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Military Mobile Ad-Hoc Networking

Performance Metric Evaluation and Commercial Availability

Thomas Moscon

6/15/2014

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Abstract.................................................................................................................................................7

1 Introduction...............................................................................................................................8

2 Literature Review..........................................................................................................................9

2.1 Networking Fundamentals...................................................................................................10

2.1.1 OSI Model....................................................................................................................11

2.2 Ad-hoc Networking..............................................................................................................12

2.3 MANETs...............................................................................................................................13

2.3.1 Vehicular Ad-hoc Networks (VANET)...........................................................................13

2.3.2 Smart Phone Ad-hoc Network (SPAN)..........................................................................13

2.4 MANET History.....................................................................................................................13

2.5 MANET Achievements.........................................................................................................15

2.6 MANET Challenges...............................................................................................................15

2.6.1 Infrastructure-less design............................................................................................15

2.6.2 Dynamic topology........................................................................................................15

2.6.3 Scalability.....................................................................................................................16

2.6.4 Varied link/node capabilities........................................................................................16

2.6.5 Energy Constraints.......................................................................................................16

2.7 MANET Performance...........................................................................................................17

2.8 MANET Metric Evaluation....................................................................................................17

2.9 MANET Challenges for Military use.....................................................................................19

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2.10 Combat-net Radios..............................................................................................................19

2.11 Multi-hop vs. Single-hop......................................................................................................19

2.12 Metrics.................................................................................................................................20

2.13 Commercial-Off-The-Shelf Products....................................................................................21

2.14 COTS Products.....................................................................................................................22

2.14.1 Harris Corporation.......................................................................................................22

3 Methodology...............................................................................................................................23

3.1 Research..............................................................................................................................23

3.2 Simulations..........................................................................................................................24

4 Proposal Conclusion....................................................................................................................25

5 Simulators....................................................................................................................................26

6 Constraints...................................................................................................................................28

6.1 Radio Propagation...............................................................................................................28

6.1.1 Diffraction....................................................................................................................28

6.1.2 Reflection.....................................................................................................................28

6.1.3 Refraction....................................................................................................................29

6.1.4 Absorption...................................................................................................................29

6.1.5 Scattering.....................................................................................................................29

8 Research Results..........................................................................................................................29

8.1 Metric Rankings...................................................................................................................31

8.2 Research Conclusion............................................................................................................34

9 Simulation Configuration.............................................................................................................35


9.1 Simulation Parameters........................................................................................................35

9.2 Scenario Parameters............................................................................................................36

9.3 Configuration Parameters....................................................................................................36

9.3.1 Packet Size...................................................................................................................36

9.3.2 Mobility Models...........................................................................................................37

9.3.3 Beaconing....................................................................................................................38

9.4 Software Configuration........................................................................................................39

9.5 Simulation Set Configuration...............................................................................................40

10 Simulation Results...................................................................................................................41

10.1 Packet Size...........................................................................................................................41

10.1.1 Packet Delivery Ratio...................................................................................................41

10.1.2 Routing Overhead........................................................................................................42

10.1.3 End-to-End Delay.........................................................................................................44

10.1.4 Throughput..................................................................................................................45

10.2 Mobility................................................................................................................................46



10.2.3 End-to-End Delay.........................................................................................................50

10.2.4 Throughput..................................................................................................................51

10.3 Beaconing............................................................................................................................51




10.3.3 End-to-End Delay.........................................................................................................54

10.3.4 Throughput..................................................................................................................55

11 Simulation Analysis..................................................................................................................56

12 Implementation Summary.......................................................................................................57

13 Future Work.............................................................................................................................59

14 Conclusion...............................................................................................................................60

15 Bibliography.............................................................................................................................62


Student address: [email protected]

Student ID number: 110043629

Award: Bachelor of Information Technology: Network and Security

Provisional thesis title: Military Mobile Ad-hoc Networking: Performance Metric Evaluation and

Commercial Availability

Supervisors’ names: Grant Wigley

Date of submission: November 24th 2014

Thesis Questions:

What are the key metrics and optimal parameters in evaluating performance for Military MANETs?

Thesis Sub-Question

What commercially available products suit the needs for a Military based Combat Net Radio

MANET?


Abstract

Mobile Ad-hoc Networks or MANETs have been around for more than 30 years. Originally developed

for the Military, they have branched out to other niche applications including traffic and transit data

analysis networks, animal tracking, marketing and even social networking. Although they have been

around for a while, there are still a lot of constraints and challenges that surround MANETs,

especially for Military purposes. Size, scalability and management are key challenges involved with

large scale MANETs, but one of the most glaring issues is performance.

The Australian Government along with the DSTO have been researching implementations of MANET

for tactical purposes that would work under a Military scenario. However unsuccessful, as it is

extremely hard to test and evaluate the performance of MANETs under a Military scenario without a

significant testing bed costing lots of time, money and expertise. My research under the guidance

and supervision of the DSTO is to find key metrics and parameters involved in the evaluation of

Military MANET performance using Commercial-off-the-shelf (COTS) products as a reference. Due to

the obscure nature of COTS radio hardware and software, common methods of performance

evaluation are necessary to test whether or not an implementation of a MANET will be feasible on

the battlefield.


1 Introduction

The Defense Science and Technology Organisation (DSTO) are currently undergoing research on

Military Mobile Ad-hoc Networks or MANETs for tactical warfare purposes due to the need for an

infrastructure-less network communication platform. Currently, the Australian Defence Force (ADF)

is using legacy communications hardware with limited bandwidth capabilities and requires a tactical

communication solution that allows convergence with speeds as low as sub megabit.

Unlike other countries like the United States, the ADF does not have the resources to deploy a fully

converged network infrastructure using satellites and other infrastructure to aid communications

within a short timeline on the battlefield. Instead, ‘Combat Net Radios’ and ‘Man packs’

communicating over UHF and VHF frequencies make up the bulk of the network, most of which is

either carried by soldiers or mounted in vehicles.

MANETs have been used and developed around the world for different tactical purposes and even

spawned from the need for better and more robust tactical networks. The reason Militaries look

towards MANETs is that they provide an infrastructure-less, easy and rapidly deployed network with

little to no management. This type of solution proves to be invaluable since there isn’t always the

time or money to setup network infrastructures on the battlefield for warfare. Instead of routers and

switches forwarding packets between layers of the network, all packets are forwarded by the

Combat Net Radios and Man Packs, making every soldier and vehicle a router in essence.

However there are still many challenges and unsolved issues relating to MANETs with research and

development being an ongoing process, one major issue is the scalability of the network. MANETs

have proven to work for small networks or networks with lots of nodes passing information between

neighbors or back to a server, but a fully converged brigade in the army with up to 1500 nodes

would require an extremely robust system. On top of that, the path of the data through the network

can be altered by the design of the Combat Net Radios and how they interface with the other

hardware.


For this, MANETs as a solution needs to be analyzed and tested thoroughly in order to produce solid

evidence about how to evaluate the performance of a network, and what are the key metrics

involved with the overall performance.

2 Literature Review

This research is dedicated to the findings on how to evaluate networking performance for Military

Mobile Ad-Hoc networks and what commercial-off-the-shelf (COTS) products are available as a

solution to this problem. Ad-Hoc networks can be designed for many purposes, but for each

different purpose, they are designed differently in order to suit networking needs in a very specific

way. The nature of the topic is looking to answer what performance metrics are more important for

a Military scenario and how we know those metrics are important.

The basis for this research is on a Military based MANET with up to 1500 nodes/soldiers in at least a

100x100km square block of mountainous and harsh terrain where nodes are constantly moving.

These types of network parameters are hard to build a network around, but so far, it is theorized

that ad-hoc networking is plausible for this type of tactical warfare.

The research will include looking at previous simulations that have been carried out in the same field

to examine how they weigh metrics, evaluation models and frameworks that outline how to

generally evaluate MANET performance, and a comparison of commercially available products to

determine what type of hardware best suits the needs of a Military MANET and what that hardware

does to perform that way.

