RTTMM: Role based 3-Tier Mobility Model for Evaluation of Delay

Tolerant Routing Protocols in Post Disaster Situation

Nikhil N. Gondaliya

and

Mohammed Atiquzzaman

G. H. Patel College of Engineering & Technology, Gujarat Technological University, Gujarat, India

School of Computer Science, University of Oklahoma, Norman, OK 73019, U.S.A.

Keywords: Delay Tolerant Networks, Disaster Mobility Model, DTN Routing Protocols, Internet of Things.

Abstract: In Internet of Things (IoT) the devices are interconnected through Internet with several redundant paths, but

they are still vulnerable to the effects of large scale disasters such as earthquakes and floods. The disaster area

may be disconnected from the rest of the Internet and the need arises to get information about the victims.

Adhoc networks like MANETs and DTNs are most suitable to support the communication in partitioned

networks, such as a network in a post disaster situation. Even an adhoc network becomes one of the essential

network architecture in IoT and attracted lots of attention in the last decade. The disaster affects the several

regions with different intensities called each region as disaster event which are located nearer to each other.

Each disaster event is assigned a group of rescue entities with hand held IoT device, where they perform the

tactical operation. The movement pattern of the rescue entities in a post disaster area is described by a mobility

model which is used to evaluate the routing protocols for post disaster scenario networks. Existing mobility

models for post disaster scenarios do not distribute the rescue entities in proportion to the intensity of disaster

events in the case of multiple events occurring simultaneously. In this work, we propose the Role-based 3-

Tier Mobility Model (RTTMM) to mimic the movement pattern of different rescue entities involved in the

disaster relief operation by distributing them based on the proportion of the intensity of the disaster event.

Our model generates the mobility traces of the rescue entities, which are fed as input to the DTN routing

protocols. We also evaluate the performance of existing DTN routing protocols using the traces obtained from

RTTMM.

1 INTRODUCTION

The Interne of Things (IoT) consists of massive

deployment of heterogeneous devices which are

battery operated and interconnected through wireless

network interfaces. The IoT communication

architectures facilitate such devices not only

connected to the backbone (i.e. the Internet) using

infrastructure-based wireless networks, but also to

communicate with one another autonomously,

without the help of any infrastructure such as

temporary wireless network (Petersen, 2015). This

temporary or adhoc based networks are MANETs and

DTNs which will become the important network

architecture in IoT (Reina, 2013). Even in IoT,

devices are interconnected through the Internet all the

time. But in many situations such as military and

disaster, they become disconnected from the Internet

backbone and communication needs to carry out

using ad hoc manner.

Natural or man-made disaster may destroy the

existing communication infrastructure, making it

difficult for rescue entities to communicate among

themselves and the outside world to perform relief

operations. Mobility characteristic of the rescue

entities in a post disaster situation is very different

from other environments, like campus, conference

and military. Relief workers, policemen, emergency

vehicles and ambulances have different movement

patterns in the disaster affected area. The mobility

model of a disaster scenario mimics the movement

pattern of rescue team members to inspect the event

areas, providing medical service and relief goods, and

collect the information about victims and damage due

to the disaster. Existing mobility models like Random

Walk (RW) and Random Way Point (RWP) cannot

be used to model the movement of rescue entities. In

the literature, the authors have proposed mobility

models to imitate the movement pattern of the entities

in a post disaster scenario. The models can be

categorized into synthetic and map based models.

Gondaliya N. and Atiquzzaman M.

RTTMM: Role based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation.

In Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pages 11-20

ISBN: 978-989-758-183-0

The synthetic mobility model by Nelson et al.

(Nelson, 2007) assumed that when an event (for

example, disaster) occurs, some entities (relief

workers) are attracted towards the event and others

(civilians) flee away from the events. This model

assumes that even though the event lasts until the

relief operation ends, but rescue team members are

always working around recently happened event. This

model does not distribute the dedicated relief workers

to the specific event areas when multiple disaster

events occur simultaneously. Another synthetic

mobility model by Aschenbruck et al. (Aschenbruck,

2007) is based on separation of rooms (zones). Zones

are established in the disaster affected area also called

incident location and movement of rescue entities is

restricted only inside their respective zones. This

model is suitable only for single incident location or

disaster event because it needs same set of zones to

be created for each disaster event.

