Adaptation Delay and Its Impact on Application

Performance for TDMA Ad Hoc Networks

Jimmi Grönkvist, Jimmy Karlsson, Ulf Sterner, Jan Nilsson, and Anders Hansson

Division of Information and Aeronautical Systems

Swedish Defence Research Agency

Email: {jimmi.gronkvist, jimmy.karlsson, ulf.sterner, jan.nilsson, anders.hansson}@foi.se

Abstract—With the advances in military ad hoc networking,more capable and adaptive protocol solutions are being proposedand are evolving. Many are based on TDMA. Issues remain,however, concerning the capability of TDMA to adapt to thedynamics of mobile ad hoc networks. To address this issuea general traffic-adaptive TDMA-based ad hoc network usingproactive routing is considered. The focal point is to determinehow the protocol adaptation delay affects the performance ofapplications with different delay requirements. Besides the totaladaption delays, the individual MAC and routing adaptationdelays are also of interest. Results show, among other things, thatit will be unfeasible to adapt the protocols to sessions with delayrequirements that are too strict. Additional backup mechanismshave to be added to deal with such sessions. Moreover, theadaptation delay for the routing is not as crucial as for theTDMA protocol.

I. INTRODUCTION

In military ad hoc networking, the main challenge is proto-

col design. The routing and, perhaps even more, the medium

access control (MAC) design are crucial for good performance.

Contention-based MAC, such as Carrier-Sense Multiple Acess

(CSMA), or reservation-based MAC, such as Time Division

Multiple Access (TDMA), have different pros and cons. The

contention-based protocol deals better with dynamic network

situations whereas the reservation-based protocol has poten-

tially a larger throughput and better ability to provide QoS

guarantees. In non-military ad hoc networks, by far the most

common protocol standard IEEE 802.11 is based on CSMA

with collision avoidance [1]. However, in military ad hoc

networks TDMA-based solutions are often selected instead;

one example is USAP [2]. A number of solutions have been

proposed for dynamically adapting the TDMA scheme to a

changing network topology and bandwith requirements [3]–

[5].

In this paper we consider a general traffic-adaptive TDMA

ad hoc network with proactive shortest path routing. TDMA

solutions can be made very efficient in static cases, but

the challenge lies in making them adaptive to changes. The

changes we refer to are changes in traffic patterns or network

topology, e.g. that a link goes down so that a new route has

to be found and used.

This work was supported by the FOI research project “Communicationnetworks for tactical voice and data”, which is funded by the R&D programmeof the Swedish Armed Forces.

It takes time to adapt; a change has to be detected, informa-

tion about it has to be spread and the protocols have to react

to it, e.g. from the point when a link disappears to the point

when the system has adapted to the new topology. We call the

time it takes from detecting a change to adapting the protocols

in all the nodes affected by this change to the new situation

the adaptation delay. Moreover, we are also interested in the

individual MAC and routing adaptation delays. Notice that

the adaptation delay can partly be controlled in the protocol

design by allowing more or less control information to be

sent, i.e. overhead. A small adaptation delay is desired but

can be costly; moreover, there are limitations to how small

the adaptation delay can be made. The aim of the paper is to

investigate how services, or traffic sessions with different delay

requirements, are affected by the adaptation delay. In particular

we show that the adaptation delay must be considerably

lower than the application delay requirements for the traffic

sessions to work satisfactorily. We also consider the MAC

and routing adaptation delays separately and show that the

MAC adaptation delay has the greater effect on performance.

Although TDMA protocols and system are well represented

in the literature, a similar analysis does not exist.

The paper is organized as follows: In Section II we more

precisely describe the adaptation delay and its properties.

How the adapation delay is modeled and the traffic-adaptive

TDMA-based ad hoc network we consider are described in

Section III. Section IV describes the scenario and simulation

setup. The results are presented in Section V. Finally, conclu-

sions are presented in Section VI.