Network simulations will be conducted in order to analyze the effects of changing parameters to suit

the optimal needs of the network in order to produce an implementation plan for a Military MANET.


2.1 Networking Fundamentals

Generic Local Area Network (LAN) or Wide Area Network (WAN) networks designed for office

buildings and enterprises usually consist of a hierarchical infrastructure. The most basic of these

infrastructures are the three layer hierarchical model, consisting of a core layer providing high speed

routing between major regions of the network, the distribution layer which provides most of the

routing, security and policies and is usually situated at a branch office connecting to the core and the

final layer, the access layer, which connects user devices and servers to the network. This design

model is shown in the figure 1 [12].


2.1.1 OSI Model

Not all networks are designed like this, however they all share a model known in networking as the

‘Open Systems Interconnection model’ (OSI Model) seen in Figure 2 [13].

The OSI Model consists of seven layers of networking each responsible for different tasks in data

transmission within a network.

Application Layer

Reserved for application protocols such as HTTP and FTP. It acts as a user-interface for the user

responsible for displaying images and data in a human-readable format by communicating with both

the presentation and session layer.

Transport Layer

The foundation for TCP, this layers main task is to provide end-to-end communication for

applications within or between networks. It provides reliability and flow-control to ensure data is

transmitted between hosts without failures, corruption or congestion.


Network Layer

Responsible for the routing and forwarding of packets between intermediate routers under

protocols such as Internet Protocol (IPv4/6) and Internet Control Message Protocol (ICMP) through

the utilization of host addressing to distinguish a packets destination.

Data Link Layer

The layer responsible for Ethernet on Local Area Networks (LAN). This layer provides encapsulation

of packets into frames and forwarding data between nodes on the same network with additional

functions such as error detection and correction.

Physical Layer

One of the most complex layers, responsible for network hardware transmission technologies of a

network. Digital Subscriber Line (DSL), Integrated Services for Digital Network (ISDN) and Wi-Fi are

three technologies that reside on the physical layer. Unlike the network layer, the physical layer

transmits raw bits instead of packets over physical links such as copper wires or Cat5 cables.

2.2 Ad-hoc Networking

Ad-hoc networks are an infrastructure-less and decentralized approach to network design. The word

Ad-hoc meaning ‘for this purpose’ assumes that the network is setup for a specific situation or

purpose. In our case, it is for Tactical Warfare. The network is ad-hoc because it does not rely on an

underlying infrastructure with managed routers, servers, access points like a traditional Wireless

Area Network would. Instead, each and every node in the network acts as a router-device,

responsible for forwarding and controlling traffic flow around the network [2].


2.3 MANETs

Mobile Ad-Hoc Networks were originally developed for tactical network communication due to the

dynamic nature of tactical warfare. Military networks have always required a mobile and dynamic

design without reliance on a fixed pre-configured infrastructure, not only to ensure rapid

deployment, but also to keep costs and complexity to a minimum and autonomy at a maximum.

MANETs create a framework suitable for this type of network design by providing an infrastructure-

less network built mainly upon multi-hop peer-to-peer technology that allows nodes to

communication over long distances and beyond Line of Sight.

2.3.1 Vehicular Ad-hoc Networks (VANET)

Vehicular Ad-hoc Networks are becoming increasingly popular today, with modern cars carrying

them around for safety purposes. This basically turns any car into a wireless node allowing vehicles

within 100 to 300 meters to connect and create a network. Vehicular Ad-hoc Networks also extend

into the Military where tanks and special armored vehicles act as a distribution layers within the

network in order to provide a backbone for a tactical MANET.

2.3.2 Smart Phone Ad-hoc Network (SPAN)

Smart Phone Ad-hoc Networks use existing wireless technology such as Bluetooth and Wi-Fi on

smart phones in order to create peer-to-peer networks that utilizing cell carrier network

infrastructure such as access points, hubs or traditional routers. Applications for this can include

social networking, free internet, local area networking and marketing endevours.

2.4 MANET History

The concept of a military MANET over radio originated from an agency known as the Defense

Advanced Research Projects Agency (DARPA). In 1973, the group researched packet-switched radio

communication in order to provide mobile network access to computers and terminals within a


mobile environment [1]. This research was motivated by the need for tactical communication on the

battlefield, and to this day is still being researched for both military and commercial markets.

Packet Radio Networks were the first generation of ad-hoc networks back in 1973. The network

consisted of firmware pre-loaded packet radios that had minimal functionality, but was able to

communicate to other radios using early radio frequency technology. This technology implemented

the physical layer, data link layer and network layer of the OSI model.

The firmware was capable of providing network metrics such as power usage, signal to noise ratio

and even had a basic error detection system which forced a retransmission of dropped packets. The

routing, although primitive at the time, allowed for rapid and automatic deployment of the network,

which was and still is a key factor for tactical communication [6][14].

Second generation ad-hoc networks arrived in the 1980s up to 1993, which were developed and

implemented as a part of SURAN (Survivable Adaptive Radio Network Programs). This enhancement

improved radio performance by making the networks smaller, cheaper, efficient and more secure [6]

[14].

A project by the name of GloMo (Global Mobile Information Systems) further enhanced mobile

networking by researching self organizing/self healing networks aimed at intelligence information

systems for the deployment of forces.

The next development in the second generation of ad hoc networks was NTDR (Near Term Digital

Radio Systems), the purpose of which was to provide self-organizing mobile communication

between Army Battle Command System automated systems and units at a brigade level and below

[6].

Finally third generation MANETs, from the 1990s until present day exploded with the invention of

portable laptops and other commercial communication devices based on packet radio systems [6].


MANETS were implemented during this generation and have since been developed for an array of

applications.

2.5 MANET Achievements

Despite the Military background of MANETs, in modern day society, MANETs are used for many

different applications in order to pass information around users. In a lecture by Kim Smith [3], one

particular example of a MANET is Dieselnet, which monitors users who get on and off buses in order

to map out where people are coming and going so that the bus system can create better routes.

2.6 MANET Challenges

MANETs propose many challenges due to the way it is inherently designed. In a MANET, all nodes

are independent of each other, and all operate in a peer-to-peer mode.

2.6.1 Infrastructure-less design

This infrastructure-less design adds difficulty to network management, making it challenging to

detect and manage faults. It is also difficult to analyze the performance and utilization of the

network, as using this type of sniffing tool will add un-necessary congestion to the network. In a

Military MANET, you will need all the performance you can get.

Having no infrastructure also diminishes the usefulness of the network. With larger networks, it will

be increasingly difficult to converge, especially with limited bandwidth and hardware.

2.6.2 Dynamic topology

Because nodes in a MANET will always be moving, the topology of the network will be dynamic,

causing frequent route changes which can result in packet loss. One of the biggest challenges for

MANETs is finding a routing protocol that has the right tradeoff between route discovery and

overhead in order to provide optimal and efficient performance.


2.6.3 Scalability

Scalability is still an unsolved issue. Challenges include addressing, routing, configuration

management, interoperability, etc. The larger the network becomes, the increasingly difficult it

becomes to maintain. In order to run a MANET with over 64 nodes, backbone infrastructure will

need to be implemented in order to keep the entire network healthy. Anything network size beyond

that will only stay alive if there’s an extremely minimal amount of traffic and network utilization.

2.6.4 Varied link/node capabilities

Varied node capability refers to the individual capability of each node and how any node can be

responsible for network congestion. Imagine a MANET where the most popular route includes the

same center node, and once that node starts to lose battery it charges down its CPU to save power.

That node is now liable for slowing down all traffic that passes through it which could be almost all

traffic.

This depends heavily on the hardware used and the topology of the network at any time. Using

mismatching hardware is also not advised, as this can have the same effect.

2.6.5 Energy Constraints

MANETs rely on each node being a router; however mobile devices especially Combat Net Radios

used in the Military have limited processing power and energy constraints. This goes against the

design of every node being a router, as forwarding packets constantly can drain energy fast.


