Global State Routing: A New Routing Scheme for Ad-hoc Wireless Networks
Tsu-Wei Chen and Mario Gerla
Computer Science Department
University of California, Los Angeles
f
tsuwei,gerla
g
@cs.ucla.edu
Abstract
In an ad-hoc environment with no wired communication infras-
tructure, it is necessary that mobile hosts operate as routers in
order to maintain the information about connectivity. However,
with the presence of high mobility and low signal/interference
ratio (SIR), traditional routing schemes for wired networks are
not appropriate, as they either lack the ability to quickly re-
flect the changing topology, or may cause excessive overhead,
which degrades network performance. Considering these restric-
tions, we propose a new scheme especially designed for rout-
ing in an ad-hoc wireless environments. We call this scheme
“Global State Routing” (GSR), where nodes exchange vectors of
link states among their neighbors during routing information ex-
change. Based on the link state vectors, nodes maintain a global
knowledge of the network topology and optimize their routing de-
cisions locally. The performance of the algorithm, studied in this
paper through a series of simulations, reveals that this scheme
provides a better solution than existing approaches in a truly mo-
bile, ad-hoc environment.
I. Introduction
In an ad hoc wireless network where wired infrastructures are
not feasible, mobility and bandwidth are two key elements pre-
senting research challenges. Mobility causes the life time of a
connection between two hosts to vary greatly; and limited band-
width makes a network to be easily congested by control sig-
nalling. Routing schemes developed for wired networks seldom
consider restrictions of this type. Instead, they assume that the
network is mostly stable and the overhead for routing messages
is negligible. Considering the difference between wireless and
wireline network, we believe it is necessary to develop a wireless
routing protocol that reacts quickly to changes of network topol-
ogy but consumes only a reasonable amount of bandwidth for the
control traffic.
In this paper, we propose a new routing scheme for ad-hoc
wireless networks. It is MAC (medium access control) layer ef-
ficient because the overhead of control message is kept low. It
still provides accurate solutions for finding optimal paths. The
rest of this paper is organized as follow. In section II, we survey
the existing wireless routing protocols. Then we propose a new
routing scheme in section III. Section IV presents the complexity
analysis of this scheme and compares it with others. To verify
the effectiveness, we simulate a mobile environment and the re-
port the performance results in section V. Lastly, we conclude our
work and propose issues for future research in section VI.
II. Previous Work
Several routing schemes based on DBF (distributed Bellman-
Ford) [1] or LS (link state) [2] have been proposed in the past
for both wireline or wireless networks. The advantages of DBF
are its simplicity and computation efficiency due to its distributed
characteristic. However, the slow convergence and the tendency
of creating routing loops make DBF not suitable for a wireless
network with high mobility. Though approaches were proposed
in [3, 4, 5, 6] to solve looping problem, none of them overcome
the problem of slow convergence.
In part for these reasons, LS is preferred and used in many
modern networks like Internet [7] or ATM [8]. In LS, a global
network topology is maintained in all routers, and any link change
will be updated by flooding immediately. As a result, the time re-
quired for a router to converge to the new topology is shorter than
in DBF. Since global topology is maintained, preventing routing
loop is also easier. Unfortunately, as LS relies on flooding to dis-
seminate the update information, excessive control overhead may
be generated, especially when the mobility is high. In addition,
because of the large amount of small packets, flooding is also in-
efficient for the radio MAC layer.
A third routing scheme proposed recently for ad-hoc wireless
network is called “on-demand” routing. Namely, the route be-
tween two nodes is computed when there is a need. Most on-
demand routing are based on a query/response approach [9, 10,
11]. Since flooding is used for query packet dissemination and
route maintenance, on-demandroutingtends to become inefficient
when traffic load and mobility increase.
III. The Global State Routing
Our goal is to design a routing scheme that is MAC efficient
in ad-hoc wireless radio networks. That is, the control packet
size should be able to achieve optimized MAC throughput, and
the number of control packet should be controllable. We prefer
to maintain the knowledge of full network topology as in link
state routing, but wish to avoid the inefficient flooding mecha-
nism. Therefore, we develop our scheme based on LS, which has
the advantage of routing accuracy, and we adopt the dissemina-
tion method used in DBF, which has the advantage of no flooding.
This scheme is called “Global State Routing” (GSR), and more
detailed description is given below.