II. PROTOCOL ADAPTATION DELAY

An ad hoc network can be highly changeable. This is

mainly due to mobility, but also due to changing traffic

flows. Different protocol solutions need to deal with these

changes in different ways and may be more sensitive to some

changes than others. These changes may lead to rerouting and

rescheduling of resources, processes that take time and require

network resources.

In principle, we can describe this process in three steps:

detection, dissemination and decision. The first step is to detect

the change. To detect a link failure, one has to transmit a

packet over the link; more specifically, a receiver of a link can

estimate its quality only by receiving (or attempting to do so)

what is actually transmitted on the link. As this does not occur

2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)

978-1-4673-2039-9/12/$31.00 ©2012 IEEE 55

all the time, there will be a certain delay in detecting events

on links. On a highly loaded link this delay may be short, but

unless traffic flows in both directions we may still see a certain

delay. For a link with low traffic flow (or none), the only

available information may be the administrative data regularly

sent by the routing and MAC protocols that are available (if

any).

In a similar way there may be delays in detecting traffic

changes that may, for example, require more time slots to be

given to the nodes on the path used for a new traffic flow,

i.e. how many packets that need to arrive before a node even

realizes it needs more resources.

The second step is to disseminate the information to the

nodes that need it. Depending on how many nodes that need

this information, the time this will take is very protocol-

dependent. Some information may only be needed locally. In

such cases, the result is very fast dissemination. In others,

information is needed all over the network.

The third step can be called decision (or negotiation) and is

the reaction to the information. In most of the existing proto-

cols the nodes can simply react to the information (reroute, or

accept a scheduling decision), but in some cases there may be

other responses (for example if the update needs to be accepted

before the protocol change is finished).

Some additional considerations to the detection of changes

need to be discussed in addition to the above. First, that a

change has occured may not even be relevant until the link is

considered for use. Proactive algorithms may detect changes

and in some cases also update protocols, but if there is no

useful traffic, it will have limited relevance for the performance

of the network. This complicates how this should be properly

measured because links without useful traffic may have longer

detection time than very busy links. For these reasons, we will

exclude the actual detection time from the adaptation delay we

investigate in this paper. Therefore, the time it takes from a

detected change until all protocols have adapted to the new

situation is defined as the adaptation delay ∆A.

It should be noted that the proactive type of algorithms

we study in the paper tends to detect changes on regularly

transmitted packets, e.g., HELLO messages, containing sta-

tus information. Similarly, the dissemination and negotiation,

which times are part of ∆A is also done by transmitting

packets at regularly intervals. The detection time, although not

directly part of the definition, should therefore be proportional

to ∆A.

A proactive routing protocol (e.g. OLSR [6]) will have

the following properties regarding adaptation delay: Normally,

updates are done without any consideration of traffic load.

Hence, only link changes will have any impact on the protocol.

Detection of link changes is based on administrative data such

as HELLO messages that are transmitted regularly. A node

can adapt to such a detected change immediately without

any information spreading (i.e. if a link fails, the node can

make a local rerouting to decide the next hop based on

information it already has in its data base) or decisions in

other nodes. The information about the change is spread

to the rest of the network through administrative messages

such as HELLO messages (locally) and Topology Control

(TC) messages (globally). Nodes receiving such messages

can update their routing table immediately without further

considering of other nodes.

A traffic-adaptive TDMA protocol will have the following

properties regarding adaptation delay: The scheduling process

will normally support reuse of time slots if nodes are suffi-

ciently far apart. To reach high efficiency, time slots need to

be assigned based on the actual traffic load on the nodes. This

means that both link changes as well as traffic changes will

have an impact on scheduling. All changes to the schedule

need to be negotiated in some way between the nodes that are

affected by the change. The number of nodes affected by a

change depends on the requirement for reuse of time slots. A

2-hop distance is common, but both more and less can be used

in some cases. Nevertheless, the number of nodes affected by a

change to the MAC schedule is normally less than the number

of nodes affected by a routing change.

It should be noted that there are correlations between the

routing and MAC adaptation delays that are not considered.

For example, a short routing adaption delay, accomplished

by frequent routing updates, would also stress the MAC

layer. Nevertheless, in the paper the effects on the application

performance of the adaption delays on the routing and MAC

layer are treated independently.

