1. Warsaw University Of Technology
Faculty of Electronics and Information Technology
Institute of Radioelectronics
Delft University Of Technology
Faculty of Electrical Engineering, Mathematics
and Computer Science
Embedded Software Group
BANET: A Real-Time Telemetry System
For Mobile Applications
Master’s Thesis in Electrical and Computer Engineering
by
Adam Kozie´n
Warsaw, July 2010
Grade ...................................
...................................
Signature of the Graduation
Committee Chair
Supervisors:
dr. Stefan Dulman,
Delft University of Technology
mgr Tomasz Jamr´ogiewicz
Politechnika Warszawska
2.
3. Adam Kozie´n
Speciality Biomedical Engineering
Born May 30th, 1985
Studies started October 1st, 2004
Curriculum Vitae
I was born on May 30, 1985, in Gorlice, Poland. I graduated from Kromer High School, Gorlice,
in 2004. My studies started in 2004 at the Faculty of Electronics and Information Technology,
Warsaw University of Technology, Poland. From 2008 to 2009 I stayed at Delft University of
Technology, The Netherlands. I am interested in wireless networks, embedded systems and
electronic music.
...................................
Signature
GRADUATION EXAM
Graduation Exam Date .......................
Grade .......................
Overall Grade .......................
Comments and conclusions
from the Graduation Committee .....................
..........................................................................
i
5. Abstract
Scientists have long dreamed of the times when the technology is seamlessly incorporated into
everyday objects to make them intelligent and when people implicitly take advantage of the
globally networked world. This vision has already started to come true. Tiny, battery-powered
devices with sensing and computing capabilities are being deployed and networked to remotely
monitor the environment. The functionality of a mobile phone is growing rapidly. It is only
a matter of time when people are dynamically networked with simple devices and with each
other, forming temporary links. There is a need for intelligent, scalable, distributed networks
that can build and maintain themselves without any centralized coordinator. This is not an
easy task, especially in large, mobile, dynamic environments where density of the networked
devices can be high. This thesis focuses on the design of a telemetry system that composes
of a set of sensing devices, a group of monitored objects and a monitoring application. The
sensing devices are attached to the objects or placed close to them. The monitored objects
can be athletes, vehicles, animals and the monitored data - speed, heart rate, GPS position.
The key design factors are: the fact that the objects tend to form groups, mobility of the
monitored objects and real-time performance of the system. The system is innovative in that it
supports group behaviour. Instead of reporting the telemetry data individually, neighbouring
objects form clusters and elect clusterheads that send the data on behalf of the whole cluster.
This can improve the performance of a telemetry system by lowering the number of long-range
communication links. To be able to make optimal design choices, the literature was studied and
the telemetry market investigated. Clustering algorithms found in the literature and state of the
art wireless technologies are presented. The chosen clustering algorithm (GBL) was evaluated in
MiXiM simulator, using detailed wireless channel model. The main performance metric was the
number of objects forced to use long-range communication links. Under ideal radio conditions for
inter-object communication, clustering gave promising results. After introducing physical radio
characteristics (interferences, delays, attenuation) and implementing a real-world MAC protocol
(CSMA/CA), the network performance dropped significantly, especially in dense, highly mobile
scenarios. To prove that a real-world deployment of such system is viable, a prototype telemetry
system (BANET) was implemented: a gateway device (aKnode), a telemetry server (BANET
Server) and a monitoring application (BANET Monitor). Real-world experiments showed that
implementing GBL in Java was not a good choice. The demand on computational power and
application responsiveness is high and the cost of the ease of implementation is too much. On
the other hand, the designed long-range communication link fulfilled its task. The average
round trip time of 832ms for a telemetry message enables real-time tracking with the update
rate of the order of seconds. The overhead of sending small data packets over GPRS can be
significantly reduced through clustering. To give an example, with periodic transmissions of
20B of telemetry data and the group size of 100 objects, the overhead was reduced from 70%
to 4.6%. In conclusion, when using high performance gateway devices it is feasible to use the
designed telemetry system in monitoring groups of mobile objects in real-time.
iii
7. BANET: System telemetryczny czasu rzeczywistego do
zastosowa´n mobilnych
Streszczenie
Celem pracy bylo opracowanie systemu telemetrycznego, kt´ory sklada sie z zestawu czu-
jnik´ow, grupy monitorowanych obiekt´ow oraz aplikacji monitorujacej. Czujniki sa umieszczone
na obiektach lub w bliskim ich sasiedztwie. Monitorowanymi obiektami moga by´c sportowcy,
pojazdy, zwierzeta, z kolei danymi telemetrycznymi mo˙ze by´c predko´s´c, tetno, pozycja GPS.
Gl´owne zalo˙zenia projektowe to: tendencja to tworzenia grup przez obiekty, mobilno´s´c moni-
torowanych obiekt´ow oraz wydajno´s´c czasu rzeczywistego systemu telemetrycznego. System jest
nowatorski, gdy˙z wykorzystuje zachowania grupowe. Zamiast raportowa´c dane telemetryczne
indywidualnie, sasiadujace obiekty tworza klastry oraz wybieraja zarzadc´ow klastra, kt´orzy
wysylaja dane w imieniu calego klastra. Rozwiazanie takie mo˙ze poprawi´c wydajno´s´c systemu
telemetrycznego poprzez zmniejszenie liczby polacze´n dalekiego zasiegu. Aby m´oc wybra´c op-
tymalne rozwiazanie przestudiowana zostala literatura oraz zbadany zostal rynek telemetrii.
W pracy przedstawione zostaly znalezione algorytmy klastrujace oraz najnowsze rozwiazania
komunikacji bezprzewodowej. Wybrany algorytm (GBL) zostal zbadany przy u˙zyciu symu-
latora MiXiM, wykorzystujac szczeg´olowy model kanalu komunikacyjnego. Gl´ownym wyz-
nacznikiem wydajno´sci byla liczba obiekt´ow u˙zywajacych laczno´sci dalekiego zasiegu. W ideal-
nych warunkach radiowych dla komunikacji kr´otkiego zasiegu pomiedzy obiektami, klastrowanie
dalo obiecujace rezultaty. Po wprowadzeniu cech fizycznych towarzyszacych przesylaniu infor-
macji bezprzewodowo (interferencje, op´o´znienia, tlumienie) oraz po zaimplementowaniu pro-
tokolu MAC (CSMA/CA), wydajno´s´c sieci znaczaco spadla, szczeg´olnie w sytuacjach du˙zego
zageszczenia obiekt´ow oraz wysokiej ich mobilno´sci. Aby udowodni´c, i˙z zbudowanie i u˙zycie
takiego systemu telemetrycznego jest wykonalne, stworzony zostal prototyp systemu (BANET)
skladajacy sie z urzadzenia-bramy (aKnode), serwera (BANET Server) oraz aplikacji moni-
torujacej (BANET Monitor). Fizyczne eksperymenty przeprowadzone z u˙zyciem prototypu
pokazaly, i˙z implementacja GBL przy u˙zyciu jezyka Java nie byla dobrym wyborem. Wyma-
gania na wydajno´s´c oraz szybki czas reakcji okazaly sie zbyt wysokie. Z drugiej strony, zapro-
jektowane lacze dalekiego zasiegu spelnilo swoje zadanie. ´Sredni czas RTT (Round Trip Time)
dla wiadomo´sci telemetrycznej na poziomie 832ms pozwala na ´sledzenie w czasie rzeczywistym
z czestotliwo´scia od´swie˙zania rzedu sekund. Koszt (wielko´s´c tej cze´sci pakietu danych nie be-
dacej danymi wla´sciwymi) wysylania malych ilo´sci danych w pakietach poprzez GPRS mo˙ze by´c
znaczaco pomniejszony poprzez u˙zycie klastrowania. Dla przykladu, przy okresowym wysylaniu
20B danych telemetrycznych i wielko´sci grupy r´ownej 100 obiekt´ow, koszt ten zostal zmniejs-
zony z 70% do 4.6%. Podsumowujac, przy u˙zyciu wydajnych urzadze´n-bram, zaprojektowany
system telemetryczny mo˙ze zosta´c wykorzystany do monitorowania grupy mobilnych obiekt´ow
w czasie rzeczywistym.
v
10. Bibliography 63
A Specification of GGTP and GSTP protocols 69
A.1 Gateway-Gateway Transport Protocol (GGTP) . . . . . . . . . . . . . . . . . . . 69
A.2 Gateway-Server Transport Protocol (GSTP) . . . . . . . . . . . . . . . . . . . . . 70
B aKnode device 74
C BANET Server operation 78
D CD Contents 79
viii
11. Acknowledgements
The major part of this thesis was carried out at Delft University of Technology. The time spent
with Embedded Software Group was much valuable for me. In the first place I would like to
thank Koen Langendoen for inviting me to his group and giving me the opportunity to do this
research. I owe the same gratitude to Stefan Dulman for his excellent supervision. He guided
me throughout this thesis and was always eager to help. I would also like to thank all the other
members of the group. Our weekly seminars gave me a lot of knowledge on Wireless Sensor
Networks and Model-Based Fault Diagnosis. In the end, I would like to thank my other friends,
from the lab room, thanks to whom the work was always fun.
The prototype implementation part (BANET) was done at Warsaw University of Technol-
ogy. I am grateful to my Polish supervisor Tomasz Jamr´ogiewicz for his support. He could
always find time to discuss implementation issues. I am also grateful to Marcin Ziembicki and
Michal Dziewiecki who helped me with PCB design and aKnodes assembly. Special thanks goes
to WRRiTM Foundation who awarded me a grant for doing this thesis project and to MobileIS
company who offered XT65 modules together with development accessories for free.
ix
13. Chapter 1
Introduction
This chapter gives a background for the thesis and clarifies its context. Firstly, the concepts
of a real-time telemetry system and a mobile sensor network are explained. Secondly, the idea
and purpose of clustering is described. Finally, possible applications are presented.