A. Network Model
The ad-hoc wireless network is modeled as an undirected graph
G
=(
V; E
)
,where
V
is a set of
j
V
j
nodes and
E
is a set of
j
E
j
undirected links connecting nodes in
V
. Each node has a unique
identifier and represents a mobile host with a wireless communi-
cation device with transmission range
R
, and an infinity storage
space. Nodes may move around and change their speed and di-
rection independently. An undirected link
(
i; j
)
connecting two
nodes
i
and
j
is formed when the distance between
i
and
j
be-
come less than or equal to
R
.Link
(
i; j
)
is removed from
E
when
node
i
and
j
move apart, and out of their transmission ranges.
For each node
i
, one list and three tables are maintained. They
are: a neighbor list
A
i
, a topology table
TT
i
, a next hop table
NEXT
i
and a distance table
D
i
.
A
i
is defined as a set of nodes
that are adjacent to node
i
. Each destination
j
has an entry in ta-
ble
TT
i
which contains two parts:
TT
i
:LS
(
j
)
and
TT
i
:SEQ
(
j
)
.
TT
i
:LS
(
j
)
denotes the link state information reported by node
j
,
and
TT
i
:SEQ
(
j
)
denotes the timestamp indicating the time node
j
has generated this link state information. Similar, for every des-
tination
j
, NEXT
i
(
j
)
denotes the next hop to forward packets des-
tined to
j
on the shortest path, while
D
i
(
j
)
denotes the distances
of the shortest path from
i
to
j
.
Additionally, a weight function, weight:
E
!
Z
+
0
,isusedto
compute the distance of a link. Since min-hop shortest path is the
only objective in this paper, this weight function simply returns
1 if two nodes have direct connection, otherwise, it returns
1
.
This weight function may also be replaced with other functions for
routing with different metrics. For instance, a bandwidth function
can be used to realize a QoS routing.
B. Algorithm
The details of GSR protocol are listed in Fig. 1. At the begin-
ning, each node
i
starts with an empty neighbor list
A
i
,andan
empty topology table
TT
i
. After node
i
initializes its local vari-
ables with proper values as described in procedure NodeInit(i),it
learns about its neighbors by examining the sender field of each
packet in its inbound queue, PktQueue. That is, assuming that all
nodes can be heard by
i
are
i
’s neighbors, node
i
adds all routing
packet senders to its neighbor list,
A
i
.
Node
i
then invokes PktProcess(i) to process the received rout-
ing messages, which contain link state information broadcasted
by it neighbors. PktProcess(i) makes sure that only the most up
to date link state information is used to compute the best route by
comparing the embedded sequence number,
pk t:S E Q
(
j
)
, with
the ones stored in node
i
’s local storage, for each destination
j
. If any entry in the incoming message has a newer sequence
number regarding destination
j
,
TT
i
:LS
(
j
)
will be replaced by
pk t:LS
(
j
)
,and
TT
i
:SEQ
(
j
)
will be replaced by
pk t:S E Q
(
j
)
.
After the routing messages are examined, node
i
rebuilds the
routing table based on the newly computed topology table and
then broadcasts the new information to its neighbors. Such pro-
cess is periodically repeated.
C. Information Dissemination
The key difference between our GSR and traditional LS is the
way routing information is disseminated. In LS, link state packets
are generated and flooded into the network whenever a node de-
tects topology changes. GSR doesn’t flood the link state packets.
Instead, nodes in GSR maintain the link state table based on the
up to date information received from neighboringnodes, and peri-
odically exchange it with their local neighbors only. Information
is disseminated as the link state with larger sequence numbers re-
places the one with smaller sequence numbers. In this respect, it is
similar to DBF (or more precisely, the DSDV [4]) where the value
of distances is replaced according to the time stamp of sequence
number.
D. Shortest Path Computation
FindSP(i) creates a shortest path tree rooted at
i
. In principle,
any existing shortest path algorithm can be used to create the tree.
In this paper, however, the procedure listed in Fig. 1 is based on
the Dijkstra’s algorithm [12] with modifications so that the next
hop table (NEXT
i
) and the distance tables(
D
i
) are computed in
parallel with the tree reconstruction.
At node
i
, FindSP(i) initiates with
P
=
f
i
g
, then it iterates
until
P
=
V
. In each iteration, it searches for a node
j
such
that node
j
minimizes the value of
(
D
i
(
k
)+
weig ht
(
k; j
))
,for
all
j
and
k
,where
j
2
V
,
P
,
k
2
A
i
and weight(k,j)
6
=
1
.