Notice that adaptation delay can partly be controlled in the

protocol design by allowing more or less control information

to be sent, i.e. overhead. A small adaptation delay is desired

but can be costly; moreover, there are limitations to how small

the adaptation delay can be made.

III. MODELING ADAPTATION DELAY

To investigate the effects of MAC and routing adaptation

delays on delay sensitive traffic, we use idealized protocols that

allow us to vary the adaptation delay in a controlled manner. In

this section we describe how the adapation delay is modeled in

our evaluation. We begin with a description of a basic model

of the detection, dissemination and decision process that we

use both at the MAC layer and the routing layer. We continue

with the physical layer after which we proceed through the

stack to the application layer.

A. Detection, Dissemination and Decision Process Model

The detection, dissemination and decision processes, de-

scribed in II, is modeled individually for each protocol layer.

Each protocol layer collect information about changes for

a time ∆C , before the dissemination and decision process

begins, see Figure 1. In average it will take a time ∆C/2 from

a change actually occurred until the dissemination phase starts

becouse of a fixed TDMA frame and slot structure. Moreover,

we assume lower layer information is available at all the layers

and the same collection time for all layers.

The dissemination and decision are assumed to have a

duration ∆D after which the protocol has adapted to the

changes. Thus the average time between the detection of a

56

Figure 1. Illustration of the information detection, dissemination, anddecision process.

change and the subsequent adaption of the protocol will be

∆A = ∆C/2 + ∆D. A change detected between time t1 and

time t2, in Figure 1, will take effect at time t3 with a adaptationdelay of ∆A.

B. Physical Layer

An essential part of modeling an on-ground or near-ground

radio network is the electromagnetic propagation characteris-

tics due to the terrain variation. A common approach is to

use the basic path-loss, Lb, between two nodes. To estimate

the basic path-loss between the nodes, we use a uniform

geometrical theory of diffraction (UTD) model by Holm [7].

To model the terrain profile, we use a digital terrain database.

All our calculations of the basic path-loss are carried out using

the wave propagation library DetVag-90 R© [8].

We define the signal-to-noise ratio (SNR), here defined as

Eb/N0, in the receiver node, Γ, as follows

Γ =P GT GR

NR LbR,

where P denotes the power of the transmitting node (equal

for all nodes), GT the antenna gain of the transmitter, GR the

antenna gain of the receiver, NR is the receiver noise power,

R is the data rate, and Lb is the basic path-loss between the

transmitter and receiver. We assume isotropic antennas in this

study.

The SNR for the links in a mobile ad hoc network will

often change quickly as the nodes move around in the terrain.

The quality of the link estimates will thus degenerate fairly

fast with time. To reduce the risk of using links with too low

SNR, a hysteresis functionality is used.

In Figure 2 we illustrate the hysteresis functionality. We

assume here that two nodes can communicate over a link if

Γ > γlow, i.e. between time t1 and time t5. However, thetransmitter will not start using the link before Γ > γhighat time t2. When the higher SNR level γhigh is reached the

transmitter will also start announcing the link to its neighbors.

The node will continue announcing the link to its neighbors

until Γ < γhigh at time t3. If the transmitter has announced a

link to its neighbors it will continue to use the link as a normal

link when γhigh ≥ Γ > γlow until its neighbors are notified

that the link do not exist anymore at time t4. The transmitter

will then stop using the link.

Figure 2. Illustration of the link hysteresis model.

The choice of hysteresis, i.e., γhigh, will affect how dynamic

the topology will be for a given mobility, i.e., the number

of changes that occurs within a given time frame. A large

hysteresis will make the topology less dynamic which is

an advantage, but also reducing the number of available

links which is a disadvantage. Hence, it is a tradeoff which

hysteresis to select, in this paper we have set it to 6 dB.

C. Medium Access Control

To divide the radio channel between the nodes, we use an

idealized traffic-adaptive TDMA protocol. The time is divided

into time slots of duration Ts. The time slots are grouped into

repeating frames consisting of NF time slots.