1.1 Goal of the work
The goal of this work was to develop a complete networking solution for a telemetry system
intended for monitoring a group of remote mobile objects in real-time. Mobility of the observed
objects and the fact that the objects tend to form groups were the key design factors. The
monitored objects can be athletes, vehicles, animals and the monitored data - speed, heart rate,
GPS position etc. The main assumptions are:
• The number of objects is usually greater than one
• The objects form more or less spatially stable groups - the reason for forming a group
could be a sport competition or a common task performed by the objects on the same
area
• The objects are usually mobile and independent from each other - deployment of the
telemetry system should not break this independence, e.g. movements of the cyclists
monitored during the race should not be constrained by the system - a cyclist should be
able to break away from the peloton to win the competition and the user of such telemetry
system should still be able to keep the trace of him
• The user accesses the system via a monitoring application running on an Internet-enabled
device (PC, smartphone)
In addition to these assumptions, the following requirements were formed:
• The telemetry data should be reported periodically in real-time with the period of the
order of seconds; the time delay between local data acquisition and data presentation to
the system user should also be seconds
• The fact that the objects form groups should be utilized - in general, it is expensive in
terms of energy and transmission overhead to periodically send small amounts of data
1
14. (tens of bytes) individually by every object; in some scenarios it would be fully sufficient
to report the status of the whole group collectively (e.g. location of the group as a mean of
group members’ locations); in other scenarios, one group member could collect individual
data from the rest of the group and send them in one message, possibly using some data
aggregation/compression techniques
• The monitoring area should be as large as possible - it is desirable to be able to monitor
the objects that stay at any place in the world
• The reporting devices should be able to operate continuously for several hours on single
batteries
• The system should self-configure on start, self-reconfigure when needed and run au-
tonomously with no configuration input from the user
1.2 Background
Current technology gives us the possibility of using small, battery-powered devices equipped
with sensors to remotely monitor the environment, machines, objects or personal activity. Some
of the solutions are designed to monitor only one object or a restricted area of the environment,
while the others provide the user with a network of such devices to cover larger sensing area
or to monitor a greater number of objects. Wireless technology facilitates deployment of such
sensor networks and increases the comfort in end-user applications.
1.2.1 Telemetry and wireless sensor networks
The terms ”telemetry” and ”wireless sensor network” are tightly coupled. ”Telemetry” is
more abstract and refers to a technology that allows remote measurement and reporting of
information [88]. A simple telemetry system is depicted in Figure 1.1. On the other hand, a
wireless sensor network is a subtype of a self-organizing, self-healing wireless ad-hoc network,
where energy-constrained nodes form ad-hoc connections and create network infrastructure [1].
Real-time telemetry system A real-time telemetry system in comparison to a regu-
lar telemetry system differs in that in the first one, the delivery of telemetry data is time-
constrained. Usually the user of a real-time telemetry system expects the data to be delivered
periodically within a specified time interval. We can further differentiate timing requirements
to soft and hard real-time requirements. In a soft real-time system, exceeding the given time
limit is tolerable - the system will respond with decreased service quality. On the other hand,
violation of hard real-time constraints can result in a catastrophic failure or costs of such viola-
tion can be exceptionally high. In this work, the designed telemetry system has soft real-time
requirements.
In a telemetry system, the collected data may be transported over wireless media such as
radio, infrared link or using wired technologies like fiber optic link, PSTN line or Ethernet
network. After the data is collected and transported to the monitoring application, it is further
processed by the application and presented to the user. Thus, a wireless sensor network may
be considered part of a telemetry system’s infrastructure (See Figure 1.2).
In general, first-generation sensor networks are application-specific networks with static
sensor nodes deployed to accurately monitor the remote environment in real-time. As shown in
Figure 1.2, there usually exist one or more centralized data-collection points (sinks, gateways)
2
15. Figure 1.1. A simple teleme-
try system for real-time measure-
ment of a rocket launch progress,
taken from [77]. Data from rocket
sensors is wirelessly transmitted
to the user’s laptop.
Figure 1.2. A typical wireless sensor network architecture,
taken from [1]. Such architecture can be also considered
a telemetry system.
able to transmit the data back to the observer. Although this wireless sensor network model
can accommodate many application scenarios, it introduces a lot of restrictions on the use of
such network.
1.2.2 New challenges for telemetry and sensor networks
With mobile devices becoming ubiquitous, there is an increasing tendency to incorporate
wireless sensor networks into people’s daily life. Projects carried out at companies like Microsoft
[16], Google [32], at big institutions like the Massachusetts Institute of Technology [80, 21],
Dartmouth University [5], University of California, Los Angeles [4] and military projects [22]
prove that the vision of pervasive computing [38] and ambient intelligence [9] is becoming reality.
In such vision, everyday objects are networked and the physical environment is enriched by
computational power. People interact with the objects and with each other forming a world-
wide network and enabling novel real-world services (Figure 1.8). Under these conditions,
where the communicating devices are mobile and often do not form stable groups, networking
can be a tough task. The reasonable way for the devices to communicate with each other is
communication in an ad-hoc manner, forming a mobile ad-hoc network.
1.2.3 Mobile ad-hoc (sensor) networks
A mobile ad-hoc network (MANET) is a collection of wireless devices that can dynamically
form a network to exchange information without using any pre-existing fixed infrastructure.
This is an important part of communication technology that supports pervasive computing.
MANETs do not complement infrastructure networks like WiFi, WiMax or GSM/UMTS. They
rather constitute a separate, equally important concept [40].
MANET’s operation is distributed, as there is no background network for central control
and management. Mobile terminals are usually autonomous nodes able to operate both as a
3
16. Figure 1.3. Example of a pervasive computing environment, taken from [35].
host and a router. They collaborate amongst themselves to provide basic networking services
such as routing. Routing can be single-hop (a packet can only be sent to devices which are in
transmission range of the sender) or multi-hop (packets are relayed by intermediate nodes until
they reach the destination). Single-hop routing is simple in terms of architecture and imple-
mentation, whereas multi-hop routing provides more functionality and applicability with the
cost of implementation complexity and maintenance overhead. The highly dynamic nature of
mobile ad-hoc networks results in frequent and unpredictable changes of the network topology.
In fact, it has been proven [8, 20, 17] that flat routing techniques (either proactive or reactive) in
large-scale, dynamic MANETs do not perform well and are inefficient in maintaining or discov-
ering routes. Furthermore, implementation of a multi-hop routing protocol in wireless ad-hoc
networks, especially under energy constraints is a significant design challenge [14]. Introducing
hierarchy to mobile ad-hoc network topology is a way of increasing network performance [8, 20].
1.2.4 Clustering
The basic idea behind clustering is to form groups (clusters) of neighboring nodes from
the flat structure of the network. Within each cluster, a clusterhead (leader) is elected to
communicate with other clusters or with external networks on behalf of the cluster members.
In addition to packet routing and forwarding, clusterheads can control channel scheduling or
maintain a TDMA (Time Division Multiple Access) schedule within the cluster. There may be
situations when no suitable cluster exists for a node to join. In this case, the node becomes a
leader of its own and is referred to as a singleton.
There are two different contexts of clustering in MANETs. In the first one, (Figure 1.4)
the nodes already form a network and clustering is a means of improving performance of this
network, so that the traffic could be managed in a better way. In the second context (Figure
1.5), clustering is applied to network a group of individuals who initially do not form any sort
of network.
Clustering in pre-existing networks In this case (Figure 1.4), clustering usually ad-
dresses large-scale networks, where the number of nodes and/or their density is high. The
mobility also plays important role here. A moving node breaks the communication links with
4
17. Figure 1.4. Clustering in routing.
Figure 1.5. Clustering in location updating,
taken from [25].
its current neighbors and creates new ones by entering a new neighborhood. This generates
many events in the network and increases the cost of network maintenance. Under such condi-
tions, flat routing techniques face the scalability problem. With flat proactive routing schemes
every node tries to maintain an up-to-date routing information about every other node in the
network. This is also known as table-driven routing. Topology changes, so natural in MANETs,
require a proactive routing protocol to regularly broadcast updates in the network to enable
other nodes to maintain correct routing information. High degree of mobility would generate
an avalanche of control messages to update the routing tables of every node. When using re-
active routing schemes in large-scale ad-hoc networks, the mobility of the nodes would cause
considerable delays and overall decrease in performance.
Clustering can provide a decent level of performance under these conditions by creating
a backbone for the routing protocol and by limiting the amount of routing information that
propagates inside the network. The nodes communicate only with the clusterheads who perform
routing and the network appears smaller. In very large-scale networks one can add another level
of hierarchy, creating superclusters, which consist of clusterheads of the ”regular” clusters.
Clustering as a means of networking the individuals Sometimes mobile hosts do
not form an explicit network - they are just a group of individuals, where the common thing is
the same ”playground” and the need for reporting the sensed data (e.g. location) to another,
remote network (Figure 1.5). Conventional approach to acquire monitoring data from the hosts
is where the hosts report individually to the base station, e.g. by transmitting GPRS packets to
the GSM/GPRS Base Transceiver Station and then over the Internet to the user. The individual
approach has several drawbacks:
• Uplink traffic When dealing with a large, dense group of mobile hosts, the demand on
the uplink channel is very high and it easily becomes overwhelmed (Like trying to make
a call during large-scale event, where lots of people use their mobile phones).
• Cost If the uplink channel is paid, it is expensive to maintain the information about all the
monitored hosts. Furthermore, when sending small amounts of data (bytes, tens of bytes)
periodically, the transmission overhead becomes very high and eventually constitutes a
substantial part of the bill.
• Energy Long-range communication is expensive in terms of energy. According to Friis
Transmission Equation, the relation between transmission power and transmission range
is quadratic - to get twice as big transmission range, we have to transmit with four times
greater power. Furthermore, mobile hosts within a cluster can share hardware resources,
5
18. e.g. a GPS receiver. Consequently, some hosts can turn off the excess hardware or put it
into sleep mode, thus prolonging the lifetime of the network.
Instead of reporting individually, we can equip the nodes with short-range radios and apply
a clustering algorithm. Neighboring nodes will collaborate to form clusters, elect one node
from the cluster to be the clusterhead and ask the clusterhead to send their data along with
its own data to the user, possibly using some data aggregation/compression techniques. This
can significantly reduce the amount of reported data (number of GPRS packets), the number
of long-range communication links, save energy on long-range, energy expensive transmissions
and money if the uplink channel is paid (e.g. GPRS). In this scenario, superclusters can also
be formed to reduce the uplink traffic even more. Naturally, clustering can also play here an
auxiliary function of just creating a foundation for the other techniques, like routing, when the
clusters wish to communicate with each other.
