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Impact of Trace-Based Mobility Models on the Energy Consumption of DelayTolerant Routing Protocols
Conference Paper · November 2021
DOI: 10.1109/ICEEICT53905.2021.9667942
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2021 5th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)
Military Institute of Science and Technology (MIST), Dhaka-1216, Bangladesh
Impact of Trace-Based Mobility Models on the
Energy Consumption of Delay-Tolerant Routing
Protocols
Md. Khalid Mahbub Khan1, Muhammad Sajjadur Rahim2, and Abu Zafor Md. Touhidul Islam3
1, 3
Department of Electrical and Electronic Engineering
University of Rajshahi
Rajshahi-6205, Bangladesh
2
Department of Information and Communication Engineering
University of Rajshahi
Rajshahi-6205, Bangladesh
1
khalidmahbub.khan@yahoo.com, 2sajid_ice@ru.ac.bd, and 3touhid.eee@ru.ac.bd
Abstract—In Delay-Tolerant Network (DTN), nodes’
energy consumption depends highly on their movement pattern
since energy-constrained nodes move in a “Store-Carry-andForward” paradigm for data communication. Energy must be
expensed efficiently, selecting a suitable mobility model for
successfully delivering messages in DTN. Mobility models are
generally two types: one is synthetic mobility, and the other is
trace-based mobility. In this research, the impact of tracebased mobility on the energy consumption for delay-tolerant
routing protocols is evaluated in terms of the average
remaining energy and the number of dead nodes. Here, three
trace-based mobility models: MIT Reality, INFOCOM, and
Cambridge Imotes are considered. Shortest Path Map-Based
Movement from synthetic mobility is also investigated in this
research for better analysis. These mobility models are
implemented for five delay-tolerant routing protocols:
Epidemic, Spray and Wait, PRoPHET, MaxProp, and RAPID
and simulated in the Opportunistic Network Environment
(ONE) simulator using a similar simulation environment.
Simulations are performed by varying message generation
intervals, message Time-To-Live (TTL), and buffer size,
respectively, while others remain fixed. From the outcomes of
simulations, we have finally found that INFOCOM trace on
MaxProp protocol has the minimum value of average
remaining energy, while Spray and Wait protocol with MIT
Reality has the maximum value of average remaining energy.
Apart from this, Shortest Path Map-Based Movement for
MaxProp protocol measures the highest number of dead nodes,
and Spray and Wait protocol in Cambridge Imotes computes
the least number of dead nodes.
Keywords—delay-tolerant
network,
routing,
energy
consumption, trace-based mobility, synthetic mobility,
opportunistic network environment simulator
I.
INTRODUCTION AND RELATED WORKS
In recent decades, data communication in infrastructureless networking schemes has gained much attention from
researchers as a considerable number of portable mobile
devices with communication capabilities are available and
being used by people. So, the improvement of Mobile Adhoc Network (MANET) has come forward. In MANET, the
existence of a contemporary link between source and
destination nodes is essential in order to route a packet in a
precise path. Packets will be dropped if the destination node
XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
is unreachable. So, data routing protocols for MANET are
designed considering these issues. Nevertheless, in practical
cases, there exist many challenging environments where
nodes experience unpredictable movement, sporadic
duration of contacts to each other, longer message delivery
delay, frequent link disruption, etc. MANET protocols fail
to route data efficiently in these cases as the dedicated endto-end path is unavailable, and the topology of such
networks becomes dynamic in nature. Delay-Tolerant
Network (DTN) has replaced MANET in these hostile
environments by allowing intermittent connectivity among
nodes. In DTN, nodes store data and carry it for a
provisional time to forward until the availability of their
connectedness with the destination. This opportunistic
behavior of nodes in this networking architecture improves
the success rate of data delivery in a wide range of hostile
applications, viz., communication in rural areas, postdisaster areas, etc. [1].