Gupta et al. (Gupta, 2015) proposed a 4-tier map

based DTN architecture to provide communication

infrastructure in the post disaster scenario and the

area is divided into shelter points (SPs) with Throw

boxes (TBs) being placed in each SP. This model

emulates only the movement pattern of relief workers

inside the SP and assumes that the messages are

delivered from the SP to the main coordination center

through Data Mules (DMs).

The existing models do not distribute the rescue

entities to the disaster event in proportion to its

intensity at different events need a varying number of

rescue entities. Moreover, each rescue entity

performs the relief operation with a pre-defined

unique role, and their movement is restricted to the

specified trajectory. These demands a suitable

mobility model for the post disaster scenario to

realistically mimic the movement patterns of rescue

entities. In this paper, we propose the Role based 3-

Tier Mobility Model (RTTMM) which mimics the

movement of rescue entities and the unique role

assigned to them. The rescue entities are distributed

to the events in proportion to their intensity values

and movement of relief workers is restricted in their

respective event area only. We implemented tool in

C++ language to generate the movement traces of

RTTMM.

The rescue entities with the devices of a post

disaster network may remain disconnected for a

significant amount of time. Such a network cannot be

supported by traditional wired networks like TCP/IP

or Ad hoc wireless network such as MANETs which

require a continuous network connection. The above

requirement of a disconnected network can be

accomplished by opportunistic networks, such as

DTNs to support the communication among the

rescue entities. The performance of such a network

mainly depends on the mobility of the rescue entities

(devices of the network). A mobility model is

therefore required to carry out the performance

evaluation of the network. The movement traces of

the mobility model used in a disaster scenario have

also a great impact on the performance of routing

protocols in DTNs. The performance of routing

protocols has been found to vary depending on the

mobility model that is used.

Many authors have evaluated the performance of

DTN routing protocols such, as Prophet (Lindgre,

2003), Epidemic (Vahdat, 2000), MaxProp (Burgess,

2006) and SprayAndWait (Spyropoulos, 2007) using

mobility models (Aschenbruck, 2007; Nelson, 2007)

of the post disaster scenario. To compare the

effectiveness of RTTMM, the performance evaluations

can be carried out using the realistic mobility model

proposed in this paper. The main problem in analyzing

the performance of a routing protocol for post disaster

scenario is the absence of a realistic mobility model

which distributes the rescue entities in proportion to the

intensity of the disaster event.

The mobility traces generated by RTTMM are fed

as input to the DTN routing protocols. The

performance parameters of routing protocols like

delivery probability and delivery delay are most

important in the post disaster operation as they deal

with information about human lives and give a picture

of the damage. The device carried out by rescue team

members is battery operated and with limited storage

so, energy consumption and buffer storage are to be

considered. Therefore, we also evaluate the routing

protocols for overhead ratio and cost per message

which show the energy conservation in the network.

The existing routing protocols are also evaluated by

varying number of devices, buffer size and message

size using ONE simulator (

Keranen, 2009).

The rest of the paper is organized as follows:

Section 2 presents related works on the mobility

modelling in a disaster situation, different network

architectures and existing routing protocols. Sections

3 and 4 explain RTTMM and its analysis,

respectively. The performance and simulation

parameters are discussed in Section 5. The simulation

results and discussions are presented in Section 6.

Finally the conclusions are drawn in Section 7.

2 RELATED WORKS

To model the mobility and select the most appropriate

wireless adhoc network architecture for the post

IoTBD 2016 - International Conference on Internet of Things and Big Data

disaster situation is a challenging task and is currently

an active area of research. This Section describes the

existing mobility models and multi hop wireless

adhoc network, such DTNs, for the post disaster

scenario. These are followed by the existing DTN

routing protocols, evaluated for the disaster mobility

scenario.

2.1 Existing Disaster Mobility Models

The mobility models proposed so far can be divided

into synthetic and map based mobility models as

described below.