1.3 Clustering - the core idea of the system
Taking all the above into consideration, the use of clustering techniques is proposed to realize
data collection from the group of mobile objects. This thesis focuses on the second context of
using clustering in mobile ad-hoc sensor networks (presented in the previous section), although
similar techniques can be used in pre-existing networks to support routing and enhance its
performance.
1.4 Applications
Such telemetry system can be the answer to problems in conventional applications like
object tracking or data gathering as well as in more futuristic scenarios, where the networking
conditions are tricky. Below, possible applications are presented. Some of them are already
being researched and are described in more detail under the Related work section.
Sport There are many sport disciplines where we can observe group behavior, more or less
organized. In road cycling, riding alone is inefficient, so the riders form organized groups to
save their energy. They ride along the streets, changing their position inside the group or
joining other groups. During marathon and similar running competitions we have a group of
individuals running in the same direction, inevitably forming temporal groups. Apart from
competitions or games, we can also observe group behavior during training. Here, monitoring
physical performance is even more important.
In case of a game or a competition, having access to real-time physiological data of the
competitors opens new directions in presenting the game and increases its attractiveness to the
public. When training is concerned in sports like cycling, monitoring athletes by their coach
in real-time creates new possibilities to improve the effectiveness of the training by giving the
athletes real-time feedback from the coach.
Emergency services and health care From time to time we can hear in the news that
people who went climbing or hiking are missing. The rescue services try hard to find them,
often using sophisticated equipment and helicopters. Such rescue missions are very expensive
and not always end with success. In professionally organized expeditions [53] there is usually
satellite communication available, combined with VHF radios, however, taking part in such
6
19. expedition costs lots of money. On the other hand, regular people who go hiking without any
communication devices can fall into serious trouble. Here, rescue services could equip them
with low-cost data transceivers coupled with some sensors (heart rate monitor, GPS position)
adding the hikers to their telemetry system. On popular, busy routes (Figure 1.7) people could
be clustered to report their ”alive” status collectively. Being independent, they could leave
the group and still be under surveillance. The data transport could be realized by satellite
transceivers and short-range radios for in-group communication. In the long term, this could
save the rescue services money on expensive satellite communication and could provide new
safety services to the ramblers.
To prove the interest in such applications, it is worth noting that a commercial solution
for monitoring tourists emerged while developing this master thesis project. However, the
monitoring devices do not collaborate in any form to improve data transport - each of them
reports individually. It is further presented in Section 2.2.
Another use case of such mobile telemetry system is a crisis situation. For catastrophic
events such as chemical or nuclear accidents/attacks, methods to rapidly deploy chemical and
radiation sensor networks are needed [22]. Mobile ad-hoc sensor networks designed for these
events could provide real-time monitoring information for response and rescue missions.
Figure 1.6. Recreational cycling as a potential
use case.
Figure 1.7. Clustering for emergency services,
people report their ”alive” status collectively.
VANETs Vehicular Ad-Hoc Networks (VANETs) present a very active field of research, de-
velopment and real-world trials. It is envisioned, that vehicular inter-networking together with
advanced traffic information systems will enable new applications (Figure 1.8), including colli-
sion and other safety warnings, as well as non-safety applications - real-time traffic congestion
and routing information, high-speed tolling, mobile infotainment and many others [87]. Cre-
ating such a reliable and scalable system is a challenge for wireless research community. In
Section 2.2, a clustering-based solution for VANETs found in the literature is presented.
Pervasive computing, people-centric sensing, social networking The concept of
Smart Mobs goes even further. Howard Rheingold in his book [34] predicts that in the future
people will be connected directly to each other, forming an intelligent, self-organizing network.
In addition, the paradigm of people-centric sensing [5] states, that the new technology will be
centered on the user, improving the quality of life and adapting to the individual, without the
need of being aware of the technical details. Current and future technological advances will turn
the widespread mobile devices into a global sensor network, allowing sensing, visualizing and
sharing information about ourselves, friends and the world we live in. To give an example, in the
era of social networking, recreational runners or cyclists could share their location and/or heart
7
20. Figure 1.8. Car-to-car communication: traffic jam signalling. Source: Car2Car Communication
Consortium.
rate information on social networking sites in real-time, opening new possibilities to current
solutions, described in Section 2.2.
Military Apart from this, there is a considerable interest in mobile sensor networks and
telemetry systems in the military [13, 22]. Micro electro mechanical sensors (MEMS) can be
deployed in the battlefield for damage assessment, surveillance, target tracking, and character-
istics measurement of incoming targets (See Figure 1.9, 1.10).
Figure 1.9. Military application domains,
taken from [22].
Figure 1.10. A soldier uses a telemetry system
to collect the data from robots deployed on the
battlefield, taken from [22].
1.5 Outline
The remainder of this thesis is structured as follows. Chapter 2 first describes the two
application scenarios based on which the system has been designed. Next, the chapter presents
related work covering academic work and commercial solutions. The first part of Chapter 3
presents clustering algorithms found in the literature and state of the art wireless technologies
for short-range and long-range communication. The second part details the proposed network
architecture and justifies the design choices. In the end, consideration is given to the stack of
protocols used in that particular architecture and to transmission overhead costs. Chapter 4
8
21. presents the clustering algorithm chosen for the telemetry system and the simulation results
for the two scenarios. Chapter 5 lays out implementation details. Firstly, aKnode - the gate-
way device is demonstrated. Secondly, BANET Server and BANET Monitor applications are
described. The chapter ends with a presentation of the results from real-world experiments.
Finally, Chapter 6 concludes the thesis and points out the issues for future work.
9
22. Chapter 2
The scenarios
Application context inevitably drives architecture design choices and definition of the ser-
vices needed in the network. Thus, the boundaries of telemetry and mobile ad-hoc sensor
networks fields were narrowed. Two scenarios were developed, on which further work is based.
This chapter first describes the two scenarios in detail. The chapter ends with presentation of
the related work covering academic work and commercial solutions.
2.1 Example scenarios
This section presents the two example scenarios, where a multi-node mobile telemetry system
can be applied. The first one catches the most attention in this thesis and describes a situation,
where the movement of mobile hosts is organized. The second scenario involves unorganized
movement, which could be considered as the worst case scenario for maintaining such telemetry
system. Both of them utilizes clustering as a networking technique.
2.1.1 Example scenario 1 - Cyclists
In road cycling heart rate, output power, cadence and velocity are considered crucial indica-
tors of performance [15] and monitoring them helps in the assessment of personal capabilities.
Current solutions [56] allow the coach to analyze the data gathered during training (afterwards)
and optimize individual training plan of the cyclist.
As stated in the previous chapter, road cycling is a group sport. Not only during the race
interaction between cyclists is important, training in groups is very common and is more effective
than individual trainings [15]. Under ideal conditions, every cyclist would follow his individual
training plan during group training. What can help in achieving best training experience is
real-time feedback from the coach. There has been research on this (see 2.2 Related work),
however, the focus was on developing a model predictive controller for optimizing the training
itself. This thesis focuses on the problem of gathering the data from the network of cyclists.
During the race, having real-time information from the cyclists can be also very useful. On
one hand, the directeur sportif can make better decisions and adjust the tactics according to
individual performance of the team members, on the other hand, the race is more interesting
to the public when live data is available.
10
23. In figures 2.1 and 2.2, the proposed scenario for real-time monitoring of cyclist’s performance
is depicted. Each cyclist has a number of tiny wireless sensors attached either to him or to his
bicycle, forming a Body Area Network (BAN) - when only the cyclist is concerned or Personal
Area Network (PAN) - when the bicycle is included. Such sensor can be a pulsoximeter, heart
rate monitor, blood pressure meter, thermometer, depending on what is required. Preferably,
bike computer would be coupled with this network to acquire data like speed, location, cadence
or output power. This personal area network has a ”star” topology where the central node
is a gateway node that relays information from the sensors to the external network where the
observer (cyclist’s coach or friends) can track him.
If we want to monitor more than one cyclist we need to equip each of them with such PAN.
Having gateway nodes by every cyclist can be justified if they are completely independent from
each other (they can leave the group and yet still be monitored). For the reasons explained
in the previous chapter we can apply networking techniques that comes from mobile ad-hoc
networks (clustering) to enhance the telemetry system’s performance.
Figure 2.1. Proposed Body/Personal Area Network for monitoring cyclist’s physical perfor-
mance.
Figure 2.2. Data path from sensors to the end-user.
11
24. Figure 2.3. Clustering in monitoring a group of cyclists.
2.1.2 Example scenario 2 - Smart Mob
This scenario reflects the situation where we have an unorganized group of monitored indi-
viduals moving toward randomly chosen directions. The real-life example could be a large-scale
event (concert, celebration of a big event) where all the participating people form a network
to share information with each other or with the organizers who can broadcast the latest news
or event details. Here, a telemetry system would only be a part of a bigger, interactive com-
munication system. People could form a multi-hop, mobile mesh network with clustering as
a backbone for maintaining the topology. For now, this is a futuristic situation, but having
in mind the concepts of pervasive computing and people-centric sensing it is worth evaluating.
The scope of the work on this scenario is limited to evaluating the clustering technique as a
means of collecting data from a group of randomly moving objects.
2.2 Related work
A thorough literature study has been performed to figure out the state of the art in the
researched area. In this section, some related projects from the academia are presented first
and then the latest commercial solutions for physical performance monitoring are highlighted.
2.2.1 Academic research
Assisted Bicycle Trainer [26] This project’s goal is to develop a Model Predictive Con-
troller for the optimization of group training in cycling. The controller predicts the heart rate
of the cyclists based on individualized heart rate models and regulates the group training by
advising cyclists to change their position in the group to adjust the group speed or to split the
group in such a way that each cyclist can meet his training plan as exactly as possible. For
indoor training, infrastructure network is used (MicaZ nodes with own communication stack).
12
25. For outdoor training, WiFi (PDA) is used for inter-bicycle communication and MicaZ nodes
with a dedicated protocol for intra-bike sensors networking.
MarathonNet The MarathonNet project [33] is inspired by the observation, that the impor-
tance of fitness and sports in people’s leisure priorities increases continuously. The project aims
at developing an application to record biometric data while running a marathon. The runners
wear small devices for sensing and communicating with the base stations which are deployed
along the marathon’s route. This is an example of opportunistic networking, where the data is
shared only when there is a base station nearby and stored locally during disconnection times.
In [33], the phenomenon of runners tending to form clusters is studied.
