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.
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