2.7 MANET Performance

The routing performance for MANETs differs for each routing protocol. For example, a routing

protocol such as Dynamic Source Routing (DSR) may incur less overhead than a protocol such as

Optimised Link State Routing Protocol (OLSR) due to the fact that it will transmit traffic data only

when there is data to be sent, where as OLSR floods the network with control and traffic data aimed

at keeping the routing tables updated as much as possible [7][17][26].

However this doesn’t always mean that certain routing protocols are better than others, they may

perform better in certain scenarios and for different purposes.

Currently there is no best implementation of a Military MANET or any MANET as they are ad-hoc

based. The performance of MANETs are being constantly researched with new implementations of

routing protocols, new parameter configurations and new hardware being built to support those

implementations better.

However, we are not there yet, and MANETs are still only being utilized in very discrete network

implementations with nothing major. The easiest way to take advantage of MANETs would be the

mobile phone and social networking market. Nothing major in Military warfare has been announced

publicly that involves a purely structure-less MANET implementation.

2.8 MANET Metric Evaluation

In an RFC written by S. Corson and J Macker [4], the authors distinguish a difference in metrics

between a routing protocol and the network context itself. To a further degree, it is also stated that

separate qualitative and quantitative metrics are evaluated for a network protocol.

Qualitative metrics explained were sleep and security, etc., where as quantitative metrics include

throughput and delay, efficiency which looks at data/control bits sent and received, and route

acquisition time. Finally, metrics that are important to a routing protocols overall context are


network size, network connectivity, traffic patterns, mobility, link capacity, and topological rate of

change and others.

There are two main ways to evaluate the performance of a MANET. The first approach uses

measurement techniques in order to evaluate a real prototype network, usually a test bed, which is

important in realising constraints and issues with a network that may not show up in simulations.

One of the largest testbeds with up to 30 nodes is the Uppsala University APE testbed which

revealed problems relating to different transmission ranges for control and data frames, a problem

known as the ‘communication gray zones’ [2].

The other approach to evaluating the performance of a MANET is via a simulation model, usually

simulated on either OPNET or NS-2. Many simulation models have been constructed in order to test

the performance of a MANET [8][9][10][11][23][24][25], but it is important to note that a simulations

do not accurately represent the actual performance of an entire network, rather they aid us in

answering specific questions about the network or help us to diagnose imperatives in the design.

Mobility models in particular aid in discovering the effects of mobility on a network, though limited,

the results these types of models can still provide useful and intelligence evidence.

Lastly, using simulations as a method of evaluating a large network can sometimes be deceiving and

a large understanding of networking is required in order to evaluate the data correctly. Many

researchers delve into the credibility of simulations for MANETs because of this issue [15].


2.9 MANET Challenges for Military use

Many popular MANET algorithms that were designed were done so for small fixed networks,

however a lot of tactical MANETs are designed with thousands of nodes in mind which makes

scalability still an issue that has unresolved challenges that range from configuration management,

security, interoperability, addressing and most of all routing.

In the Military, a term coined SWAP (Size, Weight and Power) is an extremely important aspect for

operating in tactical warfare and adds an extra budget for the types of physical networking devices

plausible out on the field [5][16]. Reducing the size, weight and power of each device on a soldier is

crucial for a more mobile and logistical mission. However, this limits the amount of processing power

allowed on each node which in turn limits the effectiveness of the MANET since every node must act

as a router as well as an end-user device. This proposes a tradeoff between efficiency and reliability.

2.10 Combat-net Radios

Radio’s used for tactical purposes are generally known as ‘Combat-net Radios’ (CNRs). These radios

are primarily push-to-talk handheld devices that utilize certain waveforms in order to transmit and

receive radio signals. CNRs most popularly operate on the Very High Frequency (VHF band) and the

Ultra High Frequency (UHF) band, meaning they only transmit radio signals between the frequencies

of 30-300 MHz and 300-3,000 MHz respectively.

There are many factors that determine the performance of a CNR. These include power, frequency,

line of sight and environmental interference. For the factors we as humans can control, the more

power used, the greater the range of transmission, but most radios reach up to 10 watts.

2.11 Multi-hop vs. Single-hop

Networks can be also be defined into single-hop or multi-hop. Single-hop refers to a network where

there is only one hop between the source and destination, usually a default gateway or router.


Multi-hop networks are used when there is no default router available, much like a traditional

MANET where every node acts as a router to forward data around the network.

This approach is usually preferable for rapid deployment in hard-to-wire areas making it ideal for

tactical MANETs as it is impossible for the military to wire a network backbone in every location of

operation. It is also required in order to extend coverage of the network through multi-hop

forwarding.

2.12 Metrics

In networking, there are many parameters that can affect the performance of a network, which are

known as metrics. Some metrics are more important to analyse than others when diagnosing issues

or slowness within a network, making it a network administrator’s job to know which metrics are

important for what type of application or network usage. When diagnosing the performance of a

network, network tools are commonly used to display metrics between devices and the overall

network, but can also be output from simulations in order to analyse the performance of a network

in the scenario being simulated.

In the simplest cases, downloading large files from somewhere is a scenario in which a large

throughput is desired, making the speed in which the files are received quicker. Alternatively, playing

a video game over the internet requires extremely low latency, where no delays are present and the

actions/data sent by the client to the server and vice versa and almost synced. With a high latency,

lag would occur and the game would become unplayable.

OPNET, one of the most common networking simulators, is able to output a variety of metrics

including throughput, utilization, number of hops per route, route discovery time, average power

and retransmission attempts. Some of these metrics may affect the performance of the network

more, and others may be barely useful at all. These types of simulators can tell us valuable

information about how a network would perform in a certain scenario, but require a decent


knowledge of the network being simulated in order to properly diagnose the results to make

decisions and answer questions based upon them.

2.13 Commercial-Off-The-Shelf Products

Many Governments purchase what is known as Commercial-off-the-shelf products, meaning

products that are publicly available which can be bought under a government contract. The

advantage of this purchase is the reduction of development, maintenance time and a saving on cost.

Notably, COTS products can also provide a standardized approach instead of a custom in-house

development approach which would add a significant cost to time and money. Standardization also

means that there is already more than enough documentation and support for the product.

It is believed that COTS products are of a higher quality than custom developed products due to the

competitive nature of the marketplace, however true, it may lead to some security issues with the

purchase. Notably in IT especially, COTS products can pose security risks due to the public nature of

the product and the integration with other products. Although this is more prominent for software

products than hardware it is still worth noting that COTS products have been the cause of safety

concerns for the Military in the past.


2.14 COTS Products

Few companies design commercial products for MANET solutions, as they are not a popular choice

for industry networks, however there is still Research and Development carried out for Tactical

MANETs, below are a few examples of some MANET technologies.

Cisco

- Radio Aware Routing for Mobile Networking

Bluetronix (Bluestar)

- R&D for Government Military MANET solutions

- SWARM Intelligent Routing

Trellisware

- Military MANET development, Robust Hardware

- Converged physical/network layer waveform

- Tactical Scalable MANET (TSM) Waveform

Harris

- Another Military hardware provider

- Joint Tactical Radio System (JTRS) certified products

2.14.1 Harris Corporation

Harris Corporation is by far the leading supplier in Combat Net Radio and Military Radio Systems.

Their products are used by the US Army, U.S. Marines, US Air Force in Iraq and Afghanistan and even

US Navy Explosive Ordnance Disposal (EOD) teams.

Their products boast secure, interoperate and extremely featured hardware with a large waveform

support. They even produce wearable computers that provide antenna support, capable of

streaming voice and video.


The Combat Net Radios such as the RF-7800V VHF provide up to 10 watts of power allowing for long

distance communication of the VHF and UHF band. Their man packs are just as impressive, with

hardware for all layers of the network.