An essential concern behind the routing protocols of
DTN architecture is the pattern of nodes‟ movement. It is
closely related to contact patterns and duration among the
nodes. Several physical factors (viz., total area, street map,
speed limit, etc.) and social factors (viz., active and free
times, friend‟s mobility, etc.) impact on the nodes‟
movement pattern. However, there are two methods to
model the contact pattern: to use a synthetic model or to use
real traces from the real scenarios. Performance measuring
parameters of network protocols, such as successful data
forwarding rate, delay, overhead, etc. are highly influenced
by either these synthetic movement models or the real-based
movement. A synthetic model is a statistical model which
creates a pattern in accordance with specific rules. These are
mainly used to formulate a category of users or applications.
Real traces are accumulated from real-life based
applications. Real traces are based on contact traces and
GPS traces. Contact-based traces provide a complete contact
list where two or more nodes are participating in contact.
The beginning time and the finish time of that contact are
also appended there. On the other hand, in GPS traces, the
node‟s location is provided for a particular period of time.
Some limitations are experienced in real traces in spite of
their attractive feature of exhibiting the real behavior of
nodes in a specific scenario. Traces would not be effective
for the new scenarios if it is not included in the collection of
them. In addition, it is not possible to enhance or extend the
traces within time as the size of them is fixed. Again, they
are not able to support any changes regarding the
perspective of users. For example, it is not considered in
traces if users move away from any troubling and serious
scenarios. To utilize real-based traces in simulations is not
cost-effective since the external files are required to read the
next point related information. On the contrary, more
flexibility has been offered in synthetic models. They can
afford adjustable parameters and support new scenarios. If
any characteristic of the network, such as the number of
nodes, is increased, synthetic models are suitable to work on
that where the traces are incapable [2, 3].
However, a challenging issue of DTN architecture is that
how efficiently the nodes utilize energy. Nodes are energyrestricted since most of them are driven by lithium-ion
batteries. The energy quantities of nodes tend to decrease
with time because of their activities such as transmitting and
receiving messages, searching for neighbors, etc.
Furthermore, nodes‟ movement pattern highly influences the
energy consumption of nodes. So, optimization of energy
consumption is essential for delay-tolerant routing protocols
since these protocols are implemented into a network,
adopting a specific trace-based or synthetic model for
nodes‟ movement patterns. For the energy-efficient
operation of routing protocols, the amount of energy
expenditure taken place within adopted trace-based or
synthetic models needs to be a minimum.
Many pieces of research have been performed in energy
consumption aspects considering trace-based or synthetic
mobility models. In [4], the authors proposed “EnergyAware Bubble Rap” (EA-Bubble Rap), an energy-efficient
version of a well-known social-based DTN protocol: Bubble
Rap, and compared it with other two existing DTN protocols
(Epidemic and Bubble Rap). Only two traces (viz., UPB
2012 and Cambridge Imotes) were used to show energy
consumption among simulated protocols for different
maximum energy values. An analysis of the performance of
simulated protocols was also given there in terms of
delivery cost, average hop count, hit rate, average latency,
etc. Other existing DTN routing protocols, traces, and
synthetic models remained unconsidered. Energy efficiency
for different synthetic mobility models except for any real
traces were analyzed in [5]. In some researches [6, 7], the
authors were mainly focused on novel routing solutions.
They were motivated to design new routing schemes with
respect to existing DTN routing techniques along with the
demonstration of their comparative performance analysis. In
such cases, Shortest Path Map-Based (SPMB) model,
external movement-based systems, and Working Day
Movement were utilized to determine the pattern of nodes‟
mobility depending on the network‟s applications.
Nevertheless, the impact of well-known real traces and other
synthetic mobility models were absent there. On the other
hand, evaluations of energy efficiency among different types
of conventional DTN routing techniques got the main
concern in [810]. Comparative performance analysis of
several protocols also took place in the above-mentioned
studies considering Shortest Path Map-Based Movement
model for the nodes‟ movement. Real traces from real
scenarios were unaddressed into those researches. The
energy consumption of the nodes highly depends on their
movement patterns which come from real cases. So, this fact
motivated us to investigate the impact of real trace-based
mobility on the energy consumption of nodes for the DTN
routing protocols.
