neighborhood-based route discovery protocols for mobile ad
TRANSCRIPT
Neighborhood-based Route Discovery Protocols for Mobile Ad hoc Networks
Sanaa A. Alwidian1*†
, Ismail M. Ababneh2‡
, and Muneer O. Bani Yassein3ᶲ
1Department of Computer Science and Applications
The Hashemite University, Zarqa 13115, Jordan
2Department of Computer Science
Al al-Bayt University, Mafraq 25113, Jordan
3Department of Computer Science
Jordan University of Science and Technology, Irbid 21100, Jordan
†
E-mail: [email protected] ‡
E-mail: [email protected] ᶲ E-mail: [email protected]
* Corresponding author. Tel: +962 5 390 3333 ext. 4786; E-mail: [email protected]
Abstract: Network–wide broadcasting is used extensively in mobile ad hoc networks for route
discovery and for disseminating data throughout the network. Flooding is a common approach
to performing network-wide broadcasting. Although it is a simple mechanism that can achieve
high delivery ratio, flooding consumes much of the communication bandwidth and causes
serious packet redundancy, contention and collision. In this paper, we propose new broadcast
schemes that reduce the overhead associated with flooding. In these schemes, a node selects a
subset of its neighbors for forwarding the packet being broadcast to additional nodes. The
selection process has for goal reducing the number of neighbors and maximizing the number of
nodes that they can reach (i.e., forward the packet to). By applying this novel neighborhood-
based broadcasting strategy, we have come up with routing protocols that have very low
overhead. These protocols were implemented and simulated within the GloMoSim 2.03 network
simulator. The simulation experiments show that our routing protocols can reduce the overhead
for both low and high mobility substantially, as compared with the well-known and promising
AODV routing protocol. In addition, they outperform AODV by increasing the delivery ratio and
decreasing the end-to-end delays of data packets.
Keywords: MANET, AODV, Broadcasting, Flooding, Route Discovery
1. Introduction
A Mobile Ad hoc Network (MANET) is an autonomous ad hoc network consisting of a
collection of mobile nodes that utilize wireless transmission for communication and cooperation.
MANETs are self-configured, self-organized and self-controlled, without reliance on any pre-
existing infrastructure or centralized access points. Therefore, they can be deployed anytime and
anywhere. The numerous applications of MANETs include search and rescue operations,
academic and industrial applications, and Personal Area Networks (PANs).
A node in a MANET is required to operate as a host as well as a router that can forward
packets so that they can reach nodes that do not reside within the transmission range of the
source node. The topology of MANETs is dynamic. Nodes are free to change their physical
location by moving freely in all directions (Yao Yu et al., 2009 ).
A Network Wide Broadcast (NWB) is a common operation that is used extensively in
MANETs to discover routes and to disseminate data throughout the network. Flooding is a
common operation that is used to perform NWB. Flooding refers to the process whereby a node
rebroadcasts a packet when it receives it for the first time (Rogers et al., 2005). Although
flooding is simple and can achieve delivery to a large percentage of nodes in the network, it has
been shown to be expensive and wasteful; it consumes much of the communication bandwidth,
wastes network resources and causes serious redundancy, contention and collision, which are
collectively referred to as the broadcast storm problem (Ni et al., 2002).
Many researchers have identified the disadvantages of flooding, and they suggested various
solutions in the literature (Ni et al., 2002; Tseng et al., 2003). Several of these solutions use a
fixed threshold value (Bani Yassein et al., 2009; Sasson et al., 2003). A node that receives a
broadcast packet participates in the NWB only if some local measure meets the threshold value.
Some other schemes build a virtual backbone whose task is to disseminate the broadcast packet
throughout the network (Alzoubi et al., 2002; Clausen & Jacquet, 2003) . Only backbone
members are responsible for broadcasting packets. This approach is vulnerable to transmission
losses and poor robustness, measured in terms of achieved coverage in the presence of losses.
The virtual backbone becomes disconnected when a node moves away from its neighbor or
neighbors. Also, location-based schemes were proposed. An issue with these schemes is that they
depend on node location information that is typically provided by additional equipment, such as
GPS devices (Williams & Camp, 2002).
The main goal of the protocols proposed in this paper is also to reduce the overhead resulting
from flooding. However, the strategy we propose is based on selecting a subset of neighbors that
can forward a broadcast packet to a large number of nodes. Our protocols do not require distance
measurement or exact location determination devices. A forwarding node that receives the
broadcast packet selects a subset of its neighbors based on their ability to reach additional nodes,
and only the selected neighbors will continue the broadcasting process. To begin with, the source
node selects its forwarding neighbors that will participate in the process. By applying this
strategy, we have, in particular, come up with route discovery protocols that have very low
overhead. Yet, they are able to adapt quickly to changes in the network topology, providing also
high packet delivery ratio and low end-to-end delay. The proposed protocols have been
implemented and simulated using the GloMoSim 2.03 network simulator.
The rest of this paper is organized as follows. Section 2 contains a review of previous
research work related to network-wide broadcasting. In Section 3, we present the proposed
neighborhood-based schemes. In Section 4, we discuss the simulation environment, the
simulation parameters and the various performance metrics that are measured in the simulations.