Each node will try to allocate enough time slots in each

frame so that it can manage its resource demands. Nodes with

more resources than needed will release unused time slots.

All traffic flows are assumed to have equal priority, i.e. if

resource demands exceed available resources, new demands

will be rejected in favor of existing allocations. If two nodes

simultaneously try to allocate the same available resource, the

node with the lowest MAC address will get the resource.

Unassigned time slots in the frame are used in round-robin

style by the nodes. All nodes, regardless of whether they have

an assigned time slot or not, will get access to the unallocated

time slots in circular order. To ensure that there always exist

some round-robin slots in a frame, we let at least one slot be

unallocated in each frame even if the demand for time slots

is higher. The adaptation delay, ∆A, for the MAC protocol is

denoted ∆AM .

The estimation of the traffic loads in the nodes is idealized.

When a packet from a new traffic flow arrives in a node, the

estimator used can estimate the resource demand for the flow

in bits/s on the first packet. The traffic estimator will keep the

demand for the resource for 0.2 s after that the last packet in

a flow arrived in the node.

D. Routing

We use an idealized minimum-hop-based routing protocol

which uses Dijkstra’s algorithm [9] to find the routes. When

57

the link layer in a node detects that a link to a neighbor goes

up or down the node will update its routing table immediately.

To model the detection, dissemination and decision process we

use the model described in section III-A. The adaptation delay,

∆A, for the routing protocol is denoted ∆AR.

E. Application

The traffic is modeled as unicast sessions with Constant

Bit Rate (CBR) flows. We assume that new sessions start

according to a Poisson process and that they have an expo-

nential distributed duration with a mean of 30 seconds. We

denote the average number of sessions that are simultaneously

active in the network with Λ. Furthermore, we assume that

the traffic is uniformly distributed over the nodes, i.e. each

node is equally probable as the source and each node except

the source is equally probable as the destination. If the route

between the source and destination is non-existing, at the start

of the session, a new source and a new destination is drawn.

During a session, the source is assumed to transmit packets

to the destination at a constant bit rate of 10.24 kbps, and

with a constant packet size of 512 bits. Thus, it models point-

to-point traffic and one-way connections. To model delay-

sensitive traffic we have a maximum acceptable delay, Dmax,

on the packets. Further, a session is considered failed if more

than 5% of the packets are delayed more than Dmax during a

session.

IV. SIMULATION SETUP

This section describes the software simulation performed.

The sample network studied consists of 64 nodes moving

according to a random walk model, where the nodes have a

speed of 20m/s. The scenario consits of nodes moving around

randomly for 1000 seconds in a square area of 64 km2. The

terrain we use is mainly flat, but with slightly hilly parts.

Let N be the number of nodes. Then, there are N(N − 1)possible point-to-point connections, either single-hop or mul-

tihop, between nodes. However, not all connections may exist.

In this study, we measure the connectivity as the fraction of

existing point-to-point connections averaged over a simulation

run. Whenever the connectivity is 100% all nodes can reach

each other through multihop during the whole simulation. In

this study the transmitter power P is chosen so that the sample

network has 95% connectivity at SNR level γhigh. Further-more, the lower SNR threshold is set to γlow = γhigh − 6 dB,

i.e. the connectivity will actually be slightly higher than 95%

in the simulations.

The length of a time slot, Ts, is set so that each time slot

can carry 512 bits of payload from the application. The frame

length NF is set at 64, i.e. without any dynamic allocations

each node will get one round-robin slot in each frame. Further,

the data rate of the system R is set so we get 20 frames per

second. Thus an allocation of one time slot in a frame will be

enough to support the traffic one session generates over one

hop.

To see how sensitive the individual protocols are to adap-

tation delays we consider three different system setups SR,

Table IADAPTATION DELAY FOR DIFFERENT SYSTEM SETUPS.

System ∆AM [s] ∆AR [s]

SR 0.01 ∆A

SM ∆A 0.01SR+M ∆A ∆A

Table IIMAX APPLICATION DELAY.