2.1.1 Synthetic Mobility Models

A number of authors have proposed synthetic

mobility models to impersonate the movement

pattern of objects in a disaster situation to provide

communication in the disaster situation. Aschenbruck

et al. (Aschenbruck, 2007) presented a synthetic

mobility model called separation of room which

divides the disaster areas into different zones:

incident zone, casualty clearing and patient waiting

area, transport zone and technical operational

command. The BonnMotion tool developed by

Aschenbruck et al. (Aschenbruck, 2010) allows

generation of the mobility traces for this mobility

model. This model is mainly used to provide medical

treatment in the post disaster scenario. It however has

the disadvantage of requiring the set of zones to be

established for each event area.

Another synthetic mobility model presented by

Nelson et al. (Nelson, 2007) assigns a unique role to

each object. They propose low level gravity based

mobility model in which events apply forces to the

objects (civilians, relief workers, policemen etc.).

Consequently, civilians flee away from the event and

the relief workers approach the event. The drawback

of this model is that relief workers are always

attracted to the recent event and they do not follow

the tactical movement inside the event area.

2.1.2 Map based Mobility Models

Uddin et al. (Uddin, 2009) presented the first map

based mobility using DTNs in post disaster situation.

They simulated the mobility for both rescue entities

and victims, including the different centres (fire

station, neighbourhood, house, medical camp, relief

camp and police station) that are established after the

disaster. Movement patterns are also defined for

rescue members and victims by extending the map

based movement model in the ONE simulator on a

built-in map of Helkensi city. This mobility model

included the impact of disaster on the transportation

network and modelled the population and relief

vehicles only.

Gupta et al. (Gupta, 2015) proposed 4-tier map

based DTN architecture to provide communication

infrastructure with respect to the flood disaster which

occurred in the Uttrakhand State of India in 2014. The

disaster area also called activity area is divided into

shelter points (SPs) which are the particular areas

assigned to the group of relief workers to investigate

the scene. Each SP is allocated static TB, which

collects the information within its SP for further

transmission to Main Control Station (MCS). Data

Mules (e.g., ambulances, boats, helicopters) collect

the data from SP and deliver to the MCS which is

connected to the outer world. The authors in their

work have assumed that inter SP communication is

managed by DMs, but they have not emulated the

movement pattern of them to deliver the messages to

the final destination (MCS).

2.2 DTNs for Disaster Scenario

DTNs are most appropriate in a disaster situation due

to their inherent characteristic to operate in the

absence of end-to-end path and continuous network

connectivity. In recent years, researchers have

presented many solutions for disaster scenario using

DTNs in the form of system to help in disaster

recovery and mobility models as discussed in Section

2.1.

Martin-Campillo et al. (Martin-Campillo, 2010)

developed a system to collect victim information

using electronic triage and mobile devices in disaster

situation. Legendre et al. (Legendre, 2011)

summarized the work done in wireless network which

is able to uphold the communication during a disaster

when the existing communication infrastructure is

damaged. They also proposed Twimight which is an

Android based application, sends tweets using

Tweeter severs in normal mode while it uses

opportunistic contacts in disaster mode between

mobile devices in the absence of network

connectivity.

Fujihara et al. (Fujihara, 2012) presented real-

time disaster evacuation guidance system which helps

the evacuee himself to gather the information about

road blockage and danger areas due to fire and share

that information opportunistically with other mobile

devices. Fajardo et al. (Fajardo, 2014) presented a

content based data prioritization method that gathers

the images of the disaster area and images with

RTTMM: Role Based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

RTTMM: Role based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

critical content is sent faster than non-critical content

in order to handle critical events immediately.

2.3 Existing DTN Routing Protocols in

Disaster Scenario

Many DTN routing protocols have been proposed in the

literature which can be categorized into flooding based,

forwarding based and social based routing protocols.

The forwarding and flooding based routing protocols

have been evaluated by the authors for the existing

mobility models in a post disaster scenario. The social

based routing protocols find their applicability in the

human mobility only so, they are not suitable in a post

disaster mobility. Martin-Campillo et al. (Martin-

Campillo, 2013) evaluated the performance of

Epidemic, Prophet, MaxProp and Time-To-Return

(TTR) in disaster scenario using model by Aschenbruck

et al. (Aschenbruck, 2007). It is concluded that

MaxProp gives best performance in delivery probability

and TTR in overhead ratio and cost per message. Saha

et al. (Saha, 2011) evaluated Prophet, Epidemic, Spray

and Wait, MaxProp and Spray and Focus routing

protocols using a cluster based mobility model.