In addition, the simulation results are presented and analyzed. Simulation results for larger area
are presented in Section 5. Finally, in Section 6, we conclude this paper and provide directions
for future work.
2. Related Work
In Mobile Ad hoc Networks, NWB is used extensively for many purposes, including route
discovery, address resolution and carrying out other network layer tasks (Rogers & Abu-
Ghazaleh, 2005). For instance, reactive routing protocols such as AODV (Perkin et al., 1999)
and DSR (Johnson, 1994) benefit from the information gathered while broadcasting route request
packets in maintaining a route table at every node. However, due to the dynamic nature of
MANETs, routes break often and routing protocols are required to update the route tables
frequently, causing a large number of broadcast messages to be disseminated across the network.
In the literature, there are many schemes proposed for broadcasting in MANETs. They have been
classified into the following five categories: simple, probabilistic, counter-based, area-based and
neighbor-knowledge-based flooding (Ni et al., 2002; Williams et al, 2002).
In simple flooding (Ni et al., 2002), a node broadcasts a received packet provided that it did
not broadcast it before. Packets received previously are discarded. In this naïve flooding, a node
rebroadcasts a packet at most once. Thus, the total number of rebroadcasts is in Θ(N), where N is
the number of nodes in the MANET (Zhang & Agrawal, 2005). Although simple flooding is a
straightforward approach that aims to reach every node in the network, it consumes much of the
communication bandwidth, wastes network resources and causes serious redundancy, contention
and collision, which are referred to as the broadcast storm problem (Ni et al., 2002).
Probabilistic schemes (Haas et al., 2002) have been proposed for broadcasting/multicasting in
wired and wireless networks. In such schemes, and upon receiving a new broadcast packet, a
mobile host rebroadcasts that packet according to a specific probability, P (Sasson et al., 2003).
It is obvious that when P=1, the probability-based approaches become similar to simple
flooding. The proposed probability-based schemes are differentiated based on the method used
for determining the value of P.
Sasson et al. (2003) have proposed a probabilistic approach so as to reduce the redundant
transmission of packets encountered in simple flooding and alleviate the broadcast storm
problem. In this approach, a broadcast probability, P, is assigned in advance. Upon receiving a
packet, a node rebroadcasts that packet according to the specified probability. It is demonstrated
in the literature that the best value of P is around 0.7 (Tseng et al., 2003). Probabilistic flooding
achieves good results compared with simple flooding. It reduces transmission redundancy, while
being able to reach a large percentage of nodes. However, this approach uses the same
probability without taking the density of the node's neighborhood area into consideration. For
example, if the node is in a dense area (i.e., has many neighbors), the packet can reach the same
set of nodes many times, resulting in broadcast redundancy. The broadcast probability should be
set low in nodes located in dense areas. On the other hand, if the transmitting node has a small
number of neighbors (i.e., it is located in a sparse area), it is less likely that the broadcast packet
will reach the hosts in the transmission area of the node, thus the broadcast probability should be
high (Bani Yassein et al., 2005).
Zhang et al. (Zhang & Agrawal, 2005) proposed a dynamic probabilistic scheme that
combines both probability-based and counter-based approaches. In this approach, a counter is
maintained at each node for counting the number of times a packet has been received. The packet
counter is used as density estimator, although it does not necessarily correspond to the exact
number of neighbors. Indeed, some neighbors may have suppressed their rebroadcasts according
to their local rebroadcast probability (Zhang & Agrawal, 2005). The probability P is increased if
the value of the packet counter is low (or equivalently if the current node is located in a sparse
neighborhood), and it is decreased if the value of a packet counter is high. Compared with the
probabilistic approaches where P is fixed, this dynamic approach has achieved higher throughput
since the total number of rebroadcasts is reduced. However, the decision to rebroadcast or not is
made after some delay.
Bani-Yassin et al. (2006) have proposed a dynamic probabilistic approach, where the
broadcast probability, P, is dynamically adjusted based on network density. To adjust the
probability, short HELLO packets are used to count one-hop neighbors. If the number of
neighbors is high, this indicates that the node is in a dense area. Thus, the chance of receiving
numerous rebroadcasts of the same packet is high, and the probability P is set low to avoid
redundancy. On the other hand, if the number of neighbors is small, P is set high to increase the
chance of reaching the neighbors.
In counter-based approaches, a specific threshold value is used, and the mobile host
rebroadcasts the packet only if the number of copies received by that host is less than the
threshold value (Sasson et al. 2003). The counter-based schemes control flooding by inhibiting
the rebroadcast of a message if it has been received more than a fixed number of times. It is
assumed that additional node coverage is not significant if the threshold value is exceeded.
Counter-based schemes can achieve high delivery ratio and throughput, but they suffer from
relatively long delays.
Area-based approaches (Williams & Camp, 2002) make use of geographical information that
is provided by GPS devices or physical layer support. Such additional information is exploited in
making broadcasting decisions that control flooding and reduce redundant rebroadcasts. The
reliance on GPS and other location devices is a disadvantage of the area-based approaches.