3 Methodology

Research (Phase 1):

- > Compare previous simulations

-- > Rank metrics based on data collected

-- > Quantitative only, no Qualitative

Simulation (Phase 2):

--- > Prepare a ‘Baseline’ network topology

---- > Change available parameters of the network

Analysis (Phase 3):

----- > Analyze effect on each key metric

------ > Propose optimal Implementation

3.1 Research

The first pool of results will aim to compare the simulations and data gathered from existing public

papers to get a rough overview of the most important and key metrics involved with the

performance of MANETs. Only quantitative data will be collected as qualitative data is not relevant

in a Military scenario. The only significant qualitative metric would be security which is a non-issue

for the military.


Metric rankings will be collected from at least 10 different sources, the sources being mostly

conference papers, journal articles and published books. Analysis will be made of the findings in

order to validate the use of each metric.

As this method is unreliable in giving a sure answer to the question with the possibility of mixed

results and a mixture of different test scenarios, it is only done for academic reference, and to see if

any patterns emerge that may provide the research with useful information.

Additionally, the mission critical nature of the Military will also be taken into consideration when

ranking these Metrics.

3.2 Simulations

There are two types of simulation techniques highlighted below. The one being used for our

research will be the model approach, as we don’t have access to real testing beds.

Measurement Techniques

- They are only applied to real systems/prototypes

- Very few test beds found in literature

- Uppsala University discovered “communication grey zones” in specific geographic areas

Model Approach

- Study of system behavior by varying it’s parameters

- Scenario based, not full spectrum

- Large number of simulation models have been developed

- Mobility models allow analysis of the effects of mobility on the network, though limited


Simulations will be conducted using the most popular network simulator, NS-2. The conditions for

the simulations will be multiple tests with a chosen best routing algorithm. First, a base simulation

will be conducted with 16 nodes in order to achieve a decent network benchmark. From there,

parameters of the network will be altered in order to analyze the effect it may have on certain

metrics of the network.

The effect on the performance of the network will be measured by sending the same artificial traffic

around the network for each test, and analyzing performance metrics such as packet delivery ratio,

routing overhead, end to end delay and throughput.

Once all the simulations have been carried out as carefully as possible, the data gathered will

hopefully show which metrics are affected the most, and which ones are affected the least, giving a

clear idea as to optimal values for the network parameters configured. From this, graphs and tables

can be deduced that show how much certain metrics are affected to give insight into an optimal

setup.

4 Proposal Conclusion

It is obvious that MANETs have potential as a solution for tactical communications but not without

many issues and challenges to overcome such as scalability, network management, terrain

interference, bandwidth and hardware limitations, and overall performance.

However there is no doubt a need for this type of network in the military due to the lack of possible

ways to deploy networking infrastructure on the fly. Utilizing MANETs means cheap and rapid

deployment for the army in the easiest way possible.

Using Commercial-off-the-shelf products is an obvious choice for governments as it cuts cost,

development and maintenance time. As well it provides a more standardized approach allowing for

better support for the product and a better performance guarantee.


Much of the research done for MANETs however doesn’t explain exactly how to measure its

performance. Most of this is done by network tools through the use of algorithms, but a lot of those

tools were intended to measure the performance of generic LAN scenarios and not MANET scenarios

where mobility plays an overarching factor on the performance, as well as the conditions that are

added due to a military setting.

This advocates the question “What are the key metrics and optimal parameters in evaluating

performance for Military MANETs”.

5 Simulators

The choice of the network simulator is important, as there are proven distinct differences between

the way they are coded, how they handle traffic and how accurate they are overall. The number of

commercially available network simulators is minimal, OPNET being the most commercial and

expensive one, where as NS-2 is the most commonly used since it’s free and open source. There

have been many discussions on the accuracy of network simulators and the importance of this

choice in many papers [27][28][29].

Research papers have provided an overview of the simulation software used in literature [27], along

with a granularity rating referring to how detailed the software is from a technical standpoint. This is

important in that it gives researchers the ability to make better decisions about which software to

use based on the level of detail required for their application.

For instance, very high level research based upon simple networking tasks won’t require much

granularity to provide accurate results for the experiments. Alternatively, extremely low level

research such as the tests carried out in this paper requires a finer level of detail in the software

coding and in some cases requires the ability to configure certain parts of the code in order to

achieve desirable results other network simulators aren’t able to provide.


For this reason, NS-2 is the most attractive choice for our research, as its modular approach allows

for a lot of configuration options and a scope into how the simulator works internally.

Simulator Name Granularity

NS-2 Finest

QualNet Finer

OPNET Fine

Glomosim Fine

GTNets Fine

OMNet++ Medium

DIANEmu Application-Level

So as a result, one of the biggest challenges in research today is finding how much detail is needed

to portray accurate results in simulations. But do we need to go down to the assembly level of the

hardware in order to portray a completely realistic network environment?

Even if that were possible, it may not have any significant impact on the results; however we do not

know that this is true because we are not that far ahead yet with simulation abstraction.

Another case that comes to mind is the validity of the packages used on some of these simulators.

For example, NS-2 being an open-source piece of software and is both coded and supported by the

community who writes all the modules for it. The routing protocols, the configuration scripts, the

source files, the tracing files, result calculation scripts and the rest are all written by the community

so how can anyone be certain of the validity of these modules of software.

Every few months these modules are being patched, fixed and updated to better reflect the real

protocols, so how valid does that make any simulation done before the patch.


6 Constraints

Through research of the way MANETs have been used in the past, it was obvious that going past 100

nodes within the network was not applicable, especially under a Military scenario. Reasons for this

include congestion and routing overheads, significantly high packet loss and poor route discovery.

Not only did it seem to be impossible in a real-time scenario, but NS-2 and other simulation software

has trouble simulating past 100 nodes. In some cases during the simulation phase of our research,

the entire network would collapse, leaving pointless data as a result.

6.1 Radio Propagation

Another constraint is the lack of ability to simulate real radio propagation using software. The reason

this is an important constraint is because radio waves suffer from a lot of physical interference when

traveling through the air (propagating) which can lead to significant performance drops. Even a static

laptop in a regular household connecting to the home wi-fi will suffer from jitter every now and

then.

Radio waves suffer from three different types of interference, diffraction, reflection, refraction,

absorption and scattering.

6.1.1 Diffraction

Diffraction occurs when a wave passes through a slit or multiple slits of an interfering medium such

as a wall.

6.1.2 Reflection

Reflection is simply a change in direction of a wave at the point of interference between two

mediums, e.g bouncing off the floor. Models have been developed to try to simulate this

phenomenon such as the ‘Two-ray ground-reflection model’ built into NS-2.


6.1.3 Refraction

Refraction is the change in direction when passing into a different transmission medium, such as

passing from air into water. However radio waves suffer from a type of diffraction known as ‘edge

diffraction’ or ‘knife-edge diffraction’ which occurs when waves pass over a mountain where line of

sight (LoS) is not available. If the terrain is especially mountainous or suffers from LoS breakage, high

antenna power or increased signal strength is needed to overcome its effects. Higher frequencies

such as VHF or UHF have more trouble passing over hills where as lower frequencies used in the HF

band for example will pass over them a lot easier.

6.1.4 Absorption

Absorption is the basically where radio waves become absorbed by matter. This means that earth or

other mediums such as water will block waves from passing through it depending on the wave

frequency. Low frequencies are able to pass through brick and very low frequencies through sea-

water which is why submarines use VLF band a lot. As the frequency rises, so does the absorption

rate.

6.1.5 Scattering

Scattering is as it sounds, waves simply deviate from a straight trajectory and take on multiple paths

based on the medium they pass through.

8 Research Results

Through a number of research papers, the most important metrics are undoubtedly Packet Delivery

Ratio, Overhead and End to End Delay. However, there are other discrete metrics that have an equal

or higher importance based on the nature of MANETs and how they add to the efficiency of any

respective routing protocol.


Each metric should also be weighed against their real-time importance on the battlefield. This means

factoring in the relevance of ‘Message Clarity’, ‘Time Severity’, ‘Environmental Interference’ and

‘SWAP (Size Weight and Power)’.