Inwhee et al. (Inwhee, 2010) proposed message

priority based forwarding protocol which handles

messages according to priority. Martin-Campillo et al.

(Martin-Campillo, 2012) proposed new energy

efficient routing and evaluated using synthetic mobility

model by Aschenbruck et al. (Aschenbruck, 2007) for

post disaster situation. Nelson et al. (Nelson, 2009)

evaluated the performance of existing routing

protocols (Epidemic, MaxProp, Spray and Wait, Spray

and Focus and Prophet) using event driven role based

mobility model and proposed Encounter Based

Routing (EBR) protocol. Recently, Bhattacharjee et al.

(Bhattacharjee, 2015) evaluated existing routing

protocols such as MaxProp, Prophet, Spray and Wait

(SnW), Epidemic routing using four different mobility

models: custom map based mobility, post disaster

mobility model, Random waypoint model and Cluster

movement model for disaster scenario. The majority of

above works used existing routing protocols such as

Epidemic, Prophet, MaxProp, EBR and SnW to

evaluate the mobility model for disaster situation. So,

we use the same routing protocols in order to evaluate

RTTMM model.

3 RTTMM: ROLE BASED 3-TIER

MOBILITY MODEL

This Section discusses the proposed synthetic

mobility model which emulates the movement pattern

of different rescue entities working in the post

disaster scenario. Sections 3.1 and 3.2 explain the

different entities involved in the disaster scenario,

disaster events, and the role assigned to a rescue

entity with its movement pattern.

3.1 Rescue Entities and Events

The following Sections describe the different rescue

entities involved in the post disaster relief operation

and the disaster events.

3.1.1 Rescue Entities

There are five different kinds of rescue entities

involved in the model: relief worker, policeman,

ambulance, emergency vehicle, hospital and relief

camp. These entities are categorized into specific

tiers. Mobile devices held by a policeman and relief

worker are treated as tier-1 devices. Ambulance and

emergency vehicle mounted with devices called DMs

are termed as tier-2 devices. Hospital and relief camp

placed at fixed location usually at distant place to

avoid the recurrence of the disaster event called TBs

have connectivity with the outside world are termed

as tier-3 devices. Also, each event area is allotted one

fixed TB and placed in the center.

3.1.2 Disaster Events

Disaster events may be an earthquake, fire damage,

landslide, flood, hurricane, etc. An event has an

associated intensity value which defines the level of

impact of the disaster. We identify the damage radius

for each event that is determined based on the

intensity of the event so that relief workers always

restrict their movement inside the damage radius. Our

RTTMM supports multiple events to occur

simultaneously or sequentially. When an event

happens, the relief workers, ambulances and

emergency vehicles are assigned to the event in

proportion to the intensity of the event. For example,

if two events occur simultaneously with intensity

values of 



and 



, the relief workers are distributed

to the events as given in Equation (1). Where, n

is the

total number of relief workers in the disaster scenario.





+ i



* i



andn

= n



- 



(1)

and n

are the number of relief workers distributed

to the events 1 and 2, respectively, based on the values

of their intensity. Similarly, ambulances and

emergency vehicles are also assigned to the events in

proportion to their intensity values.

IoTBD 2016 - International Conference on Internet of Things and Big Data

3.2 Role and Movement Pattern of

Rescue Entity

Each rescue entity is assigned a unique role and it acts

accordingly. For example, the relief workers move

within the damage radius of the event area. Each

relief worker randomly selects the distance to travel

within the damage radius from the center and returns

to the same point. The same movement pattern is

repeatedly followed again and again. Ambulances

move between the hospital and the event area and

transfer victims from the event area to hospital.

Similarly, emergency vehicles also move between an

event area and the relief camp to provide relief goods

for the victims to be distributed by the relief workers.

The policeman performs patrolling by randomly

selecting event locations, hospital and relief camp.

4 ANALYSIS OF THE RTTMM

The mobility model RTTMM discussed in the

previous Section needs to be analyzed to know the

characteristics of the network and the behavior of the

devices which can be used by network designers to

select the appropriate Ad Hoc network and propose a

suitable routing protocol. We compare our mobility

model with Event Driven Role-based Mobility Model

(EDRMM) because it exhibits similar characteristics

with RTTMM but not strategic movement of relief

workers. Both the models have been analyzed using

different parameters which are described in Sec. 4.1.