Neighbor-knowledge-based schemes make rebroadcast decisions depending on information
on neighboring nodes obtained by exchanging HELLO messages. One example of such
approaches is flooding with self-pruning (Peng & Lu, 2000). In this scheme, a 1-hop neighbor
list is maintained at each host, and this list is added to every broadcast packet. Upon delivering a
packet to the neighbors of a node, each neighbor compares its list of neighbors with the list
recorded in the packet. A packet is rebroadcast if some neighbors of the receiving node are not
included in the list recorded in the packet. An issue with this scheme is that redundancy is not
avoided. Two or more neighbors may have a common neighbor that is not listed in the packet,
and these neighbors will broadcast the packet so as to reach this neighbor.
3. The Proposed Schemes
Our proposed broadcast schemes use a novel neighborhood-based approach for dynamically
selecting the group of nodes that forward the broadcast message. The source node selects a
subset of its 1-hop neighbors for forwarding the broadcast packet, includes their addresses in the
packet header, and broadcasts the packet. A node that receives a broadcast packet is a forwarding
node if its address is included in the packet header. Otherwise, it drops the packet. Forwarding
nodes repeat the same process carried by the source.
The two broadcast schemes we propose differ in the method used for selecting forwarding
nodes. In the first method, a number of 1-hop neighbors that have the largest number of
neighbors are selected as forwarding nodes. In the second method, a subset of 1-hop neighbors
that can reach all 2-hop neighbors forms the forwarding group. The two schemes are respectively
referred to as the Broadcast-based K-Neighbor Scheme, and the Broadcast-based Covering
Neighbors Scheme. Below, they are described within the context of on-demand route discovery.
In this case, the packet being broadcast is the Route REQuest (RREQ) packet, and the goal is
finding a path to a destination node. When a RREQ packet reaches its destination node, the
destination sends a reply to the source of the request, and it does not forward the packet.
Information on neighbors that is used in the proposed schemes is obtained via HELLO messages
that are exchanged periodically, as in AODV.
3.1. Broadcast-based K-Neighbor Scheme (BKNS) This scheme is an on-demand, broadcast-based ad hoc route discovery protocol that is designed
for MANETs. The main goal of this scheme is to control the flooding process by reducing
redundant broadcasts, which reduces the routing overhead. To facilitate the understanding of
BKNS, we present a human activity called “cooperative search for a fugitive”, and adopt its
logic in BKNS.
3.1.1 The Cooperative Search for a Fugitive
We are in a police station, S, in a city, and we want to look for a fugitive hiding in a house, D.
We assume that we do not know where the house is, and city dwellers are very cooperative. We
can start the search process at the police station by searching in K (e.g., 3) neighboring houses
that have the largest number of neighbors. From each of these houses, we are guided by their
inhabitants to up to K neighbors that they know have the largest number of neighbors. A
condition is that we never continue the search process from a house reached previously. Using
this search method, we may be unable to find the house D, although we will likely reach most
city houses. For example, D may be located in a sparse section of the city. Upon failure, we can
make a thorough (naïve flooding) search starting from the police station.
3.1.2 Implementation of BKNS
BKNS is implemented based on the cooperative search described above, where S is the source of
the broadcast packet, D is its destination, and the houses and neighboring houses represent
MANET nodes at relevant time instances.
Figure 1 represents the topology of a MANET at some time instance. In BKNS, each node
maintains a parameter called the degree of the node, where the degree of node X, degree(X), is
the number of neighbors of this node. The degree of a node is equal to the size of that node’s
neighbor table, nbrTable. This table contains an entry for each neighbor from which a HELLO
message was received within the previous time-period, called the hello-interval. In Figure 1,
degree (S) =3, degree(C) = 12, degree (B) = 5 and degree (A) = 2.
Every hello-interval, each node broadcasts a HELLO message containing its address and
degree. Upon receiving the HELLO message, a node updates its routing and neighbor tables,
such that an entry will be added in both tables for the node that sent the HELLO message, if it is
not already in the table of neighbors.
At any time, the neighbor table of node X, nbrTable(X), will contain the addresses of all X’s
1-hop neighbors and their degrees. The neighbor table entries are sorted in the decreasing order
of the degree field. When a source node S wishes to communicate with a destination D, and there
is no known route to this destination, it prepares a RREQ message and selects the first K one-hop
neighbors that have the largest degrees as forwarding nodes. Through simulation experiments,
we have tried K = 1, 2, .., 8, and have found that choosing K = 4 as the maximum number of
forwarding nodes achieves good results for the simulation parameters considered. However,
using K = 4 does not achieve good results in environments with low density. In this case, the
performance of BKNS becomes almost exactly the same as the performance of AODV, since
almost all nodes will participate in the route discovery process. That is, the value of K should
depend on node density.
We have experimented with K being a fraction of the number of neighboring nodes, N.
Extensive simulation empirical evidence shows that K= 𝑵
𝟑 performs very well for various
densities. In what follows, we limit ourselves to this variant of BKNS.