Advanced Traffic Information Systems In [43], a cluster-based multi-channel commu-
nication protocols are proposed for vehicular ad-hoc networks. In the proposed scheme, the clus-
terhead (CH) vehicle functions as a coordinator to collect/deliver real-time safety messages to
the neighboring clusterheads. In addition, the clusterhead vehicle controls channel-assignment
for cluster-member vehicles and non-real-time traffic. The scheme uses contention-free TDMA-
based MAC within cluster and the IEEE 802.11 MAC among clusterhead vehicles.
SENSEable City [80], CenceMe [31] SENSEable City is a research initiative at the Mas-
sachusetts Institute of Technology. The vision is a city functioning as a large-scale, real-time
control system where people share information with each other and local urban communities
are tied together with social networking. In one of the latest subprojects [29], urban mobility
patterns are studied by monitoring taxi drivers and public transport users in real-time. Re-
ports are sent using GPRS infrastructure. CenceMe - a prototype system allows the user to
automatically export his current activity information (walking, sitting) to members of his social
network, e.g. by publishing status messages on Facebook. Activity type is recognized by a
Symbian-based sensing daemon running on a cell phone. The daemon utilizes a set of sensors
built in a cell phone or connected to it via Bluetooth. It processes the sensors data and sends
the status over WiFi or GPRS to the social network’s server.
BikeNet [11], CarTel [21] BikeNet and CarTel are other examples of opportunistic net-
working solutions. In the first project, Moteiv Tmote Invent motes and Nokia N80 mobile
phones form a ”bike area” network with the capability of inter-bike and bike-to-environment
communication. In this project, not only a cycling-specific information is important. Bicycles
act as data mules for gathering environmental data (e.g. pollution). The data is then sent
to the server and stored in a database. GPRS communication is also considered for real-time
sensing. CarTel is another project with very similar purpose. However, none of them considers
clustering.
Animal tracking projects ”Electronic Shepherd” [41] has originally been made to address
special needs for sheep and reindeer farmers who were seeking a system to keep track of their
animals during the grazing season. The system includes GPS receivers, UHF radios for short-
range communication and GPRS modems for reporting to the farmers. It is based on the
concept of flock behavior - the flock leader can monitor the state of the other flock members
and act as a gateway for reporting the location. In a similar project, ZebraNet [23], simple
flooding protocol has been used for the communication between animals. FalconTrak [55] is
an ongoing project with individual-based, satellite communication for tracking young falcons
throughout their annual cycle.
13
26. Healthcare monitoring systems NASA project [27, 39], originally developed for space-
flight applications, is an example of a real-time electrocardiogram (ECG) monitoring system.
The ECG holder communicates via Bluetooth with a PDA which sends the data in real-time
via GPRS to the remote server. The software has also been developed to visualize the electro-
cardiogram on the PC. This system has been designed for a single person usage.
2.2.2 Commercial solutions
Tour de France live data Already from 2005 we have been given the chance to follow real-
time cyclists’ performance and positioning data during the most prestigious cycling competition
in the world. In 2005 [54], German group from SRM equipped some of the riders with GPRS
modems which transmitted the data in real-time to the public. Over the last few years, Polar
has offered similar functionality [76]. However, in both cases, only selected, top-riders are
monitored and group monitoring has not yet been applied to the race.
MapMyFitness [69], TrainingPeaks [84], My Tracks [73] These two web portals are
excellent examples of the evolving social networking. On the first site, regular people can upload
their favorite running, cycling or hiking routes to share with the others, search for new popular
routes and even calculate the calories burnt. There is an iPhone application called iMapMy [67],
created especially for the users of this portal. The application records training information and
uploads the data to the portal afterwards. The second portal focuses on athletes, offering tools
like Virtual Coach to build individual training plans. My Tracks is an Android phone application
similar in functionality to iMapMy. The training logs can be uploaded and visualized in Google
My Maps or presented in Google Spreadsheets.
Locatelo [68] A commercial solution for monitoring people for emergency situations emerged
while developing this master thesis project. Locatelo consists of a monitoring centre and a set
of devices given to the monitored people. A device (Figure 2.4) uses GPS for positioning and
GPRS for reporting to the monitoring centre. However, the devices do not collaborate in any
form to improve data transport - each of them reports individually. Very recently, Locatelo has
been launched and is now being tested by a number of Polish emergency services [68, 59].
Figure 2.4. Different versions of devices used in Locatelo system to monitor people for emergency
situations, taken from [68].
14
27. Chapter 3
Network design
During system design, a thorough literature study and industry investigation was performed
to find the optimal networking solution that meets the requirements presented in Section 1.1.
The first part of the chapter presents clustering algorithms found in the literature and state
of the art wireless technologies for short-range and long-range communication. The second
part details the proposed network architecture and justifies the design choices. In the end,
consideration is given to the stack of protocols used in that particular architecture and to
transmission overhead costs.
3.1 Clustering schemes
Table 3.1 shows a comparison between various clustering algorithms found in the literature.
The only restriction in selecting these algorithms from the others was mobility support (ex-
cepting LEACH). All of them consist of two basic phases: cluster formation phase and cluster
maintenance phase. The first phase lasts much shorter than the second one and after the clus-
ters are formed, the communication inside the cluster or between clusters is possible. The table
compares the following characteristics:
• Purpose - The motivation for developing the algorithm.
• Network type - An algorithm may be created for static networks, without mobility
support, or it may be invented especially for mobile networks, where mobility is one of
the factors in algorithm decisions.
• Mobility assessment - To assess the relative mobility between nodes, one can use Re-
ceived Signal Strength Intensity parameter (RSSI) by measuring the difference in RSSI of
the coming messages from the other node. The second option is to use GPS receiver to get
the absolute position and send the message with the location and speed to the requesting
host.
• Cluster formation phase - This phase can be implemented as a recursive process, where
the number of rounds depends on the size of the network. In another case this phase is
not recursive, the number of rounds is fixed and the whole cluster formation process takes
a constant time interval.
15
28. Algorithm LEACH MOBIC WCA CM, CM-
IR
GBL DDVC/
DDLC
Publication date 2000 2001 2002 2004 2004 2008
Purpose Data col-
lection
Routing MAC Data col-
lection
Location
updating
Routing
Network type static mobile mobile mobile mobile mobile
Mobility assess-
ment
- RSSI RSSI/
GPS
GPS GPS Doppler
shift/GPS
Cluster formation
phase
not recur-
sive
not recur-
sive
recursive same as
LEACH
3 rounds recursive
Decision metric Probabi-
listic
Weight Weight Probabi-
listic
Weight Weight
Cluster range 1-hop 1-hop 1-hop 1-hop 1-hop 1-hop
Table 3.1. Clustering schemes
• Decision metric - A metric, based on which the decision of becoming a cluster-head
or joining an existing cluster is made. The selected algorithms use two different decision
metrics: probabilistic decision and decision based on weights, where multiple weighted
factors are considered.
• Cluster range - A cluster can be a 1-hop cluster (2 hops in diameter) or bigger.
LEACH This probabilistic clustering algorithm [19] was a source of inspiration for many
researchers over the years. In LEACH, the probability for a node to become a cluster-head is a
function of the desired number of cluster-heads in the network and the number of times a node
has already been a clusterhead. In joining the clusters, nodes choose the clusterhead which is
physically closer, based on RSSI. LEACH was designed primarily for static networks for data
collection proposes and is not a right choice in mobile scenarios.
MOBIC MOBIC [2] is a mobility-aware algorithm which takes the ratio of power levels due to
successive message receptions as a mobility metric. Nodes with the smallest variance in relative
mobility to their neighbors are elected as cluster-heads. It was designed to improve throughput
and message delays in the network. However, there are three shortcomings of MOBIC. In large-
scale, dense networks, an RSSI measurement can be very inaccurate due to interferences , what
can lead to not optimal decisions in electing cluster-heads. Furthermore, MOBIC does not check
for the suitability of the node when joining the clusters. A node can join the cluster despite its
instability and unsuitability. Additionally, MOBIC suffers from the ripple effect of re-clustering
[42].
WCA The WCA algorithm was proposed by Chatterjee et al. [6] to achieve the optimal
operation of the MAC protocol. Each node calculates a combined weight from the node speed,
battery power, transmission range and node degree, which is then disseminated through the
whole network. The node with the global minimum weight is chosen as cluster-head. Then
the process is repeated until all the nodes are clustered, excluding already chosen cluster-heads
from the consecutive rounds. It is a recursive process and the execution time depends on the
network diameter.
CM, CM-IR These two algorithms [28] were inspired by LEACH. They take mobility into
account when joining the clusters, however the leader election process is identical to LEACH, so
16
29. Figure 3.1. Result of using pure probabilistic metrics in mobile networks. Over time, the spatial
distribution of cluster-heads within the network becomes uneven.
the leaders are elected using purely a probabilistic metric, without any mobility considerations.
This could lead to a situation where over time, the spatial distribution of cluster-heads within
the network becomes uneven - there are regions with the nodes that are very probable to become
leaders in the next round and regions where this probability is very low (Figure 3.1).
GBL GBL [25] is the algorithm chosen for evaluation in this work. It has been proposed to
alleviate the problem of location updating in mobile environment. Mobile hosts form clusters
and location update messages are sent only by the cluster leader on behalf of all cluster members,
decreasing the overall number of location messages sent via expensive uplink channel. The
algorithm is described in detail in the next section.
DDVC and DDLC DDVC and DDLC [37] are the recently proposed algorithms which aim
at establishing stable clusters in pseudolinear highly mobile ad-hoc networks (cars on highway,
ships, trains, airplanes). The algorithms involve dynamic clusterhead election and the scheme
for cluster maintenance. One algorithm uses Doppler shift to measure the relative mobility, the
other needs a GPS receiver. In both of them, the clusterhead election process is recursive. The
notion of using Doppler shift of communication signals for the purpose of routing is a fairly new
idea [36], but for the nodes with low velocities, it is not an accurate measure.