4.1 Parameters for Analysis

The following three parameters are considered to

analyze the mobility models which show that whether

the network of the post disaster remains connected or

not and the device will have more opportunity to

forward the messages.

1. Average Device Degree: It is defined by the

average number of neighbors per device and it

is used to differentiate the connectivity of the

network.

2. Maximum Device Degree: The maximum

device degree is the maximum number of

neighbors of a device.

3. Clustering Coefficient: It represents a measure

of the degree to which devices in a graph tend

to cluster together.

4.2 Results for Analysis of RTTMM

In order to assess the mobility model discussed in

Sec. 3, the following scenario is considered. The

simulation is carried out for 6000 seconds with a

square grid size of 3000 m

. There are total 52 mobile

devices involved in a disaster scenario: 38 relief

workers, 2 fixed TB in the center of the event area, 4

ambulances, 4 emergency vehicles, 2 policemen, 1

hospital and 1 relief camp. The transmission range of

each device is set to 50 meters. Two events occur

simultaneously with intensity values of 4000 and

2000. The speed of the relief workers, ambulance and

emergency vehicle and policeman is set to 3 m/s, 12

m/s, and 7 m/s respectively. The same configuration

parameters are set for EDRMM and it also includes

some specific settings, which are taken as per

(Nelson, 2007). Average results for 10 different

simulations with different random seeds are collected.

Figure 1 shows the metrics as a function of time

for RTTMM and EDRMM. As shown in Figures 2(a)

and 2(b), the average device degree and maximum

device degree are always higher in RTTMM due to

planned movement of rescue team members. At the

start of the simulation before the disaster event

occurs, all rescue team members gathered at one place

(for ex. Relief workers and emergency vehicles at

relief camp) which shows higher values of the

average and the maximum device degree in the same

Figure. These metrics are important because they

show the information about number of neighbors a

device has at any point of time which can offer the

high network connectivity.

Figure 1(c) depicts the average clustering

coefficient of all the devices which shows that the

network remains more clustered in RTTMM than

EDRMM due to the strategic and confined movement

of rescue team members. These analyses prove that

RTTMM is more suitable for use in post disaster

situation.

5 EXPERIMENTAL SET UP AND

PERFORMANCE METRICS

In this section, we describe the configuration

parameters for RTTMM and the performance metrics

for the evaluation of the routing protocols.

RTTMM: Role Based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

RTTMM: Role based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

(a) Average device degree Vs Simulation time (b) Maximum device degree Vs Simulation time

Figure 1: Analysis of mobility models: RTTMM and EDRMM.

5.1 Configuration Parameters

The configuration parameters of RTTMM, which

generates the mobility traces of rescue entities for

input to the routing protocol, and the configuration

parameters of ONE are described below. Table 1

shows the parameters of RTTMM and ONE that are

common to all the experiments, unless explicitly

specified. We have taken 10 message copies for the

SnW and EBR routing protocols; settings for the

other routing protocols are as per default

implementation available in the ONE simulator.

5.2 Performance Metrics

The following performance metrics have been

considered to assess and compare the performance of

the existing routing protocols with RTTMM using

DTNs.

1. Delivery Probability or Delivery Ratio: It is

calculated as the ratio of the number of

messages successfully delivered to the

destination to that of the total number of

messages generated in the network.

2. Average Delivery Delay or Latency: Delivery

delay is the time elapsed between the creations

of the message at the source and delivered

successfully to the destination. Average

delivery delay is average of delivery delay of

all the delivered messages.

3. Average Overhead Ratio: It is the ratio of the

difference between the total number of

messages relayed minus delivered

successfully to that of the number of messages

delivered successfully. This is also a measure

of the additional number of transmissions

required for each message to be delivered from

source to the destination.

4. Cost per Message: It is defined as the total

number of message transmissions divided by

the total number of successfully delivered

messages.

