjal, T. Camp, and N. Aschenbruck, “Changing trends in
modeling mobility,Journal of Electrical and Computer Engi-
neering, vol. 2012, 2012.
[5] F. Ekman, A. Ker¨
anen, J. Karvo, and J. Ott, “Working day
movement model,” in Proceedings of the 1st ACM SIGMOBILE
workshop on Mobility models. ACM, 2008, pp. 33–40.
[6] S. Isaacman, R. Becker, R. C´
aceres, M. Martonosi, J. Rowland,
A. Varshavsky, and W. Willinger, “Human mobility modeling at
metropolitan scales,” in Proceedings of the 10th international
conference on Mobile systems, applications, and services (Mo-
biSys). ACM, 2012, pp. 239–252.
[7] Q. Zheng, X. Hong, J. Liu, D. Cordes, and W. Huang, “Agenda
driven mobility modelling,International Journal of Ad Hoc and
Ubiquitous Computing, vol. 5, no. 1, pp. 22–36, 2009.
[8] Cisco, “Cisco Visual Networking Index: Global Mobile Data
Traffic Forecast Update, 2016 - 2021,” Tech. Rep., March 2017.
[9] P. Rost, C. J. Bernardos, A. D. Domenico, M. D. Girolamo,
M. Lalam, A. Maeder, D. Sabella, and D. Wbben, “Cloud
technologies for flexible 5g radio access networks,” IEEE Com-
munications Magazine, vol. 52, no. 5, pp. 68–76, May 2014.
[10] L. Shi, K. W. Sung, and J. Zander, “Future tv content delivery
over cellular networks from urban to rural environments,IEEE
Transactions on Wireless Communications, vol. 14, no. 11, pp.
6177–6187, Nov 2015.
[11] C. P. Lau, A. Alabbasi, and B. Shihada, “An efficient live tv
scheduling system for 4g lte broadcast,” IEEE Systems Journal,
vol. PP, no. 99, pp. 1–12, 2016.
[12] C. P. Lau and B. Shihada, “Tv broadcast efficiency in 5g
networks from subscriber prospective,” in 2015 IEEE Global
Communications Conference (GLOBECOM), Dec 2015.
[13] K. Poularakis and L. Tassiulas, “Exploiting user mobility for
wireless content delivery,” in Information Theory Proceedings
(ISIT), 2013 IEEE International Symposium on. IEEE, 2013,
pp. 1017–1021.
[14] N. Golrezaei, K. Shanmugam, A. G. Dimakis, A. F. Molisch,
and G. Caire, “Femtocaching: Wireless video content delivery
through distributed caching helpers,” in INFOCOM, 2012 Pro-
ceedings IEEE, March 2012, pp. 1107–1115.
[15] G. M. Lee, S. Rallapalli, W. Dong, Y. C. Chen, L. Qiu, and
Y. Zhang, “Mobile video delivery via human movement,” in 2013
IEEE International Conference on Sensing, Communications and
Networking (SECON), June 2013, pp. 406–414.
[16] Y. Wu, S. Yao, Y. Yang, T. Zhou, H. Qian, H. Hu, and
M. Hamalainen, “Challenges of mobile social device caching,
IEEE Access, vol. 4, pp. 8938–8947, 2016.
... A device is wirelessly connected to the core network, and the RAN transmits its signal to various wireless endpoints, and the signal travels with other networks' traffic. RANs are Generic Radio Access Network (GRAN), which uses base transmission stations and controllers to manage radio links aimed at circuitswitched and packet-switched core networks; and GSM Edge Radio Access Network (GERAN), which chains real-time packet data which provisions both circuit-switched and packet-switched services which efforts only on packet-switched services [7]- [9]. ...
... The same authors in [9] propose human mobility-based content distribution which uses the broadcast nature of wireless communication to reduce the number of duplicated transmissions of contents in the radio access network for conserving radio resources and energy. A generic broadcast strategy of 5G is tested in the simulations and compared within actual existing deployment of base stations as well as a smaller cells environment. ...
Conference Paper
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In Iran, satellite platform was never been the largest television (TV) platform as an alternative of the digital terrestrial television in which land-based stations broadcast television content by radio waves in consumers' receivers gains more attention. This paper presents analysis on TV performance, persisting TV challenges with state-of-art solutions a broadcast performance of 5G media houses basing on Markov assumptions of communication operations and transfer rates dealing with calculations of imperative parameters for the 5G data coverage. The Erlang performance model is designed as a function of two variables of the number of data emission streams and resource-hungry application receivers, based on their values to determine the performance probability with a broadcast perspective. The simulated results depicted that it’s possible to regulate the value of the broadcast, lives streams, mobile deliveries using satellite platform concerning service slicing.
... Zheng et al. proposed the agenda driven mobility model in [21] emphasizing the social role of humans in making movement decisions. In [22], the authors analyzed the human activity mobility model comprehensively for content broadcasting in 5G networks. ...
Today's modern communication technologies such as cloud radio access and software defined networks are key candidate technologies for enabling 5G networks as they incorporate intelligence for data-driven networks. Traditional content caching in the last mile access point has shown a reduction in the core network traffic. However, the radio access network still does not fully leverage such solution. Transmitting duplicate copies of contents to mobile users consumes valuable radio spectrum resources and unnecessary base station energy. To overcome these challenges, we propose huMan mObility-based cOntent Distribution (MOOD) system. MOOD exploits urban scale users' mobility to allocate radio resources spatially and temporally for content delivery. Our approach uses the broadcast nature of wireless communication to reduce the number of duplicated transmissions of contents in the radio access network for conserving radio resources and energy. Furthermore, a human activity model is presented and statistically analyzed for simulating people daily routines. The proposed approach is evaluated via simulations and compared with a generic broadcast strategy in an actual existing deployment of base stations as well as a smaller cells environment, which is a trending deployment strategy in future 5G networks. MOOD achieves 15.2% and 25.4% of performance improvement in the actual and small-cell deployment, respectively.
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Understanding mobile big data, inherent within large-scale cellular towers in the urban environment, is extremely valuable for service providers, mobile users, and government managers of the modern metropolis. By extracting and modeling the mobile cellular data associated with over 9600 cellular towers deployed in a metropolitan city of China, this article aims to link cyberspace and the physical world with social ecology via such big data. We first extract a human mobility and cellular traffic consumption trace from the dataset, and then investigate human behavior in cyberspace and the physical world. Our analysis reveals that human mobility and the consumed mobile traffic have strong correlations, and both have distinct periodical patterns in the time domain. In addition, both human mobility and mobile traffic consumption are linked with social ecology, which in turn helps us to better understand human behavior. We believe that the proposed big data processing and modeling methodology, combined with the empirical analysis on mobile traffic, human mobility, and social ecology, paves the way toward a deep understanding of human behaviors in a large-scale metropolis.
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This paper proposes VideoFountain, a novel service that deploys kiosks at popular venues to store and transmit digital media to users' personal devices using Wi-Fi access points, which may not have Internet connectivity. We leverage mobile users to deliver content to these kiosks. A key component in this design is an in-depth understanding of user mobility. We gather real mobility traces from two largest location-based social networks (Foursquare and Gowalla) and analyze both macroscopic and microscopic human mobility in different cities. Based on the insights we gain, we study several algorithms to determine the initial placement of content and design routing algorithms to optimize the content delivery. We further consider several practical issues, such as how to incentivize users to forward content, how to manage copyrights, how to ensure security, and how to achieve service discovery. We demonstrate the feasibility of VideoFountain using trace-driven simulations.