Once node
j
is found,
P
is augmented with
j
,
D
(
j
)
is assigned
to
D
(
k
)+
weig ht
(
k; j
)
and NEXT
i
(
j
)
is assigned to
next
i
(
k
)
.
That is, as the shortest path from
i
to
j
has to go through
k
,the
successor for
i
to
j
is the same successor for
i
to
k
.
IV. Complexity
In this section, we analyze the complexity of the GSR scheme
and compare it with two other routing schemes: DBF and LS. The
complexity is studied under five aspects:
1. ComputationComplexity (CC): the number of computation
steps for a node to perform a routing computation after an
update message is received;
2. Memory Complexity (MC): the memory space required to
store the routing information;
3. Data Complexity (DC): the aggregate size of control pack-
ets exchanged by a node in each time slot;
4. Packet Complexity (PC): the average number of routing
packets exchanged by a node in each time slot;
5. Convergence Time (CT): the times requires to detect a link
change.
Protocol CC MC DC PC CT
GSR
O
(
N
2
)
O
(
N
d
)
O
(
j
E
j
)
=I O
(1)
O
(
D
I
)
LS
O
(
N
2
)
O
(
N
2
)
O
(
j
E
j
)
=I O
(
N
)
O
(
D
)
DBF
O
(
N
)
O
(
N
)
O
(
N
)
=I O
(1)
O
(
N
I
)
Table 1. Complexity Comparison
Table 1 shows the results of our comparison. In the table,
N
denotes the number of nodes in network(
j
V
j
),
D
denotes the max-
imum hop distance, the diameter, in the network,
d
and
I
denote
the degree of node connectivity and the routing update interval,
respectively.
GSR and LS have same memory complexity and computation
complexity as both maintain the topology for the whole network
and use Dijkstra’s algorithm to compute shortest path routes. Di-
jkstra’s algorithm requires typically
O
(
N
2
)
steps to compute the
shortest paths from one source to all destinations, although it is
possible to reduce it to
O
(
N log N
)
[12].
O
(
N
2
)
memory space is
required to store the network topology represented by a connec-
tion matrix. As for DBF, it has complexity of
O
(
N
)
for comput-
ing and memory,as it only keeps the distance information for each
destination, and computes shortest paths in a distributed fashion.
For the data complexity, in GSR each node broadcasts infor-
mation for
N
d
links on average, and the complexity is divided
by
I
, the update interval. LS, on the other hand, has similar ac-
cumulated data size for each link update, but its update interval
I
may become extremely small when mobility increase. This issue,
to be addressed shortly, was verified through simulation.
In addition, as LS transmits one short packet for each link up-
date, its packet complexity can be as high as
O
(
N
)
when the mo-
bility is high. On the other hand, both GSR and DBF transmit a
fixed number of update tables using longer packets to optimize the
MAC throughput.
Lastly, the convergence time for GSR is also superior than that
for DBF. In fact, if shorter update interval is used, GSR can con-
vergeasfastasLS.
V. Simulation
Unlike in [5, 4, 9, 10], where wireless network is simulated by
static network with higher link failure rate, we used a truly mobile
environmentin our simulator to determine the connectivityamong
mobile hosts. The simulation is programmed in C++ to simulate
an environment of 500
500 unit
2
. Arbitrary numbers of nodes,
representing the mobile hosts, move independently on their own
trajectories within this virtual space. The maximum moving speed
and the number of nodes are given at run time.
Additional assumptions used in our simulations includes:
(1) no node failure during simulation;
(2) node number is always constant in the run time of simulation;
(3) a time slotted system;
(4) radio transmission range is fixed at
R
, which is specified at the
beginning of the simulation;
(5) two nodes can hear each other if they are within the transmis-
sion range, that is, open space channel model is used.
Three routing schemes: DBF, LS and GSR are used exclusively
in the simulation. The DBF and LS are based on the schemes
described in [13]. Both DBF and GSR can be executed with a
routing update interval (
I
) specified at run time. By default,
I
is set to 3 (one update per three time slots), while in LS, nodes
flood link state packets wheneverthey detect changes in their local
connectivities. Also, the number of nodes in our simulation is set
to 60.
A. Performance Measurements
Two metrics are used to evaluate the routing performances:
routing inaccuracy and control overhead. Using them, we exam-
ine the impact to the performances of different mobility, update
interval and radio transmission range.