We have simulated five DTN protocols: Epidemic [11],
Spray and Wait [12], PRoPHET [13], MaxProp [14], and
RAPID [15] considering individually three real traces for
nodes‟ movement: MIT Reality, INFOCOM, and
Cambridge Imotes; Shortest Path Map-Based Model from
the synthetic models is also included here for the ease of
comparison. Our simulation tool is the Opportunistic
Network Environment (ONE) simulator [16]. We have
evaluated the energy consumption of nodes for simulated
protocol with respect to the average remaining energy and
number of dead nodes. The rest of the paper is arranged as
follows: Section II provides a brief review of the
investigated mobility models (real trace-based and
synthetic). Section III delivers a short description of the
selected delay-tolerant routing protocols. Simulation
environment-related discussions are given in Section IV,
and an analysis of research outcomes is provided in Section
V. Finally, Section VI concludes the research and provides a
direction on the future endeavor of this research.
II.
INVESTIGATED MOBILITY MODELS
We have included three real trace-based mobility models
in our research: MIT Reality, INFOCOM, and Cambridge
Imotes. We also added Shortest Path Map-Based
Movement, which is representative of the synthetic mobility
models, for a better understanding of the results. This
section briefly explains the above-mentioned mobility
models.
MIT Reality is a campus-based trace in which 97 Nokia
6600 phones were deployed with Bluetooth connectivity.
Here, the information of contact was collected from coursewise faculties and students of MIT in the 2004-2005
academic year. The information regarding data transmission
among the participants, their location, and their adjacencies
was included in that contact information. The duration of
this data set is 246 days [17].
INFOCOM trace is also known as Haggle 3 project
trace. It is based on an IEEE authorized conference named
by the same name held in 2005. In this case, Intel motes
(Imotes) devices were distributed among 41 participant
volunteers through the Bluetooth interface. Later in 2006,
the organizer of the same conference involved 98
participants, which is known as INFOCOM 6 data set. The
total duration of both INFOCOM 5 and INFOCOM 6 is 3
days. Since we have included only INFOCOM 5 in our
study, we recognize INFOCOM 5 as INFOCOM trace [2].
Another campus-oriented trace is Cambridge Imotes.
Here, Imotes were provided to 36 participants around the
campus area, University of Cambridge. These devices are
deployed in the most popular places on the campus for 10
days. Bluetooth technology is used as the interface in this
case. This trace is also known as Haggle 4 project trace [2].
Shortest Path Map-Based Movement is a synthetic
mobility model which is more realistic than other MapBased Movement models. In this model, nodes move
randomly through the paths that are well-defined from fixed
map data. Nodes choose random points on the map from
their current positions. Then they try to find out and follow
the shortest path onto those random points from their
present locations according to Dijkstra‟s algorithm. The
destined random points are selected randomly, otherwise
from a list known as Point of Interest (POI). This list is
chosen from the real world popular destined places such as
tourist-attraction places, more popular shops or restaurants,
etc. [16, 18].
III.
SELECTED DELAY-TOLERANT ROUTING PROTOCOLS
This section briefly describes the examined delaytolerant routing protocols in this research: Epidemic, Spray
and Wait, PRoPHET, MaxProp, and RAPID.
Unbounded replication of messages is the main idea
behind the Epidemic protocol. In this strategy, the message
containing node continually replicates the message and
forwards the replica of messages to those recently
encountered nodes with it that have no copy. In such a way,
finally, the message would reach the destined target node.
But the same message would be flooded in the entire
network. Successful message transmission is possible in this
approach without considering network resources, viz.,
bandwidth, delay, overhead, buffer size, etc. For this reason,
this approach is not an ideal solution for routing data in the
DTN environment [11].