Message Clarity

Message clarity refers to the idea that any message given by a soldier should always be received in

full, and playback clearly over all received radios with minimal artifacts. This is an extremely

important factor to consider as a network radio operator as the difference between hearing a

message clearly and not hearing it at all could be life and death.

Time Severity

Time severity refers to the importance that any message sent over the network should reach it’s

destinations in an extremely timely manner. A call made out by a soldier for an attack, a retreat or a

position status will need to be heard by all receivers immediately in some cases, and the network

carrying this order over radio should not be the bottleneck of a tactical operation.

Environmental Interference

As explained previously, radio waves suffer from many different types of interference, diffraction,

reflection, refraction, absorption and scattering. This factor affects the network the most. As these

types of interferences are unable to be simulated via software, it becomes impossible to determine

and calculate their true effects on a MANET. Additionally, terrain can drastically change between

scenarios, for example mountainous areas will have a different negative effect on a MANET than

areas of rainfall. The closest tool we have to simulating a real environment is Mobility Models which

simulate moving nodes within a network.

SWAP (Size Weight and Power)

Military standards hold severe restrictions on the size and weight of carried hardware by soldiers. It

is usually recommended to have Combat Net Radios that are less than 10 inches and output no more

than 4-6 watts. Using radios with unnecessary complex waveforms with increased bandwidth will eat


up CPU and power, so keeping software choice in mind is also advised. Additionally, having

intelligent software that dials voltage and frequency back during idle states as well as manages

resources efficiently should be almost a standard by now.

8.1 Metric Rankings

The following results are a collection of Network Performance Metrics ranked on their use in

literature when analysing Mobile Ad-hoc Network simulations. The application for which the

simulation results were intended to aid was also a contributing factor, as VOIP can be a very

demanding application. As most papers in literature do not focus on Military scenarios, the

relevance of ‘Message Clarity’, ‘Time Severity’, ‘Environmental Interference’ and ‘SWAP (Size Weight

and Power)’ also came into mind.

Packet Delivery Ratio (%) – Very High

Total Number of Packets Received / Total Number of Packets Sent.

This metric is the most important, as it directly reflects packet loss as well. Losing packets results in

loss of time, wasted network utilization, twice the CPU and power being used in order to re-send the

packets, and an overall diminishing of the networks performance. The cause of lost packets comes

from a packet being unable to find its destination before the ‘Time to Live’ timer, or being dropped

during mid-air transmissions due to wave interference.

Average Hop Count (n) – Very High

Average number of nodes a single packet passes through.

With a relatively small network, e.g. a node size of 16, having a low hop count should be attained

easily. With wireless technology, passing packets between 2 wireless nodes is already variably

slower than through a cable, so the more you increase that number, the lower the performance will

be at an exponential rate. This metric alone is responsible for affecting most other performance

metrics on the list, with each transmission adding delay and slowing down throughput significantly.


If the network has a high hop count, it is usually due to a poorly performing routing protocol or bad

routing configuration, the latter being uncommon, as MANET configuration is minimal.

Route Discovery Time (ms) – Very High

Average time in Milliseconds for a route to be discovered from source to destination.

The metric refers to the time it takes for a route to be updated in the routing table. Specifically, the

entire ‘Round-Trip-Time’ (RTT) from when a radio sends a query packet to its destination (DEST) until

it receives a REPLY and the route is successfully added to the SRCs routing table. Slow discovery time

is usually the result of a bad routing protocol or faulty nodes, and is usually not caused by a slow or

congested network, as control packets are inherently prioritized before data packets in most routing

protocols [33].

Average End-to-End Delay (ms) – Very High

Average Time in Milliseconds for packets to travel from source to destination

End-to-End Delay or Latency is the time it takes for a message to travel from the source to the

destination. Directly effecting ‘time severity’, it is important that there isn’t a large amount of delay

in a Military scenario, as commands need to be given and followed in a timely manner. Delay is

increasingly affected by high hop count, network congestion and queuing.

Overhead (%) - High

Total Number of routing packets sent / Total Number of Packets Sent.

Routing overhead or Control packet overhead refers to the amount of control/routing packets sent

relative to data packets. If there are a lot of routing packets being sent around the network, then it is

indicating that the network is not performing efficiently. In general LAN networks, protocols are

configured in ways that aim to reduce routing overhead by intelligently routing networks, however

with MANETs, the level of overhead is usually up to the routing protocol being used. Low overhead


means that data is able to reach its destination with minimal work, however some protocols require

higher overhead in order to ensure packet delivery ratio and availability is high.

Jitter - High

Deviation of packet delay over a period of time.

Jitter has a very distinguishable effect on VOIP. It is primarily the variation in the arrival of packets

from the source to the destination. When packets start to arrive from a voice message across the

network with ease, but only half the message is played back before the rest reaches the destination,

the effects of jitter can be seen. Usually caused by queuing, network congestion, and route changes,

jitter can have devastating effects on VOIP, directly effecting ‘time severity’ and ‘message clarity’.

Average Throughput (kbp/s) - Low

Average speed of packets through the network

Throughput is not necessarily important for VOIP application, however depending on the quality of

the codec used for the voice data, a minimum data rate is required in order to transmit the data

smoothly across the network. The higher the voice quality, the more throughput needed in order to

transmit the data across at once.


8.2 Research Conclusion

Looking at the inherent nature of VOIP across all services, we see that the same metrics are equally

important and key. With VOIP being the main application for Military MANETs other than signaling

and remote control, it is only natural that the key metrics for a Military MANET share those with

VOIP.

Most VOIP services have their own Service Level Agreement in order to ensure that their customers

receive maximum VOIP quality [31]. These rules can also apply lightly to Combat Net Radios,

however will not be achieved so easily.

Through extensive lab testing at Cisco Labs, it is reported that VOIP quality degrades when jitter

exceeds 30 ms [32]. There are also guidelines suggesting that a VOIP supported network should not

exceed 150 ms end-to-end delay in any direction. The bandwidth however depends on the quality of

the voice codec used and its sampling rate. For Combat Net Radios, this requirement shouldn’t pass

anywhere over 13kbit.

As for packet loss, to achieve crystal clear VOIP quality, you would need anywhere near 99% packet

delivery ratio. However this is simply not achievable on the battlefield and with the lower bit rate

that Combat Net Radios have, it is easier to conceal loss for longer before the quality is audibly bad.

This is yet another factor that depends on the proprietary hardware and software of the radios.

Concealing up to 40 ms of lost data may be achievable given good hardware, which would result in a

loss that could not be concealed every minute or two [32].


9 Simulation Configuration

The simulations shown in this section were done in order to provide an analysis of how different

parameters affected the network when tweaked. The analysis process consisted of four steps:

- What parameters should be configured?

- What’s the optimal value for those parameters?

- What Metrics do those parameters effect?

- Are those Metrics important for MANET VOIP?

The routing protocol used for all simulations will be OLSR as it is a proactive routing protocol capable

of sending and receiving HELLO packets which help to provide some premise when analyzing the

effect different parameter configurations have on routing overhead.

It is also the most balanced of the routing protocols, boasting a steady performance over all network

sizes without dipping in performance significantly compared to the other MANET routing protocols.

9.1 Simulation Parameters

The configuration for the NS-2 Physical Layer:

Channel Wireless

Radio Propagation TwoRayGround Model

Interface Wireless

MAC Protocol 802.11a

Antenna Type Omni

Interface Queue Type DropTail/PriQueue


9.2 Scenario Parameters

The parameters available for a simulation scenario:

Dimensions x – x

Max Queue size (in packets)

Routing Protocol (DSR, AODV, DSDV, OLSR)

Number of Nodes (1 – 256)

Movement Model (Random Waypoint, Gauss-Markov, Reference Point Group Mobility).

Traffic Model (Type/Speed/Max Connections)

Simulation Duration (seconds)

9.3 Configuration Parameters

There will be 3 sets of simulations done in order to discover an optimal MANET configuration. They

are Packet Size, Mobility Model and Beacon Timer.