A.1. Routing Inaccuracy Routing inaccuracy is checked by
comparing the next hop table of each node with the tables gener-
ated by an off-line algorithm. This off-line algorithm has knowl-
edges of the exact network topology to compute the optimal so-
lution for each node at each time slot. For a destination which is
still far away, an incorrect value in the next hop table is less crit-
ical than nodes that are close by. Considering this, we define the
routing inaccuracy for node
i
,
A
i
,as:
A
i
=
1
D
X
next
i
(
k
)
6
=
next
i
M
(
k
)
(
D
,
hop
i
(
k
)+1)
then the overall routing inaccuracy is computed by averaging
A
i
,
for all
i
2
N
,where
N
,
next
i
()
,
hop
i
()
,
D
are defined in section
III, and
next
i
M
()
is the next hop table computed by the off-line
algorithm.
A.2. Control Overhead The control overhead is evaluated
by examining the average number of routing control packets ex-
changed on each link. The reason for using the number of con-
trol packets instead of the total control bits exchanged is due to
the characteristic of radio devices and MAC layer protocol. It is
known that a radio device spends considerable time to switch from
receive to transmit mode. This typically exceeds the time used for
sending a small packet. If spread spectrum is used, the acquisition
time may become even more significant.
For LS, we account for each link state packet that is generated
by a node either because it detects a topology change, or it re-
ceives one from its neighbors and forwardsit by flooding. Each of
packet requires a transition for radio device from receiving mode
to transmitting mode. For DBF type algorithm, a routing table up-
date is counted as one packet. This is under the assumption that
the routing table can be transmitted in a fixed number of consecu-
tive MAC layer frames (without transmit/receive switching.)
B. Simulation Results
In addition to routing accuracy and control overhead, we also
examine the impact to performance due to changes in mobility,
update interval and radio transmission range. These results are
summarized below.
B.1. Routing Inaccuracy Fig. 2 shows the inaccuracy of dif-
ferent routing schemes at different node speeds. LS performs
best at all speed ranges, since it reacts the fastest to the topol-
ogy changes. GSR performs less accurately than LS since it up-
dates the routing information only every three time slots. How-
ever, GSR still performs better than DBF.
B.2. Control Overhead As shown in Fig. 3, both DBF and
GSR algorithms have a flat distribution of packet overhead, which
means the overhead of both cases remains constant regardless of
mobility. This is because nodes in both schemes exchange routing
information periodically with only their adjacent neighbors. On
the other hand, with LS schemes, the overhead is much worse
than DBF and GSR which means more packets are generated. The
figures also show that as degree of mobility becomes higher, the
overhead for LS increases. This validates that LS is not suitable
for high mobility environment.
B.3. Mobility Impact As Fig. 3 shows, the control overhead
for LS increases rapidly as nodes move at higher speeds. An un-
manageable flood of packets overwhelms the radio channel and
dominates packet queue in each node. On the other hand, mobil-
ity has no effect on control overhead for DBF and GSR. This is
reasonable because in LS, routing updates are event driven: a node
sends a link state packet into the network whenever changes in its
neighborhood are detected. And a large amount of these link state
packets will then be generated due to the flooding mechanism.
The impact of mobility to routing inaccuracy is, however, in-
dependent of the impact to control overhead. Overall, higher mo-
bility causes higher inaccuracy for all three schemes. LS performs
the best in every mobility value, as indicated by Fig. 2. LS sus-
tains inaccuracy equal to or lower than 15% even at a node speed
of 160 units per time slot, while DBF provides poorly acceptable
routing solutions. Our GSR performs between DBF and LS; in
low speed range, GSR is as accurate as LS; while in high speed
range, GSR becomes worse but is still better than DBF.
B.4. Update Interval Update interval also plays an important
role for the routing overhead and inaccuracy. As we showed ear-
lier, LS achieves higher routing accuracy because update pack-
ets are sent out immediately whenever a node detects topology
change. Thus the delay is bounded by the minimum time res-
olution used by the system. Fig. 4 shows that GSR inaccuracy
is degraded or improved by adjusting the routing interval up or
down. The same holds for DBF as shown in Fig. 5, except that
the improvement is not very significant as the accuracy is already
poor even in the low mobility conditions. As expected, we note
that more improvements can be observed when mobility is high.