The limited replication-based concept is implemented in
the Spray and Wait technique instead of uncontrolled
replication in Epidemic. Here, only a maximum allowable
number of message copies are forwarded to the same
number of relays. This maximum allowable number is
denoted as L. The value of L depends on several factors,
viz., the density of nodes in the network required for
message transmission, etc. This forwarding approach
consists of two phases, one is the Spray phase, and the other
is the Wait phase [12].

Spray: In the „Spray‟ phase, a source node
forwards L copies of a message to the L distinct
relay nodes.

Wait: L relay nodes wait with carried message
copy for the future encounter with the destination
node to route their message copy to the destination.
So, this phase is known as „Wait‟.
The source node maintains a set of probabilities to
forward a message in Probabilistic Routing Protocol using
the History of Encounters and Transitivity (PRoPHET)
approach. Instead of multiple message-copy transmission,
this technique considers the likelihood regarding actual real-
world encounters of nodes. When meetings occur with the
source, messages are forwarded based on probability from
the source. The encountered node which has a higher
probability of delivery would receive the message soon [13].
MaxProp forwarding strategy assigns and prioritizes
definite schedules to route data packets. The sender
maintains a ranked list of packets that decides which packet
would be transmitted or dropped at the earliest basis. This
list relies on the cost determined from the sender toward all
destinations and reflects the probability of message delivery.
MaxProp strategy stabilizes the preference of packets based
on minimal hop counting and avoids numerous copies
acceptance of the same message [14].
Resource Allocation Protocol for Intentional DTN
(RAPID) is a routing protocol for DTN where resource
allocation problem is the primary concern and attempting to
improve the forwarding criterion such as average latency,
maximum delay, etc. Here, the DTN model is seemed to be
a utility-controlled scheme where a utility function is
managed. A utility value
is referred by that utility
function for every data packet on the basis of the routingrelated criterion.
serves as the expected contribution of
packet i against the recognized routing criterion. Within the
utility, the packets that resulted in maximum increases
would be replicated first by RAPID protocol, and this
protocol is intended to replicate all the messages in this way
if the network resources (viz., bandwidth. etc.) permit [15].
IV.
SIMULATIONS
In this research, we have investigated the effects of
nodes‟ real trace-based mobility models on the energy
consumption of delay-tolerant routing protocols. To do so,
we have simulated five delay-tolerant routing protocols:
Epidemic, Spray and Wait, PRoPHET, MaxProp, and
RAPID using three trace-based mobility models: MIT
Reality, INFOCOM, Cambridge Imotes, and also included
Shortest Path Map-Based Movement as a synthetic mobility
model. Simulations have been performed in Java-based
Opportunistic Network Environment (ONE) simulator,
whose one of the notable features is to support different
mobility models including real traces. An energy module
[18] is configured within ONE simulator for conducting
simulations since the evaluation of the energy consumption
of nodes is the main focus. Table I and Table II show the
settings of the necessary simulation parameters and the
information regarding the investigated trace-based mobility
models, respectively. Here, the entire simulation area is 4.5
km × 3.4 km. A single group of pedestrians carrying
portable devices is considered as the mobile nodes since the
real traces are campus or conference venue oriented, and the
speed of mobile nodes is 0.5-1.5 m/s. But the number of
mobile nodes in the group is fixed according to the settings
of the investigated traces and synthetic mobility model.
Here, the Bluetooth interface is used for data
communication among the mobile nodes. To rationalize the
simulations and to evaluate the energy consumption for the
selected delay-tolerant protocols using the considered real
traces, we have normalized the simulation duration to 3 days
for all real traces and Shortest Path Map-Based Movement.
Simulations are performed on the map of Helsinki city.
TABLE I.
NECESSARY SIMULATION PARAMETERS
Parameter
Simulation area
Simulation time
Routing protocols
Number of message replica (for
Spray and Wait)
Seconds in time unit (for
PRoPHET)
Utility algorithm (for RAPID)
Mobility models
Interface type
Transmission speed
Transmission range
TTL
Message generation intervals
Buffer size (MB)
Message size (MB)
TABLE II.
