Modeling for Geo-Routing in Multi-Hop Wireless Networks - Application and Multidisciplinary Aspects of Wireless Sensor Networks - page 151

Scattering of nodes in an area of 200 × 200

200

180

160

140

120

Source node

100

80

Destination node

60

40

20

0

0 20 40 60 80 100 120 140 160 180 200

Distance in X-axis

Fig. 7.5 Location of nodes in an area of 200 × 200 m 2

Before running the UDG model, Dijkstra shortest path algorithm was used to
test network connectivity, and only connected graphs were used in measurement. If
the graph was not connected or if there is no path that exists between source and
destination then the model to produce the random nodes generation was run again
until a connected graph was found.
Figure 7.6 shows the path selection in the network as given by Dijkstra's
algorithm. The blue lines in the plot show the links/paths that can be found between
different nodes, while the colored line (black, cyan, red, and green) shows the
selected shortest path. In Dijkstra's algorithm every link is normally associated with
some cost/weight, here taken to be “1.”
Figure 7.7 shows the transmission radius of the optimal path found by Dijkstra's
algorithm, which can be seen not to follow the one discovered by the “ Localized
Greedy Algorithm. ” This point can be observed by noting the red circle whose center
is node 25 and the next forwarding hop is chosen to be 37 instead of node 40 which
was the farthest neighboring node in the transmission range of red circle.
After establishing that the network is connected, the UDG model is run on the
same topology with the transmission radius “ R ” of 61 m.
Figure 7.8 shows the optimum ideal path that has been obtained by running
“ Localized Greedy Algorithm ' which is used for UDG, where the basic functionality
has been proved, as the next forwarding neighbor is chosen which is closest to the
destination and lies inside the transmission radius “ R ” which is clearly observed
from Fig. 7.9 .