After determining its K candidate neighbors, the source node appends their addresses to the
RREQ message. Upon receiving the RREQ message, only those nodes whose addresses are
among the K-neighbors’ addresses will process the message and rebroadcast it further, as shown
in Figure 2. The scheme BKNS is shown below in Figure 3.
C
S
A B
Figure 1: A MANET topology
1. Periodically, every HELLO_INTERVAL, broadcast a HELLO message containing own
address and degree.
2. On receiving a HELLO message :
3. update nbrTable(X), so that it will contain <1-hop neighbor address, 1-hop neighbor
degree > for neighbors.
4. maintain the nbrTable(X) entries sorted in decreasing order according to the degree
field.
5. if X needs to communicate with a destination D, the following actions take place:
6. if a route exists to the destination
7. use it.
8. else
9. prepare a RREQ message, select the first K neighbors, and appends their addresses
to the RREQ message to be sent, where K= 𝑵
𝟑 .
10. Upon receiving an RREQ message, the following actions take place:
11. if the recipient node is the destination, respond to the source.
12. else
13. only those intermediate nodes whose addresses are in the RREQ message will
process the RREQ and rebroadcast it further.
14. If no response is received from D and the number of RREQ retries have been exhausted
15. source sends a flooding RREQ packet.
Figure 3: BKNS implementation algorithm
Figure 2: BKNS broadcast example
3.2. Broadcast-based Covering Neighbors Scheme (BCNS)
This is the second ad hoc, on-demand, broadcast-based scheme that we propose for controlling
flooding and reducing its overhead. In BCNS, the forwarding one-hop neighbors of a particular
node are selected such that they cover all of that node’s two-hop neighbors. For a node X, we
refer to the set of X's one-hop neighbors that cover all of its two-hop neighbors as
CoveringSet(X), and the set of the two-hop neighbors as SuperSet(X). An important aspect of
constructing CoveringSet is to keep this set as small as possible. This is because the smaller the
set, the less the overhead. Unfortunately, the task of selecting the optimal covering set with
minimum size is an NP-hard problem (Wikipedia, Accessed June, 2009). Therefore, in our
BCNS scheme, we propose a greedy algorithm as a heuristic for constructing the CoveringSet, as
illustrated in the next subsection. The idea of our BCNS scheme is clarified in Figure 4.
For a node to calculate its CoveringSet, it requires the set of its 1-hop neighbors and 2-hop
neighbors. To obtain the list of 1-hop neighbors, we depend on periodic HELLO messages that
are sent periodically (every HELLO_INTERVAL) by each of the nodes. To obtain the list of 2-
hop neighbors, a node sends a list of its own neighbors with the HELLO message it transmits
periodically. The proposed BCNS scheme has been implemented using the algorithm shown in
Figure 5a.
Figure 4: BCNS scheme concept
3.3. Heuristic for Calculating the CoveringSet
Let us refer to the set of all 1-hop neighbors of node Y as 1-hop(Y), and the set of all
neighbors of 1-hop neighbors of Y as 2-hop(Y). In addition, let SuperSet(Y) denote the set of
unique 2-hop neighbors of Y. We have: SuperSet(Y) =∪∀ 𝑖 ϵ 2−ℎ𝑜𝑝 𝑌 . The subset of 1-hop(Y)
that covers 2-hop(Y) (i.e., CoveringSet(Y)) is computed by the algorithm shown in Figure 5b.
1. Periodically, every HELLO_INTERVAL, broadcast a HELLO message containing own
address, degree and list of addresses of 1-hop neighbors.
2. On receiving a HELLO message at a node X:
3. Update nbrTable(X), so that it will contain <1-hop neighbor’s addresses, 1-hop neighbor’s
degree, 2-hop neighbor’s addresses>.
4. Sort the contents of nbrTable(X) in the descending order of the degree field.
5. If X needs to communicate with a destination D, the following actions take place:
6. If a route exists to the destination.
7. use it
8. Else
9. find a subset of 1-hop neighbors that cover all 2-hop neighbors by applying the
CoveringSet(Y) heuristic shown in Figure 6.
10. Prepare a RREQ message, and include the addresses of the nodes in the CoveringSet(X) in
the RREQ message.
11. Upon receiving an RREQ message, the following actions take place:
12. If the recipient node is the destination.
13. done.
14. Else
15. only those intermediate nodes whose addresses are in the RREQ message will process
the RREQ and rebroadcast it further.
16. If the destination is not found and the RREQ_RETRIES timer expires
17. source sends a flooding RREQ packet.
Figure 5a: BCNS implementation algorithm
4. Performance Analysis
In this research, we evaluate the performance of two routing protocols that are based on the
proposed route discovery schemes and compare them with AODV, where route discovery is
based on flooding.