Value
4.5 km × 3.4 km
3 days
Epidemic, Spray and Wait,
PRoPHET, MaxProp, and RAPID
10
30
Average delay
MIT Reality, INFOCOM,
Cambridge Imotes, and Shortest
Path Map-Based Movement
Bluetooth
2 Mbps
10 m
6 h, 12 h, 1 d, 2 d, and 3 d
515 s, 1525 s, 2535 s, 3545 s,
and 4555 s
5, 10, 15, 20, 25
1
INFORMATION RELATED TO MOBILITY MODELS
Mobility
Type
Duration
Trace/Campus
Trace/Conference
Number of
nodes
97
41
MIT Reality
INFOCOM
(Actually
INFOCOM 5,
also known as
Haggle 3)
Cambridge
Imotes
(Haggle 4)
Shortest Path
Map-Based
Trace/Campus
36
10 d
Synthetic
mobility model
100
3d
246 d
3d
Table III provides the necessary parameters for the
energy settings of the simulations. We have conducted
simulations and evaluated the outcomes by varying message
generation interval, TTL and buffer size, respectively, for
the following metrics: average remaining energy of nodes
and number of dead nodes.
TABLE III.
Fig. 1. Average remaining energy vs. message generation intervals.
PARAMETERS RELATED TO ENERGY MODULE
Parameter
Initial energy of each node
Scan energy
Transmit energy
Receive energy
Energy recharge interval
Threshold value of energy for each
node
V.
A. Varying Message Generation Intervals
Figs. 1 and 2 demonstrate how node‟s remaining energy
and the number of dead nodes for the investigated mobility
models (MIT Reality, INFOCOM, Cambridge Imotes, and
Shortest Path Map-Based) on simulated delay-tolerant
routing protocols (Epidemic, Spray and Wait, PRoPHET,
MAxProp, and RAPID) are affected with the variation of
message generation interval (from 515 s to 4555 s). In this
case, TTL and buffer size are fixed at 1 d and 5 MB,
respectively. In Fig. 1, MIT Reality experiences higher
average remaining energy for all simulated protocols with
the variation of message generation interval while
INFOCOM exhibits the least average remaining energy for
that case. Moreover, MIT Reality for both Spray and Wait
and RAPID protocol can be regarded as the highest energyefficient because of their maximum value of average
remaining energy. But between these two, Spray and Wait
protocol has a slightly higher value for average remaining
energy. So, this protocol with MIT Reality is the most
energy-efficient. The minimum message replication strategy
of Spray and Wait within a limited area like campus reduces
additional energy expenditure. In opposition, MaxProp
protocol for INFOCOM consumes the most nodes‟ energy
and is considered the least energy-efficient. Nodes need
more communication of data in a conference type area like
INFOCOM, so they consume more energy for the MaxProp
protocol.
Value
6000 J
0.92 mW/s
0.08 mW/s
0.08 mW/s
More than 3 days, we do not like to
recharge the batteries within the
simulation period
800 J (about 14% of the initial
energy)
ANALYSIS OF THE SIMULATION RESULTS
This section provides a discussion on the outcomes of
the simulations. The entire discussion consists of the
following three sub-sections.
Fig. 2. Number of dead nodes vs. message generation intervals.
In Fig. 2, the number of dead nodes is the highest in
Shortest Path Map-Based Movement and the lowest in
Cambridge Imotes for all examined protocols. Due to the
random mobility of nodes for Shortest Path Map-Based
movement, the energy of many nodes reaches almost zero in
this type of mobility, and MaxProp protocol produces the
highest number of dead nodes. Furthermore, Cambridge
Imotes for Spray and Wait protocol has a minimum number
of dead nodes because a limited number of mobile nodes
compared to other mobility models are considered in this
trace. As a result, the energy of few nodes goes to zero.