B.5. Radio Transmission Range The range of radio trans-
mission determines the degree of node connectivity. As shown in
Fig. 6, the larger the transmission range the larger the connectivity
degree and the larger the control packet size for GSR and LS.
Fig. 7 shows the decrease in routing error rate as transmission
range increases. Larger transmission range means more nodes can
be reached in one hop without requiring routing decision. How-
ever, as indicated in [14], the spatial reuse is less efficient when
transmission range is large. It is interesting to note that, the worst
case doesn’t happens when
R
=80
, which is the smallest range
in our simulation. Instead, it happens at about
R
= 150
,regard-
less of mobility. It is obvious that as transmission range increases,
the hop distance between any two nodes also decreases, so less
routing error may be formed. On the other hand, as transmission
range decreases, the graph will become disjoint. To the extreme,
when the transmission range equals zero, no two nodes can talk to
each other, and the next hop entries will becomes all empty.
VI. Conclusion
In this paper, we introduce a new routing scheme, the Global
State Routing, to provide an efficient routing solution for wireless,
mobile networks. The routing accuracy of GSR is comparable to
an ideal LS scheme and thus superior to the traditional DBF, al-
though it doesn’t require individual link state broadcasting which
may cause serious consumption of wireless bandwidth. As a re-
sult, GSR is more desirable for a mobile environment where mo-
bility is high and bandwidth is relatively low.
Our future plan includes the evaluation of MAC layer impact
to the routing efficiency. This requires enhancements to our simu-
lators with different MAC layers such as MACA, MACA/PR and
Cluster/TDMA [14]. QoS routing for multimedia support in wire-
less environment is also considered to be embedded in GSR since
it provides higher routing accuracy without sacrificing bandwidth.
Besides simulation, implementing GSR in an IP wireless testbed
is also in progress.
References
[1] D. Bertsekas and R. Gallager, Routing in Data Networks,
chapter 5, Prentice Hall, second edition, 1987.
[2] J.M. McQuillan et al., “The new routing algorithm for the
ARPANET,” IEEE Transaction of Communications, vol. 28,
no. 5, pp. 711,719, May 1980.
[3] S. Murthy and J.J. Garcia-Luna-Aceves, “A routing protocol
for packet radio networks,” in Proc. IEEE Mobicom,Nov.
1995, pp. 86–95.
[4] C.E. Perkins and P. Bhagwat, “Highly dynamic destination-
sequenced distance-vector routing (DSDV) for mobile com-
puters,” in ACM SIGCOMM’94, 1994, pp. 234–244.
[5] S. Murthy and J.J. Garcia-Luna-Aceves, “A routing protocol
for packet radio networks,” in Proc. IEEE Mobicom,Nov.
1995, pp. 86–95.
[6] C. Hedrick, “Routing Information Protocol,” in IETF RFC
1058, 1988.
[7] J. Moy, “OSPF Version 2,” in IETF RFC 1583, 1994.
[8] The ATM Forum, “Private Network-Network Interface
Specification v1.0,” 1996.
[9] M. S. Corson and A. Ephremides, “A destributed routing al-
gorithm for mobile wireless networks,” ACM-Baltzer Jour-
nal of Wireless Networks, vol. 1, pp. 61–81, Jan. 1995.
[10] V. D. Park and M. S. Corson, “A highly adaptive destributed
routing algorithm for mobile wireless networks,” IEEE In-
focom, 1997.
[11] C.-K. Toh, “A novel distributed routing protocol to support
ad-hoc mobile computing,” in IEEE IPCCC, 1996.
[12] R. Sedgewick, Weighted Graphs, chapter 31, Addision-
Wesley, 1983.
[13] Andrew S. Tanenbaum, Computer Networks, Third Edition,
Prentice Hall, 1996.