6 EVALUATION OF DTN

ROUTING PROTOCOLS

In this Section, we analyze the performance of five

RTTMM as the mobility model. The main objective

of evaluating these routing protocols is to verify their

effectiveness and applicability in the post disaster

scenario. The Section 4 has shown that RTTMM

offers more network connectivity than existing model

EDRMM so, we have chosen the movement traces of

1000 2000 3000 4000 5000

Average node degree

Simulation time (Seconds)

RTTMM

EDRMM

1000 2000 3000 4000 5000

Maximum node degree

Simulation time (Seconds)

RTTMM

EDRMM

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1000 2000 3000 4000 5000

Clustering coefficient

Simulation time (Seconds)

RTTMM

EDRMM

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Table 1: Configuration parameters of RTTMM and ONE

simulator.

Parameter Value

Simulation area 3000 m

Simulation time 6000 seconds

Transmission range 50 meters

Transmission speed 2 Mbps

No. of devices 100, one hospital and one

relief camp

Message generation

interval

One message/second from

relief workers;

One message every 30-35

seconds from hospital and

relief camp

Message size 25k

Buffer size of relief

workers

5 MB

Buffer size of DMs and

TBs

100MB

Speed of relief workers 3 m/s

Speed of

ambulance/emergency

vehicle

12 m/s

Speed of policeman 7 m/s

No. of events 2

Damage radius 20% of intensity value

Event intensity I

= 4000 , I

= 2000

RTTMM as input to the routing protocols. To

demonstrate the effectiveness of the routing

protocols, we varied the number of devices, buffer

sizes and message sizes. The average of 10 simulation

runs is considered on different input files generated

using RTTMM for a 95% confidence interval.

6.1 Effect of Varying Number of

Devices

Simulations were carried out to evaluate the

scalability of the routing protocols by increasing the

device density. The number of devices varied from 50

to 250 in increments of 50. Experiments were carried

out considering that 80% of the devices are relief

workers, 3% are policemen and 17% are vehicles.

Figure 2(a) shows that delivery probability is

increasing with number of devices for EBR and

MaxProp while it is decreasing for Epidemic, Prophet

and SnW. MaxProp shows the highest delivery

probability due to its wise strategy for the selection of

relay devices as compared to others. EBR has next

highest performance in terms of delivery probability

which takes the advantage of encounter information

of DMs with statically placed TBs. The forwarding

strategy of Epidemic, Prophet and SnW do not work

in this scenario and messages are dropped by the

devices without delivering to the actual destination.

The average delivery delay of all routing

protocols decreases as the number of devices

increases except Epidemic as shown in Figure 2(b).

EBR has lowest delivery delay amongst all routing

protocols. Figure 2(c) depicts the average overhead

ratio, which increases with the number of devices for

all routing protocols excluding EBR. EBR has a low

overhead ratio than others and which does not

fluctuate while increasing the number of devices. The

cost per message is increasing with the number of

devices for MaxProp, Epidemic and Prophet routing

protocols while it remains stable for EBR and SnW as

shown in Figure 2(d). These results show that

MaxProp outperforms in terms of delivery

probability, but at the cost of the other three

parameters. EBR has lower delivery probability than

MaxProp with minimum delivery delay, overhead

and cost per message.

The performance of routing protocols depends on

the availability of buffer space, particularly when

they use multi copy message approach. We have

chosen message size of 50k in these sets of

experiments and buffer size is varied from 1MB to

8MB in increments of one. The buffer size is only

varied for the mobile devices carried by relief

workers. Figure 3(a) shows that delivery probability

increases as buffer size increases for all the routing

protocols. EBR shows the highest delivery

probability up to buffer size of 3MB and becomes

stable at 4MB. MaxProp outperforms at a buffer size

of 4MB onwards as its performance is mainly

depends on the available buffer space.

6.2 Effect of Varying Buffer Size

The average delivery delay is decreasing with an

increase in the buffer size except Prophet as shown in

Figure 3(b). EBR and SnW have the lowest average

delivery delay with marginal increase with the buffer

size. Figure 3(c) demonstrates that an average

overhead ratio is higher for Epidemic and Prophet and

decreasing with an increase of buffer size for all

routing protocols. It remains lowest and stable for

EBR and SnW schemes. The cost per message does

not vary much for all the routing schemes and it

remains mostly steady for EBR as depicted in Figure

3(d).