We have implemented the proposed BKNS and BCNS route discovery schemes within the
GloMoSim network simulator version 2.03 (Zeng et al., 1998). This simulator already contains
an implementation of AODV. In our simulation experiments, we model a network of 10, 20, and
50 nodes. The nodes are placed randomly in a rectangular flat area. Two different areas of
dimensions equal to 600 m × 600 m and 1000 m × 1500 m were considered. The goal of using
the larger area is to experiment with longer paths (i.e., paths with more hops) and lower node
densities. The network bandwidth is 2 Mbps and the medium access control (MAC) layer
protocol is IEEE 802.11. For experiments that investigate the effect of speed, the maximum node
velocities (MaxSpeed) considered are 1, 5, 10, 20 and 50 m/s. Node velocities are distributed
uniformly over the interval [0, MaxSpeed]. Additional simulation parameters are shown in Table
1. Parameter values adopted in this work have been used in the literature (e.g., (Trung et al.,
2007)). Each simulation run lasts for 300 seconds, and runs are repeated ten times with different
random seeds.
Input: nbrTable(Y) - neighbor table for node Y.
Output: CoveringSet(Y).
1. If 2-hop(Y)==NULL
2. return (0)
3. Else
4. SuperSet(Y) =∪∀ 𝑖 ϵ 2−ℎ𝑜𝑝 𝑌 .
5. Initialize CoveringSet(Y) to .
6. For nodes in the sorted 1-hop(Y) list do:
7. check if the current node has a path to some nodes in SuperSet(Y) and add it
to CoveringSet(Y).
repeat until all nodes in the original SuperSet(Y) computed in 4 are covered by
CoveringSet(Y).
return (CoveringSet(Y)).
Figure 5b: Covering set construction heuristic
Table 1: General simulation parameters
Parameter Value
Simulator GloMoSim 2.03 Routing protocols evaluated AODV, BKNS, BCNS Simulation time 300 s Number of nodes 10, 20, and 50 nodes Simulation area 600 m 600 m or 1000 m 1500 m Transmission range 250 m Movement model Random-waypoint Traffic type Constant Bit Rate (CBR) Data payload 512 bytes/packet Packet rate 1, 2, 4, 6, and 8 packets/s Link bandwidth 2 Mbps
4.1. Performance Metrics
In comparing the performance of the protocols considered in this paper, we have used several
common performance metrics. These are the control overhead, packet delivery ratio, end-to-end
delay, and saved rebroadcasts (Sun et al., 2008).
Control overhead
The control overhead (overhead, for short) represents the ratio of the number of control packets
generated by the protocol to the number of data packets received by the destinations. It is
computed as follows:
𝑂𝑣𝑒𝑟ℎ𝑒𝑎𝑑 = 𝑁𝑜. 𝑜𝑓 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑠𝑒𝑛𝑡
𝑁𝑜. 𝑜𝑓 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑
Packet Delivery Ratio
The Packet Delivery Ratio (PDR) is the ratio of the number of data packets received by
destination nodes to those sent by the source nodes. The PDR is computed as follows:
𝑃𝑎𝑐𝑘𝑒𝑡 𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦 𝑅𝑎𝑡𝑖𝑜 = 𝑁𝑜. 𝑜𝑓 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑
𝑁𝑜. 𝑜𝑓 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑠𝑒𝑛𝑡
Average end-to-end delay
This performance parameter represents the average delay between the time when the data packet
originates at the source node and the time it reaches the destination node.
Saved Rebroadcasts
The saved rebroadcasts performance parameter represents the ratio of the number of route
request (RREQ) packets retransmitted to the total number of route request (RREQ) packets
received by the nodes (Hanashi et al., 2008). Let r be the number of RREQ packets that are
received by the nodes, and let t be the number of RREQ packets that they retransmit, the
percentage of saved rebroadcasts is computed as follows:
𝑆𝑎𝑣𝑒𝑑 𝑅𝑒𝑏𝑟𝑜𝑎𝑑𝑐𝑎𝑠𝑡𝑠 = 𝑟 − 𝑡
𝑟∗ 100%
4.2. Simulation Results and Analysis
In the following subsections, we present and analyze the simulation results obtained for various
input parameters. Unless it is specified otherwise, the results are shown for the 600 m × 600 m
area.
4.2.1. Effects of Speed and Number of Traffic Generators:
The purpose of the simulation experiments summarized in this subsection is to study the effect of
the speed of nodes on the performance of the protocols using different numbers of constant bit
rate (CBR) traffic generators. In these experiments, MaxSpeed is varied from 1m/s to 50 m/s. We
have conducted many experiments for 10, 15 and 20 CBR traffic sources, where each source
generates a traffic load of 4 packets/s. However, Due to space limitations, we will present the
simulation results for 20 sources.
In Figure 6, BKNS and BCNS generate substantially less control overhead than AODV.
Also, the overhead of AODV increases substantially with the speed of nodes. However, the
overhead of BKNS and BCNS is relatively stable. For the maximum speed of 1 m/s, BKNS and
BCNS outperform AODV by 29% and 46%, respectively. For the maximum speed of 50 m/s,
BKNS and BCNS outperform AODV by 70% and 72%, respectively. Overall, the simulation
results show that as the number of sources increases from 10 to 15 to 20, the control overhead
increases for all speed values, and both BKNS and BCNS outperform AODV significantly for all
source numbers considered. When the number of sources increases, the probability that packets
collide becomes larger, leading to higher route discovery overhead.