3.2 Short-range wireless technologies
This section presents short-range wireless technologies that are available in the industry and
can potentially be used for short-range communication in the designed telemetry system. As
discussed in previous chapters, short-range communication serves two purposes in the system:
• Data transportation from sensors to the gateway node (Figure 2.1); for that purpose, an
energy-efficient networking technology supporting simple network topology (star) needs
to be found
• Communication between gateway nodes to form clusters and to periodically transfer sen-
sors data from cluster member nodes to the elected clusterheads afterwards (Figure 2.3);
here, a technology is needed that allows implementing a mobile ad-hoc network; energy
efficiency and simplicity are also important but less critical as gateway nodes have more
resources available (memory, computational power, energy)
17
30. Required data rate The required data rate for short-range radios depends on the amount of
sensors data transmitted through that link. Assuming ten sensors for each object and periodic
transmissions of 10B every 5 seconds from each sensor, the required data rate to transmit all
the sensors data to another object within 1 second is 100 B/s (800 bps). If faster transfers are
needed, say 10 ms transmission time, the required data rate would be 10 kB/s (80 kbps).
IEEE 802.11 - WiFi IEEE 802.11 [65] commonly known as WiFi is one of the most rec-
ognizable wireless technology. It is a set of communication standards for Wireless Local Area
Networks (WLAN). It has been developed to substitute and support wired Local Area Networks
(LAN). The standard defines two operational modes: infrastructure-based and ad-hoc. The first
mode is commonly used to provide wireless access to the Internet. At least one device has to
be configured as access-point. The others connect to the Internet via that access-point. In the
second mode no access point is required and any device can communicate directly with any
other device that is within transmission range. The standard uses contention-based medium
access scheme - Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in com-
bination with various modulation techniques, depending on IEEE 802.11 version. Although
it is possible to create a MANET from WiFi-enabled devices operating in ad-hoc mode, the
main issue is power consumption which is very high compared to other short-range wireless
technologies. The reason for high power consumption is that IEEE 802.11 is meant for high
performance applications giving high bandwith for WLAN traffic.
Bluetooth Former IEEE 802.15.1 standard, Bluetooth [50] is another widespread wireless
technology. In contrast to IEEE 802.11 which typically offers 100m range, Bluetooth is intended
for Wireless Personal Area Networks (WPAN) and has a range of 10m. Designed primarly for
hand-held, battery powered devices it is typically used as a cable-replacement technology. The
only operating mode is a master-slave mode in a star topology (piconet), where the master
node can interconnect with up to seven active slave devices. Within a piconet, a Time Division
Multiple Access (TDMA) schedule with frequency-hopping spread spectrum (FHSS) technology
is applied. When energy consumption is concerned, Bluetooth takes in idle state about ten
times less energy than WiFi (25 mW compared to 256 mW - measured values). Nevertheless,
Bluetooth is not an energy-efficient protocol compared to other WPAN technologies.
Although very popular and applicable to simple data-transfer scenarios, Bluetooth is useless
when MANET creation is concerned. Piconets can be extended to more complex networks called
scatternets, but there still exists a master-slave relationship there. Furthermore, before sending
any data a communication channel between a master and a slave has to be established. These
factors practically eliminate Bluetooth as a choice for MANETs.
IEEE 802.15.4 The standard [66] specifies a link for low-power, low-cost and low-rate wireless
personal area networks. IEEE 802.15.4 is widely used in embedded applications such as home
automation, industrial control or wireless sensor networks where the data rate is not high and
where energy consumption is an important factor. Like the whole family of IEEE 802 standards,
it defines services and protocols for the lower two layers of the seven-layer OSI networking
reference model (See Figure 3.2). The standard uses CSMA/CA medium access mechanism
and supports star as well as peer-to-peer topologies. Apart from contention-based medium
access, there is a possibility to use the optional superframe structure with guaranteed time slots
for time-critical data.
Since the MAC layer supports peer-to-peer connections, IEEE 802.15.4 can serve as a ba-
sis for ad-hoc networks. Furthermore, there are several higher-layers protocols that are built
on top of IEEE 802.15.4 (See Zigbee [91], MiWi [70], SimpliciTI [78], Wireless HART [89],
6LoWPAN [44], DigiMesh [52]) or even embedded operating systems and software development
18
31. environments that support that standard (See TinyOS [83], Moteworks [72], SOS [7], Contiki
[10], Nano-RK RTOS [12], MANTIS OS [3]).
Zigbee Zigbee [91] is built upon the foundations provided by the IEEE 802.15.4 standard.
It specifies full protocol suite for high-level communication between Zigbee devices and unifies
networking interface by supplying the end-user with application profiles. The main extensions
compared to IEEE 802.15.4 are support for complex network topologies and reliable multi-hop
communication. To perform data routing and forwarding process to any node in the network, an
AODV reactive ad hoc protocol has been implemented. Like IEEE 802.15.4, Zigbee is targeted
at long battery life, low data rate embedded applications where security support is relevant.
Figure 3.2. IEEE 802.15.4 and Zigbee stack.
ANT ANT [48] is a proprietary protocol and a silicon solution for wireless sensor networks.
It is marketed as an ultra low-power technology with optimized network efficiency that enables
ANT-powered devices to operate for years on a coin cell battery compared to months for other
technologies. It uses a TDMA scheme with fixed packet size (17B) which suits repetitive trans-
missions where low latency is required. Like in Bluetooth, ANT communication is channel-based
and there always exists a master-slave relationship. Before ANT nodes can communicate, the
channel must be configured and slaves must synchronize to master’s timing.
ANT supports various network topologies, however all of them are constrained with a
master-slave relationship, thus disabling ANT from creating a flexible mobile ad-hoc network.
On the other hand, ANT seems a reasonable choice for sensor networks in a simple star topology.
ANT is becoming widespread in health and fitness tracking. Lots of leading brands have
adopted ANT in their products [48]. Some of them are Garmin, Trek, Adidas, Nautilus, SRM.
Bluetooth Low Energy Bluetooth Low Energy [49] is an emerging open standard targeted
at similar market as ANT - sports and fitness, medical, home and office. It is said that its power
consumption is only a fraction of what classic Bluetooth consumes. The technology has been
optimized for applications with low data throughput and low battery capacity. This includes
small devices like sensors, watches, remote controls or mobile phones. What differs Bluetooth
Low Energy from ANT is two implementation options:
• Single mode implementation - This is a pure Bluetooth Low Energy implementation meant
for energy-constrained devices like sensors or watches. It provides months to years of
lifetime on a standard coin cell battery.
19
32. Technology IEEE
802.11
Bluetooth IEEE
802.15.4
Zigbee ANT Bluetooth
Low Energy
Application focus WLAN,
high data
rate
WPAN,
high data
rate, cable
replace-
ment
WLAN,
WPAN,
WSN, low
power,
industrial
control
WLAN,
WPAN,
WSN, low
power,
industrial
control
WPAN,
WBAN,
ultra low
power
WPAN,
WBAN,
ultra low
power
Bandwith 54, 600
Mbps
1, 3, 24
Mbps
250 kbps 250 kbps 1 Mbps 1 Mbps
Transmission
range [m]
125, 230 1, 10, 100 100+ 100+ 30 10
Battery life1
hours2
hours-
days2
months2
months2
years years
Protocol stack
size
100-250 kB 100-250 kB 4-32 kB 4-100 kB 2 kB -
Network size 248
8 264
216
232
248
Topologies sup-
ported
peer-
to-peer,
star
star, scat-
ternet
peer-
to-peer,
star
peer-to-
peer, star,
tree, mesh
star,
scatternet-
like
star
1
Coin-cell battery, 8 Byte data message, 2 second message interval, 24 hours per day, 7 days
per week. Estimation based on [47]
2
Theoretical only. Peak current requirement for 802.11, Bluetooth and 802.15.4-based standards
exceeds coin cell battery capability so coin cell operation is impractical.
Table 3.2. Short-range wireless technologies comparison. Given values are based on protocols
specification, if not specified otherwise. If a protocol has multiple versions, the values in a table
entry are separated by the comma.
• Dual mode implementation - Meant to combine classic and Low Energy versions of Blue-
tooth on a single chip. The radio circuitry is shared between these two standards lowering
the costs of Low Energy adoption in classic Bluetooth applications. Both standards have
their own MAC addresses.
Bluetooth Low Energy employs Frequency Division Multiple Access (FDMA) scheme in
combination with TDMA using a frequency hopping transceiver. Supported network topologies
depend on the presence of dual mode devices in the network. If the network consists of single
mode devices, the only topology available is star. One device acts as a master and the others
as slaves. When dual mode devices are present, it is possible to create a star-bus network with
classic Bluetooth connections between each star’s dual mode masters.
3.3 Long-range wireless technologies
This section presents the available long-range wireless technologies that were initially se-
lected for consideration. As discussed in previous chapters, a technology is needed that has
global or near-global range. The requirements for data rate are not high - a rough estimation
is presented below. Standards like Evolved EDGE, LTE, EV-DO or WiMAX are not covered
in this work as they either do not provide enough coverage and/or are still in their early stages
of deployment.
20
33. Required data rate It can be estimated, that with one clusterhead having to forward
messages from a hundred of cluster members within one second, with message size of 100B and
without any compression, the required data rate is only 10 kB/s (80 kbps).
3.3.1 Cellular-based Networks
GSM/GPRS/EDGE The digital standard known as Global System for Mobile Communi-
cations (GSM) [61] is the most widely deployed mobile telephony system in the world. According
to [88], 80 percent of the global market - 3 billion people across more than 210 countries uses it
for voice and data communication. Both signalling and speech channels are digital, thus GSM
is considered a second generation (2G) mobile phone system. It is a cellular system, which
means the area where the system is deployed is split into cells which are physically represented
by Base Transceiver Stations (BTS). A Mobile Station (MS) continuously monitors its vicinity
searching for available cells and is always registered to only one, most suitable cell at a time.
The communication between Mobile Stations and Base Transceiver Stations is accomplished
using both FDMA and TDMA channel access methods - each frequency channel is divided into
time slots.
Apart from its core technology - circuit switching, used for voice and data connections,
packet switching technology - General Packet Radio Service (GPRS) has been introduced to
address the needs for data transmission. The main issue with circuit-switched data transmissions
is that the TMDA time slot used by that circuit is allocated for the time of the connection, even
though no data is transferred in the meantime. It is costly and not optimal from the point of
view of resources allocation. When using GPRS, the user pays only for the data transferred, not
for the GPRS connection time. During data transmission from MS to the external server, data
packets travel through BSC (Base Station Controller), SGSN (Serving GPRS Support Node),
GGSN (Gateway GPRS Support Node) and reach the external server (Figure 3.3). For more
information, see [57]. The maximum downlink and uplink data rate for GPRS is 80 kbps.