Application Dmax [s]

A0.2 0.2A1.0 1.0A5.0 5.0

SM , and SR+M . System SR represents a system with high

adaptation delay at the routing layer but with low adaption

delay at the MAC layer. System SM represents a system

with low adaptation delay at the routing layer but with high

adaptation delay at the MAC layer. System SR+M represents

a system with high adaptation delay at both the routing and

MAC layers, see Table I. Furthermore we consider three

applications, A0.2, A1.0, and A5.0, with different demands on

Dmax, see Table II.

V. RESULTS

In Figure 3 we show the session success rate as a function

of different adaptation delays ∆A, and a different number of

average active sessions in the network Λ, for application A1.0.

As can be seen, the probability of successful sessions is high

only for very light traffic loads or if the adaptation delay is

low. The blue curve in the figure represents the maximum

values of Λ that give a success rate of 80% and is the curve

we will study further in the paper. It can be seen as a capacity

measurement.

02

46

810

0

5

10

15

20

0

20

40

60

80

100

∆A

[s]

Λ [sessions]

Su

cc

ess

ra

te [

%]

Figure 3. The success rate for applications with delay requirements of 1second as a function of the adaptation delay and average number of activesessions in the network. The highlighted blue line marks a success rate of80%.

58

10−3

10−2

10−1

100

101

0

2

4

6

8

10

12

14

16

18

∆A/D

max

Λ [sessions]

A0.2

A1.0

A5.0

Figure 4. The maximum number of sessions giving a success rate of 80%as a function of the rate between adaptation delay and maximum applicationdelay ∆A/Dmax. This is done for application A0.2, A1.0, and A5.0.

We investigate to what degree the necessary adaptation

delays, ∆A, are proportional to the applications delay require-

ments, Dmax, by studying the ratio ∆A/Dmax. Therefore, in

Figure 4, we show the maximum value of Λ that give 80%

success rate as a function of the ratio ∆A/Dmax.

We see that the success rate curves are very similar for

the different applications, i.e. a specific value of ∆A/Dmax

will have similar result independent on application delay re-

quirements. This shows us that the needed adaptation delay to

support successful applications is at least close to proportional

to the required application delays.

When the ratio ∆A/Dmax increases the maximum value

of Λ will start to decrease. Up to a value of ∆A/Dmax

approximately 0.1 it is no problem at all to handle applications

and a very high throughput can be achieved. Above this point

more sessions will fail and we will see a rapid decline in

the number of sessions that can be successfully handled by

the network. When the adaptation delay becomes as high as

Dmax, only a small fraction of the traffic can be handled. In

such cases the round-robin (preallocated) slots will need to

handle the traffic load being offered as reservation of time

slots will be too slow to prevent session failures.

As can be noted there are some differences in the behavior

of the three success-rate curves. Although the application A5.0

can work with the largest adaptation delay, when considering

the ratio ∆A/Dmax for that application, A5.0 is most affected

by the mobility. This is because for a given value of ratio

∆A/Dmax the application A5.0 will have the largest values

of the time ∆A and consequently most number of changes in

the network per update interval.

Furthermore, at low values of ∆A/Dmax there is a differ-

ence for the maximum value of Λ between the applications.

The reason is mainly due to that the traffic estimator retains its

value for 0.2 s after loosing a packet flow because of rerouting

of that flow by another node. In a heavily loaded network this

will lead to a temporary increase of the queues due to no more

10−2

10−1

100

101

0

2

4

6

8

10

12

14

16

∆A

[s]

Λ [

sess

ion

s]

SR

SM

SR+M

Figure 5. The maximum number of sessions giving a success rate of 80%as a function of the adaptation delay. This is being done for application A0.2

in three different systems SR, SM , and SR+M .

10−2

10−1

100

101

0

2

4

6

8

10

12

14

16

18

∆A [s]

Λ [sessions]

SR

SM

SR+M

Figure 6. The maximum number of sessions giving a success rate of 80%as a function of the adaptation delay. This is being done for application A5.0

in three different systems SR, SM , and SR+M .

available resources until the traffic estimator is updated. This

will be more difficult to handle for low latency applications

such as A0.2.