Figure 6: Overhead for 20 sources and a CBR of 4 packets/s
It can be seen in Figure 7 that BKNS and BCNS have slightly higher delivery ratios than
AODV for all maximum speed values. When the maximum node speed is low (1 m/s), BKNS
and BCNS outperform AODV by 1.3% and 2%, respectively. For the high maximum speed
value of 50 m/s, the performance improvements are 1.7% and 2.4%, respectively. The results of
the simulation experiments show that increasing the number of sources reduces the delivery ratio
for all protocols. Reasons for this reduction are packet collisions and dropped packets. Overall,
BCNS and BKNS slightly outperform AODV in terms of packet delivery ration for all numbers
of sources considered (10, 20 and 50 sources).
In Figure 8, BKNS and BCNS reduce the end-to-end delay by over 20% as compared with
AODV for all maximum speed values. Furthermore, as the number of traffic sources increases
from 10 to 15 to 20, the end-to-end delays increase for all protocols. As more data packets are
generated per time unit when the number of sources increases, higher queuing delays can be
expected. However, BKNS and BCNS still outperform AODV in terms of average end-to-end
delay for all source numbers considered.
Figure 9 depicts the Saved Rebroadcasts (SRB) achieved by our protocols in comparison with
AODV when the number of sources again equals 20. Similar trends were obtained for 10 and 15
sources. As the figure shows clearly, our schemes outperform AODV in terms of avoiding
redundant retransmissions of received packets. In addition, both BKNS and BCNS prove their
stability and ability to save rebroadcasts even for high speed values, whereas the performance of
AODV degrades significantly as the maximum node speed increases. On average, BKNS and
BCNS save 90% and 97% of the rebroadcasts for all maximum speed values. However, AODV
saves 60% of the rebroadcasts for the maximum speed of 1m/s, and saves only 33% for the
maximum speed of 50 m/s.
Figure 7: Packet delivery ratio for 20 sources and a CBR of 4 packets/s
4.2.2. Effects of Traffic Load and Number of Nodes:
The purpose of the simulations presented in this subsection is to investigate the influence of
varying the traffic load of the sources. For this purpose, we consider the source packet rates of 1,
2, 4, 6, and 8 packets/s, where the number of CBR generators is 10 and node speeds are
uniformly distributed over the interval [0, 50 m/s]. When the total number of nodes is 10, we say
that the network is sparse; in contrast, when this number is 50, we say that the network is dense.
We have conducted many experiments for 10, 20 and 50 nodes. However, due to space
limitations, we will not show the simulation results when the number of nodes is 20. Rather, we
show simulation results for 10 (sparse network) and 50 (dense network) nodes so as to show the
impact that the density of nodes has on overall performance.
Figure 8: End-to-end delay for 20 sources and a CBR of 4 packets/s
Figure 9: SRB for 20 sources and a CBR of 4 packets/s
4.2.2.1. Sparse Network Results
Here, the number of nodes in the network is 10, and each node generates a traffic load that varies
from 1 packet/s to 8 packets/s. In Figure 10, it is clear that our protocols outperform AODV in
terms of routing overhead for all traffic load values. This is because they control flooding by
selecting only a subset of nodes for forwarding packets. Figure 10 results show that for low
traffic load, BKNS and BCNS outperform AODV by about 40% and 56%, respectively. When
the traffic load is high, the improvement reaches 47% and 52% for BKNS and BCNS,
respectively.
Figure 11 shows that BKNS and BCNS also outperform AODV in terms of PDR for all
traffic load values. When the traffic load is low, both BKNS and BCNS outperform AODV by 7
percent. For the highest traffic load value, BKNS and BCNS outperform AODV by 12 and 14
percents, respectively.
Figure 10: Overhead for 10 nodes and MaxSpeed = 50 m/s
Figure 11: Packet delivery ratio for 10 nodes and MaxSpeed = 50 m/s
The large overhead of AODV as compared with BKNS and BCNS increases packet end-to-
end delays. The average end-to-end delay of AODV is higher than that of the proposed
protocols, as shown in Figure 12. The average end-to-end delay of AODV is larger than that of
BKNS and BCNS by about 50 to 90 percent.
Figure 13 shows that BKNS and BCNS outperform AODV in terms of SRB for all traffic
loads considered. When the traffic load is 1 packet/s, BKNS and BCNS outperform AODV by
14 and 24 percents, respectively. For 8 packets/s, BKNS and BCNS outperform AODV by 15
and 26 percents, respectively.
Figure 12: End-to-end delay for 10 nodes and MaxSpeed = 50 m/s
Figure 13: SRB for 10 nodes and MaxSpeed = 50 m/s
4.2.2.2. Dense network Results
The purpose of the simulation results presented in this subsection is to illustrate the impact of
increasing the number of nodes on the performance of the protocols under study.
It can be seen in Figure 14 that BKNS and BCNS outperform AODV in terms of control
overhead for all source traffic load values considered. For the low source traffic load of 1
packet/s, BKNS and BCNS outperform AODV by 73% and 80%, respectively. For the source
traffic load of 8 packets/s, the performance advantages of BKNS and BCNS are 62% and 84%,
respectively. Comparing Figure 14 with Figure 10, it can be seen that the control overhead
increases when the number of nodes is increased from 10 to 50. The reason is that more control
packets are expected to be sent when there are more nodes in the network.