To improve data transmission rates in GSM networks, Enhanced Data rates for GSM Evolu-
tion (EDGE), also known as Enhanced GPRS (EGPRS) has been developed and included into
the GSM family of standards. By introducing sophisticated coding and transmitting methods
it can provide downlink and uplink data rates of 236.8 kbps.
UMTS/HSPA Universal Mobile Telecommunications System (UMTS) [86] is a third-
generation (3G) mobile telecommunications system also known outside Europe as FOMA or
W-CDMA. The standard is chosen by most network operators wanting to upgrade their sys-
tems to 3G. Unlike GPRS or EDGE which reuse the GSM Base Station System (BSS), UMTS
requires new base stations (Node B), their controllers (RNC) and new frequency allocations
forming a new radio access network - UTRAN (UMTS Terrestrial Radio Access Network). How-
ever, the rest of existing GSM/GPRS infrastructure is shared between these systems (Figure
3.3). UMTS offers enhanced voice and data capacity with better noise immunity in comparison
to GSM. Most commonly, it uses Wideband Code Division Multiple Access (WCDMA) as its
air interface enabling 384 kbps data transfer speeds for downlink and uplink channels.
To extend and improve the performance of existing UMTS protocols, High Speed Packet
Access (HSPA) has been proposed. HSPA is a collection of two protocols: High Speed Downlink
Packet Access (HSDPA) and High Speed Uplink Packet Access (HSUPA) which offer data rates
of 14.4 Mbps and 5.76 Mbps respectively.
What is worth mentioning, UMTS aims at allowing smooth convergence between terrestrial
mobile systems and Mobile Satellite Systems (MSS) by adopting the same radio interface for
21
34. both satellite and cellular systems and using a single mobile terminal [30]. Very recently, the
first dual-mode satellite/terrestrial smartphone has been released [60].
Figure 3.3. An architecture of GSM and UMTS networks. Both systems use the same Core
Network.
3.3.2 Satellite-based Newtorks
Satellite communication is possible thanks to a set of artificial satellites launched and main-
tained by various telecommunications companies. The data from and to the satellite is trans-
ported by radio waves. There exist various satellite constellations using different Earth orbits
(Figure 3.4).
Figure 3.4. The three types of satellite orbits: LEO, MEO and GEO. Based on Wikipedia,
modified.
GEO satellites A satellite on a Geostationary Earth Orbit (GEO) appears motionless to the
earth-based observer due to the fact that its rotational period is equal to the Earth’s period.
On one hand, this simplifies the communication as ground terminals can direct their antennas
toward the satellite only once, without the need for any satellite’s motion tracking equipment.
On the other hand, due to the high altitude of a geostationary orbit (35 786 km above the
ground), powerful transmitters and sophisticated receivers are required to achieve satisfactory
communication parameters. Furthermore, the big distance to the GEO satellite implies high
signal latency (250 ms up to 900 ms one way) making these systems unusable in some real-time
applications.
22
35. Technology GPRS EDGE WCDMA HSPA IsatM2M GmPRS
Generation 2.5G 3G 3G 3.5G - -
Bandwith
(DL/UL)
80/80 kbps 236.8/236.8
kbps
384/384
kbps
14.4/5.76
Mbps
100/25 B
(burst)
60/15 kbps
Coverage large large medium medium near-
global
near-global
Transmission
Cost1
$0.15/MB $0.15/MB $0.15/MB $0.15/MB - $5.00/MB
1
Based on present operators’ offer.
Table 3.3. Long-range wireless technologies comparison.
The main satellite networks that provide global or near-global data communication using
geostationary satellites are Inmarsat [62] and Thuraya [82]. For mobile telemetry, Inmarsat
offers IsatM2M - a burst messaging service with message sizes up to 25 bytes from the terminal
and 100 bytes to the terminal. Thuraya offers a different type of service for mobile telemetry -
Geo Mobile Packet Radio Service (GmPRS) similar in functionality to GPRS, with data rates
up to 60/15 kbps (download/upload) and a dedicated Thuraya Module [81].
There exist other satellite systems that use GEO satellites and provide similar services, but
their coverage is regional [79, 45, 46].
MEO satellites Medium Earth orbit (MEO) satellites orbit the Earth at an altitude between
35 786 km and 2 000 km. Currently, this type of orbit is used mainly by satellite navigation
systems like GPS [88], Glonass [88] or Galileo [88]. There is only one-way communication
in such systems - the GPS receiver continuously receives signals broadcasted from the visible
satellites. The signals contain their positions. Next, it compares travel times of the signals from
each satellite and determines the distance to each of them. Finally, the receiver computes its
position using trilateration method, based on satellites positions and the distance to them.
LEO satellites A Low Earth Orbit (LEO) satellites spin above the ground at an altitude
between 2 000 km and 160 km. In contrast to geostationary orbit satellites, their position above
the ground is not fixed. In addition, the visibility of the satellites from the ground is limited
because of their low altitude. Because of these two facts, a large number of LEO satellites is
required to provide uninterrupted connectivity. LEO satellites are less expensive to launch into
orbit than geostationary satellites and require less powerful transmitters as the orbit radius
is far smaller. Current LEO systems have signal latencies as little as 40 ms or less but their
throughput is typically lower than in GEO-based networks.
The three major satellite networks that operate at Low Earth Orbit are Iridium [64], Glob-
alstar [58] and ORBCOMM [75]. Iridium is the only truly global solution - it covers the whole
Earth including poles, oceans and airways. Globalstar does not cover polar areas while OR-
BCOMM uses only 29 satellites being a low-cost solution that realizes the concept of a delay-
tolerant network - the data to be send or received is transferred only when a ORBCOMM
satellite is visible by both the mobile transceiver and a ground infrastructure of the ORB-
COMM system. Otherwise, the data is stored in the satellite which continues its orbit until it
encounters the destination. In worst case scenario, such delay can be up to 100 minutes of a
full orbit.
23
36. 3.4 Proposed network architecture
In Figure 3.5, the proposed network architecture is presented. Telemetry data is not sent
directly to the system user. Gateway devices report to the telemetry server and the users acquire
the data through that server. Such approach offers several advantages:
• There is only one centralized connection point for the gateway devices that want to send
their telemetry data and the parameters of this connection (IP address, port number) can
be static
• The user may connect to the server occasionally, while the telemetry data collection being
uninterrupted (the server can run continuously)
• Multiple users can connect to the telemetry server and use the system
Figure 3.5. Proposed network architecture of the telemetry system.
3.4.1 Clustering algorithm
GBL was selected as the clustering algorithm for the mobile telemetry system at hand
because it best fits system requirements. Most importantly, it supports mobile networks and its
cluster formation phase is not recursive, which is good in highly dynamic networks where the
topology changes very quickly and where the algorithm should work fast. However, the original
GBL presented in [25] is not free from drawbacks and had to be modified it in order to meet the
requirements of the system. A detailed description of the modified GBL along with the results
of performed algorithm simulations are presented in Chapter 4.
24
37. 3.4.2 Sensors-gateway communication
To create a star network from a set of sensors and a gateway node one should choose from
IEEE 802.15.4, ANT and Bluetooth Low Energy. These technologies were developed especially
for such applications. They offer low data rates with long battery life.
If energy efficiency is critical, ANT or Bluetooth Low Energy are good choices. Otherwise,
IEEE 802.15.4 can be used as it offers peer-to-peer connectivity and can be applied to both
sensors-gateway and inter-gateway communication, simplifying network architecture. In this
thesis, IEEE 802.15.4 was chosen.
3.4.3 Inter-gateway communication
For communication between mobile objects (effectively between gateway nodes), the IEEE
802.15.4 standard was chosen. It best suits the needs as it provides peer-to-peer connectivity
and was designed for battery-powered devices and low data rate applications.
WiFi’s energy consumption would be the main issue when chosen, while with Bluetooth or
ANT creating a MANET would be complicated. Zigbee or other IEEE 802.15.4-based standards
mentioned in Section 3.2 would be a good solution if the network involved multi-hop commu-
nication and complex network architecture (tree, cluster tree, mesh). However, in single-hop
networks IEEE 802.15.4 is fully sufficient and using e.g. Zigbee would defeat the purpose the
standard was created for.
3.4.4 Long-range communication
For long-range communication, the GPRS technology was chosen. The coverage of GPRS
is currently very high even in rural areas, so taking the cyclists scenario there should be no
problem in passing the telemetry data to external networks in real-time. The throughput of
GPRS should also be enough considering the expected telemetry message sizes and message
rates.
HSPA technology has been created to address the problem of the ever increasing throughput
demands in multimedia applications and to provide Internet connectivity in mobile broadband
modems. If GPRS was not enough for the telemetry system, one could switch to EDGE.
Furthermore, current UMTS/HSPA world coverage is little in comparison to GPRS or EDGE
[88].
Satellite services for telemetry market offer much lower data rates with higher hardware
and maintenance costs in comparison to GPRS/EDGE, however they can be considered as an
alternative solution for the telemetry system noticing their global range.
3.4.5 Protocol Stack
The telemetry system integrates four types of networks:
• Internal sensor network at every monitored object (IEEE 802.15.4)
• Mobile ad-hoc network of the monitored objects (IEEE 802.15.4)
• Long-range communication beween a gateway node and the telemetry server (GPRS)
• Access to telemetry data by the system user (user’s Internet connection)
25
38. For that reason, data from the sensors must travel a long way before they reach the end-user,
being encapsulated and decapsulated many times by various transport protocols. A full set of
protocols the telemetry data encounters on its way is shown in Figure 3.5.
When a sensor device wants to send its sensor’s data (or is requested to do so by the gateway
device), it queries the sensor for the data (e.g. heart rate value, temperature, position) and
asks WSNTP to deliver it to the gateway device. WSNTP defines message format and uses
IEEE 802.15.4 for transportation. When the gateway device receives the message, it checks
whether it is a cluster member, a clusterhead or a singleton. If the gateway node is a singleton
or a clusterhead, it sends the sensor data directly to the telemetry server using GSTP. GSTP
defines messaging format and uses TCP to transport the messages to the server. In case the
gateway device is a cluster member, it sends the sensor data to the clusterhead using GGTP.