9.3.1 Packet Size

The default packet size used in NS-2 is 512 bytes. The three parameter configurations we will be

using in the simulations are 512 bytes, 1024 bytes and 2048 bytes. Using a smaller packet size than

512 bytes will not have much of an impact on the network, however increasing it may result in a

reduced overhead and increased throughput, especially in smaller networks.

The negative effects of this could lead to lower Packet Delivery Ratio, packet corruption, network

contention and higher latency. Altering this parameter may also lead to discovering attributes about

a routing protocol without primary knowledge of it.


9.3.2 Mobility Models

There are three Mobility or Movement models being simulated for this research, the Random

Waypoint Model, Gauss-Markov Model and the Reference Point Group Mobility Model.

Random Waypoint Model

In the Random Waypoint model, nodes are randomly spread across the area of field. Each node has

a pause time in which it is stationary for, once this time has expired it then chooses a random

destination and speed (within the set limits). Upon reaching the nodes destination at the chosen

speed, it pauses again for the specific pause time and waits for it to expire again [38].

It is not an overly realistic simulation, however it provides enough mobility to simulate the work that

the routing protocols will have to do in order to account for dynamic nodes within a MANET. For this

reason it is not scrutinized too heavily in the field of network research and is actually used more

commonly than other mobility models.

Gauss-Markov Model

The Gauss-Markov model takes on a more realistic approach to random movement. First of all, the

model has a deeper level of configuration as it is inherently more complex. It works quite similarly to

the Random Waypoint Model except that at random intervals of time it changes its next location

based on current location, speed and direction of movement [38].

Reference Point Group Mobility Model

The Reference Point Group Mobility Model or RPGM model is based around nodes moving as a

group. This can be relevant to Military scenarios where platoons of soldiers are moving as a group

around a leader. The way this model works technically is that a leader of a group of nodes will

choose the direction and speed to move at with the rest of the group following but deviating slightly

for added realism [38].


This model is adapted in other applications such as firemen, disaster teams, rescue teams and other

emergency scenarios that require groups of people to stick together. This model is capable of

providing the most realistic results for scenarios such as this.

Mobility Model Summary

The reason these three mobility models were chosen was to provide varied context for the

simulations. The first model ‘Random Waypoint Model’ was used to provide the standard baseline

model, as it is widely used and accepted as the standard model in research. It is not overly complex

and it provides enough mobility to put routing protocols under stress.

Finally, using more intense mobility models can reveal information about the repair time of a routing

protocol.

9.3.3 Beaconing

The 3 beaconing frequencies being tested are 1 second, 2 seconds and 5 seconds intervals. The

default beacon timer for the OLSR routing protocol in NS-2 is 2 seconds. Increasing the beacon timer

will most likely reduce overhead on the network but result in less intelligent routing and possibly a

loss of useful routing information. Lowering the beacon time will increase routing overhead but do

the exact opposite and provide smarter routing, but at a large trade off.


9.4 Software Configuration

The following is a list of software used to conduct the simulations.

Operating System: Lubuntu 12.04

Lubuntu is a fast and lightweight version of Ubuntu. The simulations were carried out on a virtual

machine blade server funded by the UniSA IT Department.

Simulation Software: NS-2 2.35

NS-2 2.35 is the latest version of Network Simulator 2. It is a discrete event open source network

simulation tool.

Movement Model Tool: BonnMotion v2.1a

BonnMotion is a Mobility scenario generation tool aimed at implementing mobility models into

network simulators for mobile ad-hoc networks in order to produce realistic scenarios for network

research.

Scripting Language: TCL/C for Simulation Scenarios

TCL is a powerful dynamic language used for a lot of networking research. It works natively with C.

TCL scripts provided the main resource behind the simulation scenarios and simulation

configuration. It acts as the main platform in which NS-2 performs on.

Trace File Generation: AWK Scripting Language

AWK scripting language is generally used for data extraction and reporting. It was used in this

research for analyzing and extracting trace file information which provides the results for the

simulations done in NS-2.


9.5 Simulation Set Configuration

The configuration for the simulations successfully completed in NS-2.

Nodes 16 32 64

Seconds 100 100 100

Width 500 1200 1500

Length 500 1200 1500

Max Connections 32 32 32

Packets/Sec 4 4 4

Que Limit 100 100 100

Beacon Time (s) 1/2/5 1/2/5 1/2/5

Packet Size (bytes) 512/1024/2048 512/1024/2048 512/1024/2048

Movement Random Waypoint

model,

Gauss-Markov model,

Reference Point Group

Mobility model.

Random Waypoint

model,

Gauss-Markov model,


Mobility model.

Random Waypoint

model,

Gauss-Markov model,


Mobility model.


10 Simulation Results

Below are the results of the simulations carried out in NS-2. The three separate sets of results come

from changing Packet Size, Beacon Time and Movement Model and comparing Packet Delivery

Ratio, Routing Overhead, End-to-End Delay and Throughput.

10.1 Packet Size

Three different packet sizes were used for these tests, 512 bytes, 1024 bytes and 2048 bytes. With

an increased Packet size, additional data can be sent per packet increasing the weight of every

packet resulting in a larger tangible loss when a single packet is lost over the network.

10.1.1 Packet Delivery Ratio

The results seen below reflect this by showing that an increased Packet Size has an increasingly

devastating effect on the Packet Delivery Ratio with increased node size, see Fig 3. For the 32 node

networks, Packet Delivery Ratio drops 20.29% between a 512 and 2048 Packet Size, and the 64

node network, a difference of 31.02% PDR is achieved. As for the 16 node network, Packet Delivery

Ratio seems to stay fairly steady over the change in Packet Size, with a total difference of 3.31%,

which is negligible.

This means that on small networks, an increased Packet Size has little to no effect on the Packet

Delivery Ratio. The reason for this is due to the already small amount of packet loss we see on a

small network using a 512 Packet Size. If smaller packets are sent, then it is easier for them to travel

around the network, causing less congestion and allowing more varied traffic to pass through nodes.

Large packet sizes mean that a single packet loss has a much more devastating effect relative to the

Packet Size multiplier.


Fig 3. Packet Delivery Ratio Simulation Results.

16 Nodes 32 Nodes 64 Nodes0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Packet Delivery Ratio (%)

Pack

et D

eliv

ery

Ratio

(%)

10.1.2 Routing Overhead

A positive effect is seen on Routing Overhead when the Packet Size is increased, see Fig 4. A small

network loses about 28% Routing Overhead between 512 and 2048 sizes, more than halving the

overhead traffic on the network. A 32 node network loses 90.11% Routing Overhead, halving overhead

almost exactly and for a large network 93% Routing Overhead is lost, resulting in only an approximate

20% difference.

It is obvious from these results that the larger a network grows and the higher the overall overhead is,

the less positive effect an increased Packet Size has on the network. Especially for 32-64 node networks,

the difference in Routing Overhead loss between 1024 and 2048 packet is quite small.

However for a small network, receiving a 65% drop in Routing Overhead, the highest drop out of all 3

networks, an increased Packet Size would prove to be extremely beneficial in reducing CPU usage of

devices on the network, however since the difference in Routing Overhead between a 1024 and 2048


packet size for a small network is only 6%, a compromise could be made to use 1024 judging by the

effects Packet Size has on other performance metrics.

The reason Routing Overhead is reduced with a higher packet size is due to less data packets being sent

overall. If the size of the data packets is higher, then less will need to be sent in order to transmit a

message across the network, therefore directly reducing the amount of control packets needed to govern

that data across the network. This drop in Routing Overhead is also diminished with higher amounts of

Routing Overhead due to the increased packet loss seen with a higher Packet Size, see Fig 3.

Fig 4. Routing Overhead Simulation Results.