Figure 15 displays the packet delivery ratio obtained for the three protocols. It can be noticed
in the figure that for low traffic loads (1 and 2 packets/s), our protocols and AODV have almost
similar delivery ratio values. However, for high source traffic loads (6 and 8 packets/s), our
protocols become substantially superior. This says that BKNS and BCNS are as effective as
AODV in delivering packets to destinations for low traffic load values; however, they are
substantially more effective for high traffic load values.
Figure 16 displays the average end-to-end delays for the various source CBR traffic rates
considered. The results show that as the traffic load increases, the average end-to-end delay for
all protocols increases as well. Nevertheless, our protocols are still superior to AODV, especially
for high traffic loads.
Figure 14: Overhead for 50 nodes and MaxSpeed = 50 m/s
In Figure 17, we plot SRB against the source traffic load. The figure shows that BKNS and
BCNS outperform AODV for all traffic loads. When the traffic load is low (1 packet/s), BKNS
and BCNS outperform AODV by 61% and 64%, respectively. For the highest traffic load value
considered (8 packets/s), BKNS and BCNS outperform AODV by 67% and 69%, respectively.
Figure 15: Packet delivery ratio for 50 nodes and MaxSpeed = 50 m/s
Figure 16: End-to-end delay for 50 nodes and MaxSpeed = 50 m/s
5. Simulation Results for Larger Area
In this section, the main goal of the simulation experiments is to show the behavior of our
protocols and the behavior of AODV when the simulation area becomes larger (i.e. 1000 m
1500 m) and MaxSpeed varies from the low speed of 1 m/s to the high speed of 50 m/s, where
node speeds are again uniform over the interval [0, MaxSpeed]. The Figures 18-21 show
consistency between the results of these experiments and those of the previous experiments. The
simulation parameters are set as follows:
Number of nodes: 50 nodes.
Maximum speed: 1, 5, 10, 20, 50 m/s.
Packet rate: 4 packets/s.
Number of sources = 20 CBR generators.
The other simulation parameters are set as in Table 1.
In Figure 18, it is clear that our protocols outperform AODV in terms of reducing the routing
overhead for all speed values. This is because they control flooding by selecting only a subset of
nodes for retransmitting packets. This reduces the number of control packets, which means a
reduction in the overall routing overhead. Figure 18 shows that for the lowest speed value
(MaxSpeed = 1 m/s), both BKNS and BCNS outperform AODV by 55%. When the maximum
speed of nodes is high (50 m/s), the enhancement reaches 66% and 76% for BKNS and BCNS,
respectively.
Figure 19 results show that BKNS and BCNS outperform AODV for all speed values by a
small percentage. When the maximum speed is lowest, BKNS and BCNS outperform AODV by
6 and 7.5 percents, respectively. For the highest maximum speed considered, BKNS and BCNS
outperform AODV by 4 and 8 percents, respectively.
Figure 17: SRB for 50 nodes and MaxSpeed = 50 m/s
In Figure 20, the average end-to-end delay for all packets in AODV is higher than in our
protocols. When the maximum speed of nodes is 1 m/s, AODV’s average end-to-end delay is
more than twice that of BKNS and BCNS. Whereas, when the maximum speed of nodes is 50
m/sec, the enhancement of both BKNS and BCNS over AODV is about 33%.
Figure 21 displays the SRB of the protocols and shows that BKNS and BCNS substantially
outperform AODV for all speed values. When the speed is low (MaxSpeed = 1 m/sec), BKNS
and BCNS outperform AODV by 58 and 59 percents, respectively. For the high maximum speed
of 50 m/sec, BKNS and BCNS outperform AODV by 80 and 89 percents, respectively.
Figure 18: Overhead for 50 nodes, 20 sources, and CBR = 4 packets/s
Figure 19: Packet delivery ratio for 50 nodes, 20 sources, and CBR = 4 packets/s
6. Conclusions
In this paper, we have proposed two neighborhood-based route discovery schemes for mobile ad
hoc networks. The primary aim of these schemes is reducing the overhead associated with the
route discovery process. The source node selects a subset of its one-hop neighbors for
forwarding the route request packet further, and it includes their addresses in the request packet
that it broadcasts. This process is repeated by every selected forwarding node, except the
destination node. Non-forwarding nodes drop received route request packets. Thus, the
forwarding nodes are selected dynamically in an expanding ring fashion starting with the source.
At each step, the selection process has for goal reducing the number of upcoming forwarding
nodes. Using extensive simulations, we have evaluated the proposed schemes and found that
Figure 21: SRB for 50 nodes, 20 sources, and CBR = 4 packets /second
Figure 20: Average end-to-end delay for 50 nodes, 20 sources, and CBR = 4 packets/
second
they have very low overhead, yet they can achieve substantially higher delivery ratios than
AODV when the traffic load is heavy. Even under moderate loads, they can achieve slightly
higher delivery ratios than AODV, which is a successful and well-known routing scheme for ad
hoc networks.