To see how sensitive the individual protocols are to the

adaptation delay, we show, in Figures 5 and 6, the success

rate curves for the three different systems defined in Section

IV: SR, SM , and SR+M , the last as a reference being the

system used to this point in Section V. Figure 5 shows this for

application A0.2 and Figure 6 shows this for application A5.0.

As can be seen in these figures most of the delay sensitivities

are part of the MAC layer; the routing layer is less sensitive to

these problems and can often handle adaptation delays much

closer to (or even above) Dmax. In fact, comparing Dmax and

∆A gives less information here than in the combined case.

In the A0.2 case a rapid decline of the maximum value of

Λ starts already at about an adaption delay of one second,

which in this case is mostly due to temporary routing loops.

59

Because time slots are given to sessions rather than actual

traffic, and we have not added any mechanism to detect packets

that have already been sent, this will quickly overload the

affected nodes. These loops will be removed rather quickly

though, often in the next update, but in the A0.2 case it

is still insufficient. In the A5.0 case, the same phenomenon

occurs, but in this case some queuing can be handled by the

applications, resulting in a higher possible adaptation delay.

VI. CONCLUSIONS

In this paper we show that in order to have a high through-

put, the adaptation delay need to be considerably smaller than

the application delay requirement. Thus, traffic sessions having

strict delay requirements need adaptation delays that are very

difficult to achieve. This means that it is unrealistic to be

able to adapt the protocols to such sessions in mobile ad hoc

networks. Either disruptions of the time-critical session have

to be accepted or other solutions must be sought.

One option is to keep resources in reserve and first use them

instead of updating the TDMA schedule whenever a change

occur. Moreover, our results suggests that time critical sessions

can only be allowed to use a small part of the total capacity.

Furthermore, the adaptation delay for the routing is not as

crucial as for the TDMA protocol, i.e. to have a time slot

available to at least being able to send the packet on some

link/route towards the destination is more important than using

the best link/route.

REFERENCES

[1] IEEE, “IEEE std 802.11 - wireless LAN medium access control (MAC)and physical layer (PHY) specifications,” The Institute of Electrical andElectronics Engineers, New York, USA, Tech. Rep., 1997.

[2] C. Young, “USAP: A unifying dynamic distributed multichannel TDMAslot assignment protocol,” in Military Communications Conference, 1996.

MILCOM ’96, Conference Proceedings, IEEE, vol. 1, Oct. 1996, pp. 235–239.

[3] C.-M. Wu, “Dynamic frame length channel assignment in wirelessmultihop ad hoc networks,” Comput. Commun., vol. 30, pp. 3832–3840,December 2007.

[4] W. Li, J.-B. Wei, and S. Wang, “An evolutionary-dynamic TDMA slotassignment protocol for ad hoc networks,” in Wireless Communications

and Networking Conference, 2007.WCNC 2007. IEEE, Mar. 2007, pp.138–142.

[5] S. Kamruzzaman and M. Alam, “Dynamic TDMA slot reservation pro-tocol for cognitive radio ad hoc networks,” in Computer and Information

Technology (ICCIT), 2010 13th International Conference on, Dec. 2010,pp. 142–147.

[6] T. Clausen and P. J. (Editors), “Optimized link state routing proto-col(OLSR),” in IETF, Request for Comments 3626,, Oct. 2003.

[7] P. D. Holm, “UTD-diffraction coefficients for higher order wedgediffracted fields,” IEEE Trans. Antennas Propagat., vol. AP-44, no. 6,pp. 879–888, June 1996.

[8] B. Asp, G. Eriksson, and P. Holm, “Detvag-90 R© — Final Report,” De-fence Research Est., Div. of Command and Control Warfare Technology,Linköping, Sweden, Scientific Report FOA-R–97-00566-504–SE, Sep.1997.

[9] R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows. Theory,algorithms, and applications. Prentice Hall, 1993.

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