6.3 Effect of Varying Message Size

When relief workers are gathering information about

the disaster area, they may need to send text as well

RTTMM: Role Based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

RTTMM: Role based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

(a) Delivery probability Vs No. of devices (b) Avg. delivery delay Vs No. of devices

Figure 2: Effect of varying number of devices.

data in the form of pictures and video clips to the

centres and vice versa. Here we check the

performance of routing protocols on varying sizes of

messages from 16k to 1MB in increment of power of

2. Average delivery probability is decreasing as

message size increases as shown in Figure 4(a).

MaxProp shows maximum delivery probability with

message size below 64k as it starts dropping the

message when it is of bigger size due to insufficient

buffer space as discussed in Section 6.2. EBR

performs better than all the routing protocols with big

message sizes.

Figure 4(b) depicts that an average delivery delay

is decreasing with increase in message size except

Prophet and the reason is that there is decreasing of

delivery probability. EBR and SnW have the lowest

average delivery delay than other schemes. The

average overhead ratio is increasing for all the

protocols, but remains the lowest for EBR and SnW

as shown in Figure 4(c). The cost per message does

not fluctuate more in case of all the routing schemes

and it stays mostly steady for EBR and SnW as

depicted in Figure 4(d).

7 CONCLUSIONS AND FUTURE

PLAN

In this paper, we proposed the role based 3-tier

synthetic mobility model, called RTTMM, to mimic

the movement pattern of the rescue team members in a

post disaster situation. RTTMM solves the limitations

of existing mobility models which do not have the

flexibility to assign different behavior and movement

pattern for different team members. RTTMM has been

compared against EDRMM and found to be more

effective and applicable in a post disaster scenario.

We also evaluated the performance of five

existing DTN routing protocols using the movement

traces of RTTMM. The simulation results show that

MaxProp outperforms in terms of delivery probability

for varying number of devices and buffer sizes, but

decreasing with message size. The demerit of this

protocol is that it shows higher delivery delay,

overhead ratio and cost per message. EBR has shown

the next best performance in terms of delivery

probability with the lowest average delivery delay. It

also has steady overhead ratio and cost per message.

There is not any protocol which performs the best

for all the metrics. It is concluded that DTNs facilitate

0.1

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MaxProp

EBR

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No. of nodes

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EBR

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Avg. overhead ratio

No. of nodes

Epidemic

SnW

Prophet

MaxProp

EBR

100

50 100 150 200 250

Cost per message

No. of nodes

Epidemic

SnW

Prophet

MaxProp

EBR

IoTBD 2016 - International Conference on Internet of Things and Big Data

(a) Delivery probability Vs Buffer size (b) Avg. delivery delay Vs Buffer size

Figure 3: Effect of varying buffer size.

(a) Delivery probability Vs Message size

(b) Avg. delivery delay Vs Message size

Figure 4: Effect of varying message size.

0.1

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1M 2M 3M 4M 5M 6M 7M 8M

Delivery probability

Buffer size (MB)

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SnW

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MaxProp

EBR

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1500

1M 2M 3M 4M 5M 6M 7M 8M

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EBR

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150

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1M 2M 3M 4M 5M 6M 7M 8M

Avg. overhead ratio

Buffer size (MB)

Epidemic

SnW

Prophet

MaxProp

EBR

1M 2M 3M 4M 5M 6M 7M 8M

Cost per message

Buffer size (MB)

Epidemic

SnW

Prophet

MaxProp

EBR

0.1

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16k 32k 64k 128k 256k 512k 1M

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Message size

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SnW

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MaxProp

EBR

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EBR

100

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16k 32k 64k 128k 256k 512k 1M

Avg. overhead ratio

Message size

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16k 32k 64k 128k 256k 512k 1M

Cost per message

Message size

Epidemic

SnW

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MaxProp

EBR

RTTMM: Role Based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

RTTMM: Role based 3-Tier Mobility Model for Evaluation of Delay Tolerant Routing Protocols in Post Disaster Situation

communication infrastructure in a post disaster

scenario when network partition is observed in IoT.

The future work is to exploit the movement

characteristics such as planned and scheduled

movement of rescue entities from RTTMM and

utilize them in the forwarding decisions by the

routing protocols in DTNs.

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IoTBD 2016 - International Conference on Internet of Things and Big Data