[14] M. Gerla and J. T. Tsai, “Multicluster, mobile, multimedia
radio network,” ACM-Baltzer Journal of Wireless Networks,
vol. 1, no. 3, pp. 255–265, 1995.
proc
Node
(
i
)
NodeInit
(
i
);
while
TRUE
do
if
PktQueue
6
=
!! packet received
foreach
pkt
2
PktQueue
do
A
i
A
i
[f
pkt
:
source
g
PktProcess
(
i;
pkt
)
od
;
fi
FindSP
(
i
);
if
(
clock
()
mod
UpdateInterval
)=0
RoutingUpdate
(
i
);
fi
CheckNeighbors
(
i
);
TT
i
:LS
(
i
)
A
i
;
od
.
proc
NodeInit
(
i
)
foreach
j
2
V
do
A
i
(
j
)
;
D
i
(
j
)
1
;
NEXT
i
(
j
)
,
1;
SEQ
i
(
j
)
,
1;
od
A
i
A
i
[f
x
j
link
(
i; x
)
exists
g
;
TT
i
:LS
(
i
)
A
i
;
D
i
(
i
)
0;
NEXT
i
(
i
)
i
;
t
i
0;
SEQ
i
(
i
)
t
i
;
.
proc
RoutingUpdate
(
i
)
t
i
t
i
+1;
TT
i
:SEQ
(
i
)
t
i
;
TT
i
:LS
(
i
)
;
foreach
x
2
A
i
do
TT
i
:LS
(
i
)
TT
i
:LS
(
i
)
[f
x
g
;
od
message
:
TT
f
i; T T
i
g
;
message
:
id
i
;
broadcast
(
j;
message
)
to all
j
2
A
i
;
.
proc
FindSP
(
i
)
Dijkstra’s shortest-path algorithm
P
f
i
g
;
D
i
(
i
)
0;
foreach
x
2f
j
j
(
j
2
V
)
^
(
j
6
=
i
)
g
do
if
x
2
TT
i
:LS
(
i
)
then
D
i
(
x
)
weight
(
i; x
);
NEXT
i
(
k
)
k
;
else
D
i
(
x
)
1
;
NEXT
i
(
k
)
,
1;
fi
od
while
P
6
=
V
do
foreach
k
2
V
,
P; l
2
P
do
Find
(
l; k
)
such that
weight
(
l; k
) = min
f
D
i
(
l
)+
weight
(
l; k
)
g
;
od
P
P
[f
k
g
;
D
i
(
k
)
D
i
(
l
)+
weight
(
l; k
);
NEXT
i
(
k
)
NEXT
i
(
l
);
od
.
proc
PktProcess
(
i;
pkt
)
source
pkt
:
source
;
TT
i
:LS
(
j
)
TT
i
:LS
(
j
)
[f
source
g
;
foreach
j
2
V
do
if
(
j
6
=
i
)
^
(
pkt:S E Q
(
j
)
>TT
i
:SEQ
(
j
))
then begin
TT
i
:SEQ
(
j
)
pkt:S E Q
(
i
);
TT
i
:LS
(
j
)
pkt:LS
(
i
);
end
fi
od
.
proc
CheckNeighbors
(
i
)
foreach
j
2
A
i
do
if
weig ht
(
i; j
)=
1
A
i
=
A
i
,f
j
g
;
fi
od
.
Fig. 1. The GSR Protocol
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 20 40 60 80 100 120 140 160
Inaccuracy
Mobility (unit/timeslot)
60 nodes
DBF
LS
GSR
Fig. 2. Inaccuracy: 60 nodes
0
10
20
30
40
50
60
70
80
0 20 40 60 80 100 120 140 160
Control Overhead (packet/node/timeslot)
Mobility (unit/timeslot)
r0 nodes
DBF
LS
GSR
Fig. 3. Overhead: 60 nodes
0
0.1
0.2
0.3
0.4
0.5
0.6
0 20 40 60 80 100 120 140 160
Control Overhead (packet/node/timeslot)
Mobility (unit/timeslot)
GSR
I= 1 slot
I= 2slots
I= 3 slots
I= 4 slots
Fig. 4. Inaccuracy at different update intervals:GSR
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 20 40 60 80 100 120 140 160
Control Overhead (packet/node/timeslot)
Mobility (unit/timeslot)
DBF
I= 1 slot
I= 2 slots
I= 3 slots
I= 4 slots
Fig. 5. Inaccuracy at different update intervals:DBF
0
10
20
30
40
50
60
50 100 150 200 250 300 350 400 450 500
Degree of Connectivity
TX Range
0 unit/timeslot
25 units/timeslot
50 units/timeslot
50 units/timeslot
50 units/timeslot
Fig. 6. Connectivity vs. TX. range
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
50 100 150 200 250 300 350 400 450 500
Routing Inaccuracy
TX Range
0 unit/timeslot
25 units/timeslot
50 units/timeslot
75 units/timeslot
100 units/timeslot
Fig. 7. Inaccuracy vs. TX. range