16 Nodes 32 Nodes 64 Nodes0%

50%

100%

150%

200%

250%

300%

350%

400%

450%

500%

Routing Overhead (%)

Routi

ng O

verh

ead

(%)


10.1.3 End-to-End Delay

The effects of an increased Packet Size on End-to-End Delay prove to be varied between all node

sizes, see Fig 5. Over all network sizes, the End-to-End Delay for a 512 Packet Size shows to be

relatively stable. For a small network, an increased Packet Size shows an almost negligible change in

End-to-End Delay, having little to no effect on the delay.

However, with an increased network size, the End-to-End Delay becomes too high for VOIP

transmission with a 1024 and 2048 Packet Size, showing an exponential increase of End-to-End

Delay for 1024, and a sharp rise for 2048.

The reason for the increased delay is that with larger packets, the ability for nodes to pass varied

data around the network diminishes, resulting in a lot of congestion causing data to be staggered at

certain points around the network for some time. This becomes increasingly devastating for larger

networks where there can be a lot of large data being sent at once with stacked queues and low

data sharing. From these results we can deduce that increasing the Packet Size is only advised with a

small network, otherwise sticking to a 512 Packet Size for larger networks will prove optimal.


Fig 5. End-to-End Delay Simulation Results.

16 Nodes 32 Nodes 64 Nodes0

50

100

150

200

250

300

350

End-to-End Delay (ms)

End-

to-E

nd D

elay

(ms)

10.1.4 Throughput

Although Throughput is not necessarily important for VOIP it is still valuable to review the effects

that increased Packet Size has on it in order to try maintain a well performing network. In this case,

maintaining a Throughput that is able to support the VOIP streams of Combat Net Radios is really

the only important factor, as dropping below that rate will result in staggered messages.

From these results we can see that the Throughput stays relatively stable over all network sizes with

a 512 Packet Size, see Fig 6. Increasing the Packet Size has the greatest effect on a small network,

increasing Throughput by x3 times exactly between 512 and 2048. For a 32 node network, the

Throughput increases only by around x2.4 times, and for a 64 node network the Throughput stays

relatively the same.

The reason this occurs is that from the previous results we have seen, the higher the Packet Size, the

higher the network congestion, especially for larger networks. This makes it hard for larger networks


to take advantage of a higher Packet Size, due to the bottlenecking that occurs and lack of varied

data being pushed through devices around the network.

From these results it is obvious that increasing the Packet Size is only advised for small networks

however it is not necessarily needed in order to provide optimal network performance.

Fig 6. Throughput Simulation Results.


10

20

30

40

50

60

Throughput (kbp/s)

Thro

ughp

ut (K

bp/s

)

10.2 Mobility

The following tests are aimed at observing the variation of performance between the various

popular movement models used in research today. The 3 movement models being tested are the

Random Waypoint model, Gauss-Markov model and the Reference Point Group Mobility model.


For a small network, the Packet Delivery Ratio is unaffected between all mobility models, as there is

minimal movement and complexity within the network, see Fig 7. For 32 nodes, the Reference Point

Group Mobility is far superior in that it clusters nodes together in order to maximize Packet Delivery


Ratio, route availability and connectivity. This is what we believe to be a more likely type of

movement you would encounter in the Military group or platoon, as opposed to random soldiers

spread over an area. This has resulted in Reference Point Group Mobility Model pushing ahead of

the standard Random Waypoint Model by 13.54%.

The Gauss Markov Mobility Model drops the Packet Delivery Ratio down 27-30% from the Random

Waypoint Model for 32 and 64 node networks. This is likely due to the realistic nature of the model,

as it attempts to add realistic pathing and velocity to nodes which puts more stress on the routing

protocol.

Overall the best performing mobility model is the Reference Point Group Mobility Model which

seems to mimic a real Military scenario the closest.

Fig 7. Packet Delivery Ratio Simulation Results.


20.00%

40.00%

60.00%

80.00%

100.00%

120.00%


Pack

et D

eliv

ery

Ratio

(%)



For a small network, Reference Point Group Mobility Model achieves the best results with a 12%

lower Routing Overhead than Random Waypoint, see Fig 8. The Gauss Markov Model had only

slightly more - 9%.

For larger networks, Random Waypoint and Gauss Markov increase at a steady rate increasing well

above 100% where as Reference Point Group Mobility Model is able to stay significantly lower, only

reaching 138% in a large network.

These results similarly reflect the last graph (Fig 7) in that the complexity and attempted realism of

Gauss Markov is putting heavy strain on the routing protocol compared to the standard model.

Reference Point Group Mobility Model is also showing significantly better results due to the

clustered nature of the model which has resulted in increasingly lower overheads the larger the

network scales, making this model excellent for scalability.

The Reference Point Group Mobility Model’s results show that clustered hierarchical topologies

provide an objectively better network performance for all network sizes.


Fig 8. Routing Overhead Simulation Results.


100%

200%

300%

400%

500%

600%


Routi

ng O

verh

ead

(%)



The End-to-End Delay does not differ heavily between movement models for all network sizes.

Fig 9. End-to-End Delay Simulation Results


2

4

6

8

10

12

14


End-

to-E

nd D

elay

(ms)


10.2.4 Throughput

Throughput also stays relatively the same, with the only dip in performance coming from Gauss

Markov which is once again affected by the complexity of the model.

Fig 10. Throughput Simulation Results


5

10

15

20

25

30

Throughput (kbp/s)

Thro

ughp

ut (K

bp/s

)

10.3 Beaconing

Beaconing is the interval in which a routing protocol sends a HELLO packet to its neighbors in order

to update its routing table with the most recent and correct topology information. The default value

for this parameter in NS-2 for OLSR is 2 seconds and the default neighbor holding timer is set to x3

that of the HELLO timer, which is 6. This holding timer is the time which upon completion will drop

any neighbors it does not receive a reply from.

The simulations were done to test a faster and slower beacon time of 1 second, 2 seconds (default)

and 5 seconds. Setting it any higher, e.g. to 10 seconds or more causes massive loss of packets.


The hypothesis behind this simulation is that a higher Beacon Time will reduce Routing Overhead

and overall network congestion, or on the other side of the spectrum, a lower Beacon Time may

lead to smarter route discovery at the cost of Routing Overhead.


Changing the hello timer has no effect on the Packet Delivery Ratio on a small network, as there is

hardly enough traffic to warrant faster route discovery. Both 1 and 2 second Beacon Timers show

almost exact figures across the board, where as a 5 second Beacon Timer shows a lower Packet

Delivery Ratio for larger networks of up to 14%.

This is likely the cause of poorer route discovery resulting in packets getting lost from incorrect

routing paths.

Fig 11. Packet Delivery Simulation Results


20.00%

40.00%

60.00%

80.00%

100.00%

120.00%


Pack

et D

eliv

ery

Ratio

(%)



Using a higher Beacon Time has shown to reduce Routing Overhead by 24% in small networks, 30%

in medium sized networks and 60% in large networks. However using a lower Beacon Time has

shown to increase Routing Overhead dramatically.

The results reveal that a higher Beacon Time has an extremely significant reduction in routing

overhead, making routing less costly and more efficient. This can be important in keeping CPU and

battery usage down. Using a lower Beacon Time seems too costly for any benefits and it does not

seem optimal no matter what benefits it may add to route discovery which is an obvious reason why

the default timer is set to 2.

The reason for the significant reduction in Routing Overhead from using a 5 second Beacon Timer is

clearly due to HELLO packets being sent out to neighbors less often. Finding an precious optimal

Beacon Time is reliant on the application of the network.

Fig 12. Routing Overhead Simulation Results


100%

200%

300%

400%

500%

600%

700%


Routi

ng O

verh

ead

(%)



For small and medium sized networks, Beacon Time has no effect on the End-to-End Delay, with

‘ms’ ranging within the same millisecond. For large networks there is no difference between a 1 and

2 second Beacon Time however a 5 second Beacon Timer results in a 19 millisecond delay increase.

This is not a large amount of delay, however it does reveal that a reduced Routing Overhead has no

positive effect on the End-to-End Delay despite a large drop in control packets taking up network

utilization.