References
Abdalla M. Hanashi, Aamir Siddique, Irfan Awan & Mike Woodward (2008), Dynamic
Probabilistic Flooding Performance Evaluation of On-Demand Routing Protocols in MANETs.
Proc. of the 2008 International Conference on Complex, Intelligent and Software Intensive
Systems, Vol. 0. pp. 200-204.
Baolin Sun, Chao Gui, Qifei Zhang, Bing Yan, and Wei Liu (2008), A Multipath On-Demand
Routing with Path Selection Entropy for Ad Hoc Networks.Proc. of the 9th International
Conference for Young Computer Scientists, Nov. 2008, pp. 558-563.
Brad Williams &Tracy Camp (2002), Comparison of broadcasting techniques for mobile ad hoc
networks. Proc. of the 3rd ACM International Symposium on Mobile Ad Hoc Networking and
Computing (MOBIHOC 2002), ACM New York, NY, USA, pp. 194-205.
Charles E. Perkin & Elizabeth M. Royer (1999), Ad-hoc on-demand distance vector
routing.Proc. of the Second IEEE Workshop on Mobile Computing Systems and Applications
(WMCSA '99), 25-26 Feb. 1999, pp. 90-100.
David B. Johnson (1994), Routing in Ad Hoc Networks of Mobile Hosts. Proc. of the Workshop
on Mobile Computing Systems and Applications, IEEE Computer Society, Santa Cruz, CA, Dec.
1994 , pp. 158-163.
H. Trung, W. Benjapolakul& P. Duc(2007), Performance evaluation and comparison of different
ad hoc routing protocols. Computer Communications, 30(11,12), 2478-2496.
Khaled Alzoubi, Xiang-Yang Li, Yu Wang, Peng-Jun Wan & Ophir Frieder (2002) Message-
optimal connected dominating sets in mobile ad hoc networks. Proc. of the 3rd
ACM
international symposium on Mobile ad hoc networking and computing (MOBIHOC'02), 157–
164.
M. Bani Yassein, A. Al-Dubai, M. Ould Khaoua & Omar M. Al-jarrah (2009), New Adaptive
Counter Based Broadcast Using Neighborhood Information in MANETS. Proc. 2009 IEEE
International Symposium on Parallel & Distributed Processing , May 2009, pp. 1-7.
Muneer Masadeh Bani Yassein, Mohamed Ould-Khaoua & Stylianos Papanastasiou (2005),
Performance Evaluation of Flooding in MANETs in the presence of Multi-Broadcast Traffic.
Proc. of the 11th International Conference on Parallel and Distributed Systems (ICPADS05), vol.
2, July 2005 pages 505 – 509.
Muneer Masadeh Bani Yassein, Mohamed Ould-Khaoua, Lewis M. Mackenzie, Stylianos
Papanastasiou & A. Jamal (2006), Improving route discovery in on-demand routing protocols
using local topology information in MANETs.Proc. of the ACM International Workshop on
Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired
Networks, ACM Press, Terromolinos, Spain, pp. 95–99.
Muneer Masadeh Bani-Yassein, Mohamed Ould-Khaoua, Lewis M. Mackenzie & Stylianos
Papanastasiou (2006), Performance analysis of adjusted probabilistic broadcasting in mobile ad
hoc networks. International Journal of Wireless Information Networks, 13(2), 127–140.
Paul Rogers, & Nael Abu-Ghazaleh (2005), Robustness of network-wide broadcasts in
MANETs. Proc. of the 2nd
IEEE International Conference on Mobile Ad Hoc and Sensor
Systems (MASS 2005), Vol. 3, Nov. 2005, pp. 161-185.
Qi Zhang & Dharma P. Agrawal (2005), Dynamic Probabilistic Broadcasting in MANETs.
Journal of Parallel and Distributed Computing, vol. 65, pp. 220-233.
Sze-Yao Ni, Yu-Chee Tseng, Yuh-Shyan Chen, & Jang-Ping Sheu (2002), The broadcast storm
problem in a mobile ad hoc network. Wireless Networks, 8 (2), pp 153-167.
Thomas Clausen &Philippe Jacquet (2003), Optimized link state routing protocol, Internet Draft,
Internet Engineering Task Force, Oct. 2003, http://www.ietf.org/internet-drafts/draft-ietf-manet-
olsr-11.txt.
Wei Peng, and Xi-Cheng Lu (2000), On the reduction of broadcast redundancy in mobile ad hoc
networks. Proc. of the ACM International Symposium on Mobile Ad Hoc Networking &
Computing (MOBIHOC), Boston, MA, 129-130.
Wikipedia, the free encyclopedia, Online, Accessed June, 2009, "Set Cover Problem," Available
from URL http://en.wikipedia.org/wiki/Set_cover_problem
X. Zeng, R. Bagrodia & M. Gerla (1998), GloMoSim: a library for parallel simulation of large-
scale wireless networks.Proc. of the 1998 12th
Workshop on Parallel and Distributed
Simulations (PADS’98), Banff, Alb., Canada, May 26-29, 1998, pp. 154–161.
Y.-C. Tseng, S.-Y. