DYNAMIC CONTROL OF MOBILE AD-HOC NETWORKS

Network Protocol Parameter Adaptation using Organic Network Control

Sven Tomforde, Bj

orn Hurling and J

org H

ahner

Institute of Systems Engineering, Leibniz Universit

at Hannover, Appelstr. 4, 30167 Hannover, Germany

Keywords:

Organic computing, Self-optimising data communication protocols, Framework for protocol control, Mobile

ad-hoc networks.

Abstract:

Data communication protocols show an increasing complexity in terms of variable conﬁgurations – especially

if their target execution area is highly dynamic. One domain that represents these characteristics are mobile

ad-hoc networks (MANets). Since nodes are moving, the situation surrounding a particular node is steadily

changing. This provides the opportunity to signiﬁcantly increase the system’s performance by continuously

adapting the protocol. This paper demonstrates the beneﬁt of such an adaptation using the Organic Network

Control (ONC) system. Based upon a brief overview of ONC, the adjustment of the framework to enable

the control of MANets is described, followed by a simulation-based evaluation using an exemplary broadcast

protocol.

1 INTRODUCTION

As the number of interconnected devices and the cor-

responding transfer load of data communication net-

works is steadily increasing, networks reach their lim-

its. This leads to the insight that the currently used

techniques (e. g. protocols and infrastructure) will not

be able to cope with the demand in the near future

(Handley, 2006). Based upon this assumption, re-

searchers develop new concepts (e. g. for the Internet

(Siekkinen et al., 2007)). To cope with the problem,

two approaches are possible: Develop new protocols

(with higher complexity in terms of more parame-

ters) and increase the quality of service or dynam-

ically adapt existing protocols to changing environ-

ments, which is done by the Organic Network Control

(ONC) system (introduced in (Tomforde et al., 2009a;

Tomforde et al., 2009b)).

The ONC system is based on the principles of

Organic Computing (Schmeck, 2005) which is a re-

cent research area focusing self-organisation to deal

with complex problems. Autonomous entities are act-

ing without strict central control and achieve global

goals although their decisions are mainly based on lo-

cal knowledge. The authors assume that due to the

complexity of the particular tasks not all situations

can be foreseen during the development process of

the system. Therefore, the system must be adaptive

and equipped with learning capabilities, which leads

to the ability to learn new actions and strategies for

previously unknown situations. The self-control of

network entities is also part of the focus of Autonomic

Computing (Kephart and Chess, 2003).

A demanding challenge for the ONC system is

the control and adaptation of mobile ad-hoc network

(MANet) protocols as they are processed in highly

dynamic environments. The possible movement of

nodes leads to a continuous change of the situation:

Neighbours are getting out of reach or joining the

sending distance. This does not only lead to complex

problems of how to conﬁgure the protocol, it also of-

fers high potential for an improvement of the system

performance. Within this paper, we explain how the

ONC system is applied to a MANet broadcast proto-

col and how the overall performance of the MANet

system can be increased using ONC.

This paper demonstrates the application of the

ONC system to MANet-based broadcast protocols

by dynamically adapting network protocol parame-

ters (e. g. values for timeouts, maximum number of

re-transmissions, number of open connections, etc.).

Section 2 describes the related work and gives a sum-

mary of approaches to adapting network protocols dy-

namically to changing environments, followed by an

overview of the ONC system in Section 3. In Sec-

tion 4, we explain how the ONC system can be ap-

plied to a new protocol and what is actually done

to enable MANet protocol control using ONC. Af-

Tomforde S., Hurling B. and H

ahner J. (2010).

DYNAMIC CONTROL OF MOBILE AD-HOC NETWORKS - Network Protocol Parameter Adaptation using Organic Network Control.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 28-35

 SciTePress

terwards, Section 5 demonstrates the beneﬁt of us-

ing ONC by explaining and analysing the achieved

results. Finally, Section 6 summarises the presented

system and names further research to be done.

2 RELATED WORK

Autonomic adaptation of networks is part of the focus

of Autonomic Computing (Kephart and Chess, 2003).

Researchers have considered the problem to adapt

network protocols for many years: from off-line opti-

misation when presenting a new protocol to adapting

protocol conﬁgurations during runtime. For the off-

line optimisation, several examples can be found in

literature, but none aims at providing a generalised

approach for more than one speciﬁc protocol (see,

e.g., (Montana and Redi, 2005; S

ozer et al., 2000;

Turgut et al., 2002)). Due to time and computational

restrictions, on-line adaptation is a more complex task

compared to the off-line part. Besides the ONC ap-

proach, different directions of research are known to

cope with the problem: adaptive protocols, compo-

sition of protocol stacks, or centralised solutions to

adapt protocol conﬁgurations.

The most obvious way of dealing with the prob-

lem is to develop adaptive protocols which are able to

handle dynamic environments. One example has been

presented in (Whiteson and Stone, 2004). They intro-

duced an on-line learning mechanism to increase the

performance of a routing protocol. Based on the Q-

routing techniques presented in (Boyan and Littman,

1994), they learn the best routes by receiving imme-

diate answers of the next hop. Another example has

been introduced in (Huang et al., 2009). They present

an adaptive medium access control (MAC) protocol

framework. Since the radio node density and ser-

vice requirements can vary widely over time, they de-

ﬁned the need of an adaptation to changing environ-

ments and needs. Their protocol prototype can switch

between CSMA and TDMA within a radio platform

scenario. Both approaches rely on the existence of

a protocol extension covering the learning/adaptation

information. In contrast to the ONC system, they

are system-speciﬁc solutions and cannot be applied

to other protocols.

Since developing new protocols for all possi-

ble adaptation and learning processes is not feasi-

ble, a research ﬁeld called protocol stack composition

emerged covering the upcoming tasks by exchanging

protocols and stacks dynamically (Rosa et al., 2007).

In contrast to the ONC system which keeps the exist-

ing and currently used techniques and optimises their

behaviour, this ﬁeld of research re-combines the pro-

tocols. Although the target deviates from the ONC

approach (the protocol stack exchange has impact on

all involved systems and can hardly be done locally),

the approach has some similarities. The most impor-

tant representatives are Appia (Miranda et al., 2001),

Cactus (Hiltunen et al., 2000), Ensemble (van Re-

nesse et al., 1998), and Horus (van Renesse et al.,

1996). Additionally, the recent work done by Mena

et al. (Mena et al., 2003) has to be named. Besides the

locality aspect, some characteristics of the approach

separate it from the requirements of the ONC frame-

work: the protocols and their conﬁgurations have to

be known in advance and further extensions with new

behavioural repertoire are not possible.

The approach presented in (Sch

oler and M

uller-

Schloer, 2004) is also dealing with a kind of proto-

col composition, but is already a bit more focused on

the techniques used within the ONC system. The au-

thors describe their adaptive monitoring architecture

for protocol stack conﬁguration and demonstrate the

integration in the Observer/Controller pattern of Or-

ganic Computing. The learning part is covered by

a Fuzzy Learning Classiﬁer System (Casillas et al.,

2004). Due to the usage of the same architectural

pattern (Observer/Controller), the approach has some

similarities with the ONC system. Unlike the ONC

framework, the approach is built again without of-

fering the opportunity of handling different protocols

and extending the set of solutions on demand.

On-line adaptation of protocols itself has been fo-

cused by researchers before. (Sudame and Badrinath,

2001) presents a ﬁrst TCP- and UDP-based study and

deﬁned the need of dynamic adaptation, but detailed

examination and a demonstration of the re-usability

for other protocols is still missing. Currently, there

exist only two approaches covering a similar target

as ONC: the systems introduced by Ye et al. and by

Georganopoulos and Lewis. The former one (Ye and

Kalyanaraman, 2001) introduces an adaptive random

search algorithm which tries to combine the stochas-

tic advantages of pure random search algorithms with

threshold-based knowledge. Their approach is based

on the initial system as presented in (Ye et al., 2001).

In contrast to our approach, Ye et al. propose a cen-

tralised system that tackles the optimisation task for

each node. To allow for such a division of work

between a central server and the particular network

nodes, problems like e. g. bandwidth usage, single

point of failure, or local knowledge accessible from

server-side have to be covered.

The second system has been presented in 2007

(Georganopoulos and Lewis, 2007) and introduces a

dynamic optimisation framework for the reconﬁgura-

tion of network protocols at all layers of the protocol

DYNAMIC CONTROL OF MOBILE AD-HOC NETWORKS - Network Protocol Parameter Adaptation using Organic

Network Control

stack. In order to optimise the performance of the sys-

tem depending on given goals, different entities can

be adjusted (applications, protocols, etc.) or replaced.

Again, the system relies mainly on a centralised el-

ement being responsible for the optimisation tasks.

The focus of the initial paper has been set on cross-

layer optimisation for the protocol stack, but less on

considering environmental conditions. Hence, the au-

thors demonstrated the performance of the solution

by applying it to two different layers of the protocol

stack: the link and the network layer. A detailed proof

of the approach and insights on the currently vague

blackbox dynamic optimisation engine are still miss-

ing, consequently a suitability of the approach cannot

be estimated – although some criteria (centralised el-

ement, low re-usability of existing protocols, etc.) are

contradicted for the ONC requirements.

3 THE ORGANIC NETWORK

CONTROL SYSTEM

The Organic Network Control (ONC) system has

been introduced in (Tomforde et al., 2009a; Tomforde

et al., 2009b). The system’s architecture is founded

on the generic Observer/Controller approach as pre-

sented by Richter et al. in (Richter et al., 2006) and

is organised using three consecutive layers, see Fig-

ure 1.

Layer 0 encapsulates an existing network protocol

instance, e.g. a broadcast algorithm for mobile ad-hoc

networks (MANets) or a Peer-to-Peer (P2P) protocol.

In terms of Organic Computing, this controlled net-

work protocol instance is the ”System under Obser-

vation and Control” (SuOC). The ONC system aims

at providing a basic solution to control existing proto-

cols dynamically without the need of knowing inter-

nals of the particular protocol or interfering with the

protocol logic. However, it is required that the pa-

rameters of the protocol can be altered by the ONC

system. Additionally, the current status of the proto-

col instance and the environment it acts in have to be

observable and accessible locally. For MANets, one

of the most important factors describing the current

status of the protocol instance’s environment is the

neighbourhood of other nodes. Besides this observ-

able environment, a performance measure (also called

ﬁtness or evaluation function) quantifying good and

bad performance has to be provided in order to evalu-

ate the current performance of the protocol.

Layer 1 of the ONC architecture aims at adapting

the SuOC dynamically to changes in the environment.

It therefore consists of two basic components: an Ob-

server and a Controller containing a machine learning

Figure 1: Architecture of the ONC system.

component (which is currently realised as a Learning

Classiﬁer System (LCS)). The Observer is responsi-

ble for locally collecting status information about the

network protocol instance and its settings. Addition-

ally, it aggregates the observed ﬁgures and augments

them with optional further knowledge (e.g. prediction

values, historic knowledge, etc.) and builds a vector

describing the current situation at the node. This sit-

uation vector then serves as input to the Controller

which has to fulﬁll two tasks: evaluate the system’s

performance within the last evaluation cycle and de-

cide about the next action to be taken. The main com-

ponent of the Controller is a LCS which is realised as

an adapted variant of Wilson’s XCS (Wilson, 1995).

The LCS is responsible for choosing the next action

– based on the situation vector. The result of the se-

lection process is an action (a conﬁguration of param-

eter sets for the controlled protocol) and a prediction

value of how the system will perform after applying

the action. To evaluate the system’s performance, the

Controller compares the system’s performance mea-

sure (the ﬁtness function) with the last prediction and

calculates the reward for the LCS to enable the au-

tomated learning. In case the LCS does not contain

a matching parameter set, new classiﬁers need to be

created. In contrast to the original LCS algorithm,

however, the ONC architecture does not allow new

classiﬁers (pairs of situation/conditions and parame-

ters/actions) to be created randomly by Genetic Algo-

rithms. Instead, control is transferred to Layer 2 of

the ONC architecture.

Layer 2 of the ONC system is again designed us-

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

ing the Observer/Controller pattern: The Observer

monitors the Layer 1 component and realises the need

of a new classiﬁer, it therefore receives the situation

vector. The Controller part contains two basic com-

ponents: a simulator and an Evolutionary Algorithm

(EA). The Controller creates an appropriate simula-

tion scenario from the situation vector and triggers

the EA to repeatedly evolve a number of parameter

sets for the network protocol. These parameter sets

are evaluated in the simulator. This bears the advan-

tage that newly created parameter sets are not directly

used in the live system, as this can cause the system

to perform badly or even malfunction. Only those pa-

rameter sets that qualify in the simulator of Layer 2

are passed back to Layer 1 and may then be applied

in the real world. Therefore, Layer 2 allows for a kind

of sandbox-learning without the risk of applying arbi-

trary parameter sets to the live system.

The ONC approach as described before provides

a black-box solution to control different types of net-

work protocols. In order to integrate a new proto-

col into the ONC architecture and consequently en-

able ONC controlling the protocol, an engineer has

to fulﬁll three major tasks: Specify the performance

metric, describe the situation (what are the dynamic

factors deﬁning the need of an adaptation, e.g. avail-

able neighbours and their positions in MANets) ac-

companied by a distance function between two sit-

uations, and provide a simulation model to enable

the simulation-based optimisation process of Layer 2.

Within the following Section, we describe how the

ONC system is applied to MANet protocols.

4 DYNAMIC CONTROL OF

MOBILE AD-HOC NETWORKS

This Section describes how ONC is adapted to allow

for the control of MANet protocols - based on the

tasks named within the last Section. In order to keep

the same organisation as before, this Section again

distinguishes between the three layers and describes

what has to be done on each layer.

The focus of Layer 0 is to integrate a new proto-

col into the framework. Therefore, the engineer has to

describe its observation and control process leading to

the need of two interfaces: one for accessing the pro-

tocol parameters and one for collecting information

about the local status of the system. The former in-

terface enables the framework to adapt the behaviour

of the protocol which means the parameter settings

can be adapted at runtime. In the latter interface, the

engineer has to deﬁne what is relevant and inﬂuences

the protocol’s performance - we call this the situa-

tion the system is in. In a MANet environment, the

most important factor inﬂuencing the protocol’s per-

formance is the distribution of other nodes within its

sending and sensing range. Therefore, a sector-based

approach as depicted in Figure 2 has been developed.

The radius of the outer circle is equal to the sensing

distance (sensDist) of the node, as this is the most re-

mote point where messages of this node can interfere

with other ones. Typically, the transmission range

for Wireless-LAN based MANets is about 250 meter

(half of the sensing distance). The radii of the inner

circles have been chosen empirically.

Figure 2: Environment representation.

As nodes within the ﬁrst circle are really close

(50m), their exact position does not matter. The sec-

ond circle (125m) has been partitioned into 4 sectors,

the third circle (200m) into 8 sectors, and the forth

circle (250m, maximum transmission range) into 16

sectors. The next two circles (375m and 500m) are

representing the area within sensing range - with both

circles divided into 32 sectors each. We assume that

a node is able to determine the current positions of its

neighbours in sensing range relative to its own posi-

tion (e. g. based on GPS, see (Pahlavan and Krishna-

murthy, 2001)). Additionally, the node’s direction of

movement is needed since it has high inﬂuence on the

best parameter set (e. g. moving towards/away from a

set of nodes inﬂuences the delay). Due to the sector-

based approach, situations are generalised which is

necessary to avoid evolving a rule for each situation.

The Layer 1 component is responsible for the

adaptation process and for increasing the system’s

performance by learning. Again, two aspects have to

be considered: a learning feedback and a measure-

ment to compare different situations. To enable the

learning feedback, a ﬁtness or evaluation function is

needed. Several ﬁtness functions have been proposed

for MANet protocols. Since our current focus is set

on MANet-based broadcast algorithms, the standard

functions are Packet Delivery Ratio and Packet La-

DYNAMIC CONTROL OF MOBILE AD-HOC NETWORKS - Network Protocol Parameter Adaptation using Organic

Network Control

tency – both cannot be measured locally at each node.

Based upon the locally available information, the tar-

get is to reduce the number of forwarded broadcasts

and assure the delivery of the broadcast to each node

at the same time. Therefore, we introduce the follow-

ing formula:

Fit(x) =

#RecMess

#FwMess

The variable x stands for the currently observed

network protocol instance. Since a new parameter set

has to be applied for a minimum duration to show its

performance, we use evaluation cycles deﬁning dis-

crete time slots. The duration of the cycle depends

on how dynamic an environment is: The faster it

changes, the shorter is the cycle to be chosen. For

the last evaluation cycle, the function takes into ac-

count the sum of all messages being forwarded by all

of the neighbours and the node to be evaluated within

the last evaluation cycle (#FwMess), and the sum of

all messages being received by them (#RecMess).

The second aspect on Layer 1 is the comparison

of situations: we need to quantify the distance of two

situations. The target is that more similar situations

will receive a low distance value and those having low

similarity will receive a high distance value. Based

upon the sector-based situation description as intro-

duced before, a measure for the similarity of two enti-

ties (A, B) can be deﬁned. To be able to determine the

distance, the possible inﬂuence of rotation and reﬂec-

tion are deducted initially. Afterwards, the formula

for the distance (δ) can be deﬁned with r ∈ RADII

and s ∈ SECT ORS as follows:

δ(A,B) =

∑

r,s

− B

r,s

)

/r.distance

The function r.distance deﬁnes the radius size as

introduced before (50m, 125m, . . . ). A

r,s

gives the

number of neighbours within the sector s of radius

r for the situation description A. This means that the

importance of a node’s neighbour decreases if it is sit-

uated within an outer radius.

Finally, Layer 2 has to be able to build adequate

simulation scenarios out of the information obtained

by Layer 1. In ONC, we use the standard network

simulation tool NS-2 (Fall, 1999), but this can eas-

ily be exchanged by other solutions. The network

simulator NS-2 has a large set of integrated or avail-

able standard protocols, but for recently developed

or proprietary protocols a simulation model probably

does not exist. The engineer has to provide a realistic

model (as it is also used during the protocol develop-

ment) which can be adapted to the observed situation

by generating an appropriate scenario. The adapta-

tion of the scenario is done using the conﬁguration

interface by considering the observed situation of the

node, e.g., a randomised instance of the sector-model

is created deﬁning the distribution of the neighbour-

ing nodes and the movement direction of the node is

transmitted to NS-2 using the same coordinate system

as for the observed system. After ﬁnishing the previ-

ously described tasks, ONC is able to control MANet-

based protocols. The beneﬁt of the dynamic control

is demonstrated in the next Section.

5 EVALUATION

Based upon the previously described adaptations,

ONC is able to control MANets. Within this Sec-

tion, the results of the evaluation are presented. Since

the adaptation of protocols is organised locally - but

has high inﬂuence on the network-level, both aspects

are taken into consideration. Therefore, we start with

the experimental setup, followed by a short introduc-

tion of the analysed protocol, and conclude with the

achieved results for the local and the network-wide

view.

5.1 Experimental Setup

The ONC framework is implemented in JAVA. The

moving agents communicating via the MANet pro-

tocol are simulated using the Multi-Agent Simulation

Toolkit MASON (Luke et al., 2004), with each agent’s

protocol instance representing a SuOC of the architec-

ture as depicted in Figure 1. The respective Layer 1

Controller is an adapted Learning Classiﬁer System as

described in (Tomforde et al., 2009b). At Layer 2, the

standard network simulation tool NS-2 (Fall, 1999)

is used to evolve new parameter sets in combination

with a standard Genetic Algorithm (population size:

15, new children per iteration: 7, mutation rate: 0.2

per child, all children via crossover with ﬁtness-based

selection of parents). We use two different simulation

tools in order to avoid having exactly the same condi-

tions while optimising rules, since a complete copy of

the current situation observed in the real environment

within the simulator is not realistic. 100 agents have

been created and applied to the simulated area, which

has dimensions of 1000 x 1000 cells (corresponds to

1000 x 1000 meters). The agents move according to a

random-waypoint-model. The Physical/Mac layer is

an IEEE 802.11 in ad-hoc mode at 2 Mbps.

To demonstrate the performance of ONC control-

ling MANets, we choose the Reliable Broadcast Pro-

tocol (R-BCast) as introduced in (Kunz, 2003), since

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

this protocol is representative for the research ﬁeld

of reliable broadcast protocols in MANets. In order

to achieve reliability and increase the packet delivery

ratio compared to other protocols, additional effort

is made by equipping the nodes with extra buffers.

These round-robin based buffers are used to store the

last p unique packets the particular nodes received.

In contrast to other protocols, the R-BCast protocol

has signiﬁcantly more variable parameters and conse-

quently the task to control the protocol is more com-

plex, but it also offers a higher potential beneﬁt due to

a dynamic adaptation. The parameters being subject

to ONC control actions are: Delay (Maximum decel-

eration time between receiving and forwarding of a

message), Allowed Hello-loss (Maximum number of

Hello-messages, which may be lost until a node is as-

sumed to be out of transmission range), HelloInter-

val (Interval between two Hello-messages), δ Hello-

Interval (Randomises Hello-Interval), Packet count

(Number of the last x stored NACK messages), and

Minimum difference (Minimum difference between

NACK messages). Details on the parameters and the

protocol can be found in (Kunz, 2003).

5.2 Experimental Results

In order to analyse the performance of the ONC sys-

tem, the simulation is repeated for two cases under

the same restrictions and using the same seeds for the

randomised values: a) all nodes are uncontrolled (no

ONC system) and use the manually optimised stan-

dard conﬁguration of the protocol, and b) all nodes

have an own instance of the ONC system to control

their protocol conﬁguration. All values presented in

the remainder of this Section are averaged values re-

ceived from three runs, where each run has a dura-

tion of 10,000 simulated seconds. During one run of

the scenario, 17,400 BCast-messages have been sim-

ulated. The learning component has been trained us-

ing 10 complete runs with different seeds – leading

to completely different movements of the nodes and

along with these to different situations.

The performance measurement relies on the ﬁt-

ness function as described for the local feedback

mechanism of the learning component (Section 4).

Since the evaluation takes both views into account (lo-

cal and network-wide view), the x in Fit(x) refers to

different systems: a) In the local view x stands for the

local network protocol instance of the node and b) on

network-level obtained for reference, x represents the

set of protocol instances within the network and gives

an averaged value for all instances.

Figure 3 plots the system’s performance consid-

ering only one node. The X-axis describes the sim-

ulation time (in simulated seconds) and the Y-axis

the measured ﬁtness value. In principle, all simu-

lated nodes show a comparable behaviour; this spe-

ciﬁc node has been explicitly chosen to demonstrate

the typical differences between an ONC-controlled

and an uncontrolled node. During the simulation, the

node gets separated from the rest of the network (no

other nodes within sending distance) between simu-

lation seconds 7,350 and 7,700. Within this interval,

the ﬁtness is 0 for both cases. But especially these sit-

uations demonstrate the beneﬁt of ONC control: The

delays have been lengthened so that the node receives

more old messages when it arrives back in sending

distance of another node resulting in a quicker recov-

ery of the ONC-controlled system.

Another observation that can be made considering

Figure 3 is the impact of the learning module. To be

able to learn, it has to be allowed to try different rules

and not to use always the best matching one. E.g.

at simulation second 1,800, the learning component

tried a rule that results in a performance slightly worse

than the standard protocol conﬁguration. These small

drawbacks have to be taken into account to achieve

an improvement for the system. Averaged over the

complete simulation time (10,000 simulated seconds),

the performance of the protocol instance has been en-

hanced in terms of the ﬁtness function from 0.8270

(all nodes perform the standard protocol without any

adaptation) to 0.8991 which is an increase of 8.71%.

Figure 4 depicts the averaged performance of the

network protocol instances on network-level. The av-

eraging leads to the effect that separation of single

nodes inﬂuences the performance only slightly. Nev-

ertheless, Figure 4 shows two drops (simulation sec-

onds 3,850 to 4,200 and 7,450 to 7,550). The ﬁrst

drop can be explained by a split of the set of nodes –

about 30 nodes are not within sending distance of the

rest. Here, two different networks have been estab-

lished. Within the second drop, again, a larger group

(18 nodes) has been separated from the rest of the

network. Despite these separation effects, the per-

formance of the system has been increased. When

all nodes perform just the standard protocol conﬁgu-

ration without any adaptation, the resulting averaged

ﬁtness is 0.8760. The same simulation with additional

ONC control for all nodes leads to an averaged ﬁt-

ness value of 0.9456 which is an increase of 7.94%.

Both aspects of the ﬁtness function are responsible

for the increase: The number of forwarded messages

has been decreased slightly, whereas the number of

received messages has been increased more signiﬁ-

cantly.

DYNAMIC CONTROL OF MOBILE AD-HOC NETWORKS - Network Protocol Parameter Adaptation using Organic

Network Control

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DYNAMIC CONTROL OF MOBILE AD-HOC NETWORKS - Network Protocol Parameter Adaptation using Organic

Network Control