Fig 12. End-to-End Delay Simulation Results


5

10

15

20

25

30

35

40

45

50


End-

to-E

nd D

elay

(ms)


10.3.4 Throughput

Changing the Beacon Time has almost no effect on Throughput and once again proves that a lower

Routing Overhead does not increase other performance metrics as a result.

Fig 13. Throughput Simulation Results


5

10

15

20

25

30

Throughput (kbp/s)

Thro

ughp

ut (K

bp/s

)


11 Simulation Analysis

From the simulations done, we can see various results on how changing a single parameter can have

significant performance differences over various sized networks. Most notable, routing overhead in

all 3 simulation sets was reduced significantly, however with some tradeoffs.

It is obvious that the default parameters set do not provide optimal performance for all applications

of the network. The difference in performance certain configurations can provide have proved that

MANETs need to be configured beyond out of the box settings.

These results alone should encourage new questions for network operators in the Military such as:

What are the optimal parameters for a MANET configuration?

Am I able to retroactively change them?

Does my proprietary hardware device support or override these changes?

If not, looking to a new vendor may be an option.

Obviously from these results, it is evident that none of the simulation configurations are optimal, but

with some minor tweaks and further testing, a sweet spot can be found in order to provide optimal

performance for the application at hand.

Additionally, optimal parameter values may differ between military scenarios and applications, for

instance using a Reference Point Group Mobility Model for simulation tests may be optimal if that

model mimics the scenario the best, or it could not.

Another example may rely on CPU and battery usage staying at an all time low due to the remote

nature of the excursion and so a higher packet size or even beacon time may be optimal for the

occasion.


Looking deeper into the results from the simulations, it is possible to extract information about how

a network protocol operates just by configuring the available parameters on the network.

Ultimately, this will give insight into a network protocol without knowing what it is which is useful

for evaluating proprietary devices with custom protocols.

From the results, we can clearly see that with an increased network size, the overhead increases

significantly. This is evidence that the routing protocol is proactive rather than reactive, meaning it

will continuously poll its neighbors in order to update its routing table between short intervals.

When testing on a fairly well performing network, it can be easy to deduce the cause of bad VOIP

quality if there is jitter. Jitter can arise from poor routing configuration so if it is prevalent then a

badly configured routing protocol may be the cause of it. The only other cause for it would be high

amounts of congestion on the network.

12 Implementation Summary

The results from our research and the research of other academic papers have revealed that an

optimal implementation plan for a Military MANET would be that of a hierarchical network. With

more than 1000 soldiers, it will be ultimately impossible and impractical to rely on a complete

MANET solution for the Military.

The first evidence to support this argument is the overall architecture of MANETs, or lack thereof.

MANETs have never been used in scenarios let alone a Military one with hundreds of nodes all being

active at the same time and providing clear VOIP quality at Military standards. The amount of

network congestion this would create would stagger the network to a halt.

The simulations done for the research of this paper reveal that even at 64 nodes there’s sub-optimal

performance for a Military VOIP network. Anything beyond 100 would most likely be broken.


However, MANETs can still fit into the equation and provide the same strengths under a hierarchical

topology. The idea is to have the network split into top and bottom layers with intermediate layers

in between providing convergence between the layering. The idea is not so far from a typical

Hierarchical Enterprise Network, however at the many bottom layers, separate MANETs consisting of

16 nodes maximum will be able to operate.

The intermediate layers would be responsible for converging the bottom layers together using more

powerful devices such as Man Packs with higher battery life, Vehicle Ad-hoc Networks or even tents

setup with routing equipment. This can extend to the top layer which would operate amongst

towers, and the highest performing hardware.

The bottom layer MANETs would have a single Border Router (BR) with possibly a backup BR for

redundancy consisting of soldiers with a Man Pack that would have their 16 node network be

redistributed into the intermediate, or distribution layer of the network. At this point, the choice of

networking protocol could even include legacy protocols such as RIP, as opposed to a MANET

protocol. With the small size of the MANET at that particular layer of the network, it wouldn’t make

a huge amount of difference.

Going back to the results of the simulation, a configuration of high level network parameters will be

advised in order to reach an optimal network performance. As we have seen, small networks

perform very well with higher packet sizes and beacon times. Having a packet size of 1024 should

not hinder performance of the network at all, and increase it across the board. Going further with a

2048 packet size may not be worth it, as the network may suffer from a spike in performance under

stress.

Additionally, increasing the beacon timer may be beneficial, but how much will most likely rely on

the scarcity and mobility of the group. With a highly dynamic and spread out topology, the network

may not be able to provide intelligent routes with a higher beacon time and so the default 2 second


timer may have to suffice. However in other scenarios, it may be beneficial to set it to around 3

seconds. This ensures a sharp increase in performance without risking a lack of intelligent routing.

13 Future Work

This paper outlines the considerations relating to the validity and overall method of evaluating and

configuring MANETs under a military scenario to some degree.

Further work needs to be done in the development of a proper framework which could act as a

guideline on how to thoroughly test and evaluate a MANET.

Currently there are no guidelines, or SLAs relating to MANETs or MANET VOIP for that fact. Combat

Net Radio vendors such as a Harris and Trellisware provide little information into the software and

hardware used on their devices. Further research can be done to advance the field of MANET testing

and evaluation to the point of enterprise LAN networks.

Forming guidelines, charts or even SLAs to govern a baseline standard for what performance metric

values are acceptable in a MANET environment. Not only for VOIP but for other military applications

as well.

This type of information is not prevalent in research as Military research is usually kept under the

rug and not released to the public. However, it is entirely possible to achieve better knowledge of

MANET metric standards without including a Military perspective.

Applying the knowledge we have today about MANETs to real test beds could help to alleviate a lot

of speculation about the validity of network simulations in research, as there are an overwhelming

number of papers that rely heavily on virtual simulators as opposed to papers utilizing real

hardware.


14 Conclusion

Mobile Ad-hoc Networks provide a very resourceful solution for tactical Military deployment on the

battlefield. However there are still many challenges to overcome in the field of Mobile Ad-hoc

Network research and development. It is only natural that governments look towards these types of

solutions for tactical operations, however with the limited amount of bandwidth and resources

owned by the ADF, achieving a network configuration similar to that of the USA is out of our reach

for now.

Many cases point to the fact that Mobile Ad-hoc Networks are ideal for a Military scenario; however

the issues involving scalability and most of all performance are still factors to consider. Research in

the field of MANETs has dictated that using MANET solutions over 100 nodes is not optimal and will

cause extremely poor performance.

Alternatively we must look to adopting different topologies such as a layered hierarchical model with

separate clusters representing higher and lower rankings within the Military.

The results from the simulations carried out in our research support this hypothesis and simply aim

at encouraging thought and planning when configuring and deploying a MANET under a Military

scenario.

The key metrics in evaluating the performance of Military Mobile Ad-hoc Networks relies heavily on

the intended application of the network, in our case VOIP. This has always been the case with

traditional networks so I see no reason why it isn’t the same for MANETs. However, without making

such a concluding argument, there are factors to consider when deploying a VOIP MANET in the

Military.


These factors include the obvious nature of the harsh terrain, the mission critical nature of the

information and the timely manner in which it needs to be sent and received across the network.

VOIP has similar time sensitive service level constrains, however additional factors such as Size,

Weight and Power also contribute to the equation.

Military Mobile Ad-hoc Networks have many constrains in that they require extremely efficient

routing with low power consumption, a high level of fidelity and an ability to propagate its radio

frequencies around the network with ease.

One of the many reasons this hasn’t been achieved without a hierarchical topology is due to the

contradictory architecture of MANETs. MANETs provide rapid deployment without servers or access

points, providing every device the ability to act as a router yet at the cost of eating up power, so

where’s the trade off.

This is the main reason a hierarchical implementation is optimal for a Military Mobile Ad-hoc

Network as it provides the best of both worlds.


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wiki.cis.unisa.edu.au web viewmobile ad-hoc networks or manets have been around for more than 30...

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