IEEE 802.15.4 is used here as a short-range radio link. When the clusterhead receives the
GGTP message (and possibly GGTP messages from other cluster members), it puts together
all the telemetry data encapsulated in them, adds its own telemetry data and assembles a
single message according to the format specified by GSTP. Before a GSTP message reaches the
telemetry server, it has to face the following protocol path: GSTP - TCP - IP - SNDCP - LLC
- RLC - MAC - GSM RF - MAC - RLC - LLC - BSS GP - FR - L1 - FR - BSS GP - LLC -
SNDCP - GTP - TCP - IP - L2 - L1 - L2 - IP - TCP - GTP - IP - L2 - L1 - L2 - IP - TCP
- GSTP - TCP - IP - L2 - L1 - L2 - IP - TCP - GSTP. For more information about GPRS
transmission plane, see [57].
The green coloured protocols are specific to the GSM/GPRS network. Using TCP/IP
protocols there may be confusing, but GPRS core network utilizes them to interconnect SGSNs
and GGSNs (See Figure 3.5). From the point of view of the gateway device’s application, there
exist only two connection points when sending GSTP messages: the gateway device itself and
the telemetry server. To send a GSTP message, the gateway specifies the IP address and TCP
socket number of the server and asks TCP to deliver it. All the intermediate protocols are
transparent to the device.
WSNTP, GGTP and GSTP These are the three protocols introduced by the telemetry
system. Sensor network part of system implementation was out of scope of this thesis. Thus,
the WSNTP is only declared as present in the system. Similarly, GGTP defines the message
format only for control messages exchanged during GBL cluster formation phase (See Section
4.1.1). GGTP and GSTP specification is laid out in Appendix A.
• Wireless Sensor Network Transport Protocol (WSNTP) is used in sensor data transporta-
tion from the sensor device to its gateway
• Gateway-Gateway Transport Protocol (GGTP) defines the format of messages inter-
changed by the gateway devices - participants of a mobile ad-hoc network; it also controls
the transport using ACK mechanism
• Gateway-Server Transport Protocol (GSTP) defines the format of messages interchanged
by a gateway device and the telemetry server
3.4.6 Transmission Overhead
As mentioned in Section 1.1, sending small amounts of data in a data packet involves
high overhead costs. When data is transmitted to the telemetry server, various protocols add
overhead to the data (payload) in the form of headers. Individual headers are detailed in Figure
3.7. Header sizes were taken from [18]. According to Orange GSM/GPRS operator (e-mail
26
39. correspondence with the operator), the billable part of a data packet includes the IP header
and its payload (in this case: TCP header, GSTP header and Data). The overhead is the
percentage of the entire packet that is not GSTP payload.
Assuming the monitored object produces telemetry data that constitutes 20B GSTP payload
and a GSTP message sent to the telemetry server does not contain Timestamp and GPS position
fields (See Appendix A), the transmission overhead in case of individual reporting is as follows:
• IP packet size:
20BIP header + 20BTCP header + 7BGSTP header + 20Bpayload = 67BIP packet
• Transmission overhead:
20BIP header + 20BTCP header + 7BGSTP header
20BIP header + 20BTCP header + 7BGSTP header + 20Bpayload
=
47B
67B
= 70%
The GSTP header is 7B, as only one entry is present. We can see that the overhead
constitutes a substantial part of the bill. Now assume there is a group of 100 objects. The
overall overhead in case of individual reporting remains the same. In contrast, with group
reporting based on the clustering algorithm (assumed to be able to produce one big cluster
from all the objects in this case), the overhead decreases. However, it can decrease only to a
certain level. The overhead cannot be reduced infinitely due to the fact, that the size of an IP
packet transmitted over GPRS is constrained by an attribute called Maximum Transfer Unit
(MTU), which for GPRS is less or equal to 1500B [63]. Thus, telemetry data from 100 objects
needs to be split into two IP packets:
• First IP packet having data from 72 objects:
20BIP header + 20BTCP header + 8BGSTP header + 72 × 20Bpayload
89B
= 1488B
• Second IP packet having data from the rest 28 objects:
20BIP header + 20BTCP header + 8BGSTP header + 28 × 20Bpayload
89B
= 608B
The transmission overhead using group-based reporting:
2 × (20BIP header + 20BTCP header + 8BGSTP header)
1488Bfirst IP packet + 608Bsecond IP packet
=
96
2096
= 4.6%
Such overhead can be obtained when the cluster covers all the 100 objects and the clusterhead
sends 2 IP packets or when two clusters are formed and each clusterhead sends one IP packet.
The minimum overhead is achievable when all the transmitted IP packets are of MTU size or
as close to MTU in size as possible. With 20B data payload, the minimum achievable overhead
equals:
48Bp1 headers + 48Bp2 headers + 48Bp3 headers + ...
1488Bp1 size + 1488Bp2 size + 1488Bp3 size + ...
=
48
1488
= 3.2%,
and corresponds to the cluster size of 72, 144, 216... objects.
27
40. WSNTP - Wireless Sensor Network Transport Protocol
GSTP - Gateway-Server Transport Protocol
GGTP - Gateway-Gateway Transport Protocol
TCP - Transmission Control Protocol
IP - Internet Protocol
SNDCP - SubNetwork Dependent Convergence Protocol
LLC - Logical Link Control
RLC - Radio Link Control
MAC - Medium Acces Control
BSS GP - BSS GPRS Protocol
FR - Frame Relay
L1 - Layer 1 protocol
L2 - Layer 2 protocol
GTP - GPRS Tunneling Protocol
Figure 3.6. Telemetry system’s transmission plane. The upper part of the figure relates to a
situation when a singleton or a clusterhead sends its sensor data. Below, a situation is presented
when a cluster member tries to send its sensor data. In this case, the clusterhead acts as a relay
between the cluster member and the telemetry system’s server. The colours distinguish different
types of networks the protocols are used in.
28
41. Figure 3.7. Data Encapsulation during transmission from the gateway device to the user PC,
based on [85]. The horizontal arrows describe communication between corresponding layers,
the vertical arrows indicate encapsulation/decapsulation.
29
42. Chapter 4
Clustering algorithm evaluation
This chapter is dedicated to the clustering algorithm chosen for the telemetry system at
hand. The original GBL is presented and the modifications introduced to it. The performance
of modified GBL was verified by carrying out a sequence of experiments. The main perfor-
mance metric was the number of network nodes forced to transmit GPRS messages. During
the experiments, the following parameters were varied: the number of nodes, node speed, stan-
dard deviation of speed distribution, transmitting power and nodes density. CSMA/CA MAC
protocol was used and its parameters were constant. The simulation results are presented for
the two scenarios: a group of cyclists moving along the road (the first scenario) and a group
of objects moving according to the Random Waypoint Movement model (the second scenario).
All the experimental results along with the full list of simulation parameters can also be found
in [24].
4.1 GBL
As mentioned in Chapter 3, this algorithm was proposed to alleviate the demand on the
uplink channel in location services by grouping nearby hosts and reporting their locations col-
lectively to the server. GBL describes group formation scheme, the strategies to report host
locations within a cluster and to report a cluster location to the server. For this thesis, only
the group formation scheme is relevant.
4.1.1 Original GBL
In GBL, every host has a unique ID and a GPS receiver for determining its location and
speed. In addition to long-range wireless communication, a mobile ad-hoc network is assumed
for collaboration between the hosts. To form clusters, the following clustering algorithm is
applied:
1. Each mobile host m broadcasts a ”Hello” message appended with its own location, velocity
and transmission range to its neighbors
2. When m receives the ”Hello” message from a neighbor n, it will check the the distance
between both hosts according to the predicted location after time parameter τ. If the
hosts are within range, n will be added to m’s neighbor list and sm,n is calculated.
30
43. 3. Host m records the connectivity degree when ”Hello” messages are received. It sums
up the degree of affinity (sm = j sm,j) with respect to its neighbors and calculate the
leadership score, sL.
4. If m does not possess long-range communication capability to the location server, sL = 0.
5. After sL of each host m is calculated, m broadcasts the score to its neighbors in the
neighbor list.
6. Host m compares all obtained leadership scores and joins to the host l with the highest
score by sending l a ”join” message. Host ID is used to break tie in leadership score
comparison, in case it is necessary.
7. If any host m receives a ”join” message, it will become the leader and add the neighbor
n to the member list. Upon elected, m will not send any ”join” message to other host k
although k may have a larger leadership score than m.
8. It is possible that a host m sends a ”join” message to another host k before receiving the
”join” message from another host. If that is the case, m will send a ”leave” message to k.
Host k then removes m from its member list.
where:
• τ - time parameter which determines a time period, after which the hosts’ neighborhood
is estimated
• Degree of affinity - metric used in cluster leadership/membership decisions:
sj,k = α(1 −
future dist(j, k)
r
) + β(1 −
(vxj − vxk)2 + (vyj − vyk)2
v2
xj + v2
yj + v2
xk + v2
yk
)
• Leadership score:
sL = w1
j∈N
sm,j + w2|N|
• α, β, w1, w2 - weights, v = {vx, vy} - velocity vector, N - set of Neighbors
There can be a situation, when there is no suitable group to join. In this case, the host
becomes a ”singleton” and periodically checks for a group to join.
For the full GBL description, see [25].
4.1.2 Modifications to original GBL
The original version of GBL is unsuitable for the proposed applications. The main reason is
that clusterhead’s movements are constrained - it always checks the cluster’s speed and adjusts
its own speed to it. Such behavior is dictated by the fact, that cluster formation phase in
original GBL is executed only once - during the system initialization.
• This constraint was relaxed by periodically repeating cluster formation phase. The leader
is completely independent from it’s cluster members and after a specified period of time,
new most suitable clusterheads are elected. Periodic reclustering also favors even cluster-
heads distribution around the system (See Figure 3.1).
31
44. • Furthermore, the host can join the clusters only during cluster formation phase, which
eliminates the potential cascading leaving effect - a situation, when joining or leaving the
cluster changes the properties of the cluster, forcing other cluster members to leave.
• Taking into account possible transmission problems like interferences and packet dropping,
ACK messages were added for unicast communication (Loin and Leave messages) and a
condition, that when the host has communication problems (e.g. no ACK for a Join
message sent), it automatically turns into a singleton.
• The algorithm was evaluated using two different mobility models: Circular Mobility and
Random Waypoint Movement Model, which are described in more detail in Section 4.2.2.
4.2 Simulation setup
To evaluate the algorithm performance, MiXiM [71] - a component-based, modular and
open-architecture discrete-event network simulator was used. It is based on OMNeT++ [74]
simulation environment which uses special NED language for describing network architecture
and C++ programming language for describing dynamic behavior of the network. The most
important feature of MiXiM is the support for mobile and wireless networks. It provides detailed
models of wireless channel, wireless connectivity, basic ready-to-use mobility models and some
communication protocols, especially at the Medium Access Control (MAC) level. Such low level
of abstraction allows reflecting real-life situations and running precise simulations. In addition,
debugging is facilitated by a user-friendly graphical representation of an animated wireless
mobile network. Figure 4.1 shows a screenshot of the tool installed on one of the computers at
TU Delft Software Technology Department’s Lab.
4.2.1 Architecture
The network was implemented in a modular way which is the most obvious choice when
using OMNeT++ framework. The whole network is a module consisting of several submodules.
The mobile nodes themselves consist of submodules. (Figure 4.2)
Network
• World - Contains the environmental model which is mainly used to collect global param-
eters like the dimensions of the network
• Connection Manager - this module is responsible for dynamically managing the con-
nections between the nodes. It calculates the distances and creates the connections if the
nodes are within interference range.
• Stats - A module created exclusively for collecting statistical information, like the number
of leaders per round, number of messages sent or message delivery times. After the
simulation it is possible to analyze this data using OMNeT++ Analysis Tool or export
the data to programs like gnuplot or Matlab.
• Mobile Hosts - Mobile hosts form a table of modules which size is determined by the
number of hosts. The hosts themselves consist of submodules which are described below.
32
45. Figure 4.1. MiXiM simulator. The network simulated here was a group of hosts moving ac-
cording to the Random Waypoint Movement model, under ideal wireless channel conditions (no
interferences, 100% message delivery ratio). The yellow nodes are cluster leaders.
Figure 4.2. Modular design of a mobile host. Every rectangle is a submodule.
33
46. Mobile Host Each mobile host is an independent module with its own parameters and sub-
modules definitions. The submodules can communicate with each other to exchange control
information. The structure of the mobile host module is the following:
• Network layer - This is the highest layer in current implementation. Here the GBL
algorithm resides. The module also provides basic network layer services like handling
packets from the lower layer, sending packets down to the lower layer and packets de-
/encapsulation (embedding headers in the message).
• MAC Layer - This layer controls the access to the wireless medium (air). It decides
when the node should send the message, so that the message do not interfere with the
other messages sent by the other nodes. In current implementation the MAC protocol
used is CSMA/CA (without RTS/CTS).
• Physical Layer - The physical layer is responsible for the process of sending and receiving
messages (frames). It controls the transmission power and decides whether the received
• Mobility module - There are two different mobility models implemented - Random
Waypoint Movement Model and Circular Mobility Model. They are described in more
detail in the next section. They define the host’s movements patterns, they also inform
the Connection Manager module about the changes in host’s position.
• Other modules - The utility module provides an interface between different node’s
modules for exchanging control messages and between nodes and the network’s Stats
module for collecting statistical data. There is also an Address Resolution Protocol (ARP)
module, which is useful in the translation between network and MAC addresses.
Such design is very flexible. It is possible to simulate the network with different mobil-
ity models by replacing the Mobility module or different MAC protocols (Mac module) with-
out changing a line of code in other modules. Furthermore, every module is exhaustively
parametrized, which allows modeling different network scenarios with much precision. The
source code of the simulation environment is included on the CD (See Appendix D).
4.2.2 Mobility models
When simulating real-life scenarios, there is a need of appropriate mobility models. If the
model used in the simulation does not reflect reality, the obtained results are not reliable and
can be misleading. In this thesis, circular mobility was used for the first scenario and Random
Waypoint Movement model for the second one.
In the first scenario, bikes or cars are moving along the road. Their speed can be described
as a two-dimensional vector. To simplify the model, the road can be ”stretched” in order to
make a straight line from it. In this way, one dimension can be removed and the movement
can be treated as a movement along the line. This could even eliminate the need to use
GPS, measuring connectivity in only one dimension. To support long, continuous simulations,
a circle can be formed from that line, where the cyclists, moving with various speeds, form
dynamic groups by approaching and receding in time. In the circular mobility model from this
thesis, all the nodes are moving along the same circle, with the specified speeds. There is a
Circular Mobility Model available in MiXiM (CircularMobility package), but its simplicity made
it impossible to model the movement patterns of the scenario. A modified version of this model
was created (akCircularMobility package) which allows choosing different speeds for every node
by generating a random value from a normal or uniform distribution. The similar situation was
with the MiXiM’s Random Waypoint Movement Model. The akRandomWaypointMovement
34
47. package was created, allowing the use of various speeds, just like in akCircularMobility. Both
mobility models are depicted on figure 4.3 and Figure 4.4.
In the future, trying BonnMotion [51] - a java software tool for creating and analyzing
mobility scenarios is considered. With this tool it is possible to model more advanced movement
patterns, like Gauss-Markov model, Reference Point Group Mobility model, Manhattan Grid
model or Disaster Area model. There is even a way to use recorded real-life traces by using the
ANSim module, which parses ANSim trace files.
Figure 4.3. Circular mobility model. Figure 4.4. Random Waypoint Movement.
4.2.3 GBL implementation
The GBL clustering algorithm was implemented in the network layer. Such decision was mo-
tivated by the possibility of evaluating the algorithm performance with different MAC protocols
without the need to change the algorithm code.
As explained in Section 4.1.2, the modified GBL works in a periodic manner. In the begin-
ning of every round, leader election scheme is executed according to GBL rules and afterwards
the leaders can serve as data collection points for their cluster members or as a backbone for the
routing algorithm. A single round is depicted in Figure 4.5. Acknowledgments (ACK messages)
were also implemented to be sure that the messages reach their destinations. In addition, every
control message (e.g. Join, Leave) is sent only once per round, to preserve timing constraints.
4.3 Simulation results
The two scenarios introduced in Chapter 2 were simulated. Four experiments were run
for the first scenario. Cyclists were moving along a circle, with different parameters set. The
parameters altered during each experiment and the other most important ones are shown in
Table 4.1.
For the second scenario, two experiments were run. In this scenario, mobile hosts were mov-
ing within a playground of 500m x 500m, according to the Random Waypoint Movement model.
The parameters altered during each experiment and the other most important parameters are
shown in Table 4.2.
4.3.1 Scenario 1, Experiment 1 - Increasing group size
This experiment was performed to get to know how the algorithm works when the group
size increases. The hosts were static and the new ones were added next to the existing group
(See Figure 4.6), keeping the distance between neighboring cyclists constant.
35
48. Figure 4.5. Single GBL round. First phase (cluster formation) is divided into three subphases:
Hello messages listening, Leadership score messages listening and Join/Leave messages listening
periods.
Figure 4.6. Scenario 1, Experiment 1. Group size increases linearly (in one dimension), forming
a line of cyclists.
We can see high efficiency in clustering, when the group size is not bigger in diameter than
the transmission range, which in this case covers about 20 cyclists. When the size of a group
exceeds transmission range, the number of leaders elected increases. This is a property of GBL.
This experiment also shows, that clustering is a scalable solution, which is not surprising, as
it is a distributed algorithm. The group size can be extremely large and we will get the same
results (assuming the same density and other parameters, like transmitting power).
4.3.2 Scenario 1, Experiment 2 - Transmitting Power
During this experiment the network was simulated using three different transmitting powers:
1mW, 5mW and 100mW which corresponded to transmission ranges of 50m, 100m and 250m.
The number of cyclists varied from 1 up to 200. Their initial placement was done by choosing
a random place from the restricted area of the circle (angles from 0◦ to 60◦), according to the
uniform distribution. In this way, with growing number of cyclists, their density also grows.
The cyclists speed was 10 m/s with a standard deviation of 0.7 m/s (normal distribution). The
36
49. Parameter Unit Experiment 1 Experiment 2 Experiment 3 Experiment 4
Number of Nodes 1 1-200 1-200 1-200 100
Circle radius m 430 430 430 430
Speed, mean m/s 0 10 10 10
Speed, std dev m/s 0 0.7 0.001, 1, 6 0.1
Timer Hello Interval s 100 100 100 100
T. HelloListen Int. s 4 4 4 4
T. ScoreListen Int. s 4 4 4 4
T. JoinLeaveListen I. s 4 4 4 4
T. WaitForACK Int. s 3 3 3 3
τ s 60 60 60 60
α 1 0.5 0.5 0.5 0.5
β 1 0.5 0.5 0.5 0.5
w1 1 0.5 0.5 0.5 0.5
w2 1 0.5 0.5 0.5 0.5
TxPower mW 5 1, 5, 100 5 5
Bitrate kbps 200 200 200 200
Simulation time s 1 000 1 000 1 000 20 000
Table 4.1. Parameters values used in the experiments, Scenario 1.
Figure 4.7. Scenario 1, Experiment 1. Number of nodes forced to transmit via GPRS (Number
of leaders plus singletons), static nodes.
Figure 4.8. Scenario 1, Experiment 1. Number of nodes in the network (N) div. by number of
nodes forced to transmit via GPRS (L+S), static nodes.
37
50. Figure 4.9. Scenario 1, Experiment 2. Number of nodes forced to transmit via GPRS (Number
of leaders plus singletons), three different transmitting powers used.
Figure 4.10. Scenario 1, Experiment 2. Number of nodes in the network (N) div. by number of
nodes forced to transmit via GPRS (L+S), three different transmitting powers used.
other parameters were constant. Every single simulation lasted 1000 s. The results are shown
in Figures 4.9 and 4.10.
From the first look, the results are surprising. We would expect decreasing ratio of GPRS
nodes in the network when the group density and size increases, as well as bigger influence of
transmission power - in these situations the number of neighbors grows, so we would expect
bigger clusters and lower number of GPRS nodes. However, we need to have in mind, that
with increasing density and transmission power, the network starts suffering from interferences
and congestion. When the node has transmission problems (e.g. no ACK for the Join message
sent), it turns into a singleton. So with growing number of nodes or higher transmission power
in this experiment, the number of singletons also grows, increasing the overall number of GPRS
nodes.
4.3.3 Scenario 1, Experiment 3 - Speed
In this experiment the network was simulated with three different cyclists’ speeds distri-
butions: 10 m/s with a standard deviation of 0.001 m/s, 10 m/s with a standard deviation of
1 m/s and 10 m/s with a standard deviation of 6 m/s. In all three cases normal distribution
38