B. Varying TTL
Average remaining energy and number of dead nodes are
plotted against varying message TTL (from 6 h to 3 d) in
Figs. 3 and 4. Message generation interval and buffer size
remained fixed at 2535 s and 5 MB, respectively. As in
Fig. 3, Spray and Wait protocol exhibits the highest average
remaining energy, while for MaxProp, it is the lowest with
the increasing message TTL. A message with larger TTL
may cause more energy expenditure for more replications
within the extended message lifetime. Again, MIT Reality
has the maximum and INFOCOM has the minimum average
remaining energy for all protocols than other mobility
models. So, Spray and Wait in MIT Reality and INFOCOM
in MaxProp are the most and the worst energy-efficient
respectively in this case.
Since we have considered 100 nodes for Shortest Path
Map-Based Movement and nodes are moving randomly in
this mobility, the energy of many nodes reduces to almost
zero or goes below the set threshold value of energy (as
indicated in Table III). So, in Fig. 4, the number of dead
nodes is higher than other traces for this mobility. On the
contrary, Cambridge Imotes shows a lesser count of dead
nodes since the total number of nodes is a minimum than
other mobility models. MaxProp protocol in Shortest Path
Map-based Movement has the highest count of dead nodes,
whereas it is the minimal for Spray and Wait protocol in
Cambridge Imotes.
MB) by keeping message generation interval and TTL fixed
at 2535 s and 1 d respectively are illustrated in Figs. 5 and
6. Similar to Figs. 1 and 3, Spray and Wait in MIT Reality
trace consumes less energy than other simulated. Because of
its controlled replication strategy, nodes expense the least
amount of energy within MIT reality trace. On the other
hand, MaxProp in INFOCOM trace consumes the highest
amount of nodes‟ average energy.
Following Figs. 2 and 4, Shortest Path Map-Based
Movement for MaxProp protocol measures the highest
number of dead nodes as the buffer capacity increases. In
contrast, Cambridge Imotes trace for Spray and Wait
protocol conceived the minimum number of dead nodes
when increased buffer capacity.
Fig. 5. Average remaining energy vs. buffer size.
Fig. 6. Number of dead nodes vs. buffer size.
VI. CONCLUSIONS AND FUTURE WORK
Fig. 3. Average remaining energy vs. TTL.
Fig. 4. Number of dead nodes vs. TTL.
C. Varying Buffer Size
Nodes‟ average remaining energy and number of dead
nodes with the increase of buffer size (from 5 MB to 25
Within this work, we have investigated the energy
consumption of four mobility models (including three real
trace-based and one synthetic): MIT Reality, INFOCOM,
Cambridge Imotes, and Shortest Path Map-Based
Movement for five delay-tolerant routing protocols:
Epidemic, Spray and Wait, PRoPHET, MaxProp, and
RAPID. We have evaluated the energy consumption aspect
of the selected DTN routing protocols over the abovementioned mobility models (with an emphasis on real
traces) from the perspective of two metrics: average
remaining energy and number of dead nodes by considering
the variation of message generation interval, TTL and buffer
size. We have eventually found from the outcomes of the
simulations that MIT Reality trace for Spray and Wait
protocol is the most energy-efficient in terms of average
remaining energy. In contrast, INFOCOM trace in MaxProp
protocol is the least energy-efficient with the increasing
message generation interval, TTL, and buffer size. For the
number of dead nodes, MaxProp protocol in Shortest Path
Map-Based Movement mobility counts the highest than
other DTN routing protocols for message generation
interval, TTL, and buffer size. Cambridge Imotes for Spray
and Wait has the minimum number of dead nodes for such
cases.
[7]
Our future endeavor would be to analyze the
performance of these trace-based and synthetic mobility
models for DTN routing protocols in terms of delivery ratio,
average latency, transmission cost, etc. which would lead us
to reach a proper understanding of the impacts of tracebased and synthetic mobility models on the energy
efficiency and performance of DTN routing protocols.
[9]
ACKNOWLEDGMENT
This work has been supported by National Science and
Technology (NST) MPhil fellowship, granted by Ministry
of Science and Technology, Government of the People‟s
Republic of Bangladesh.
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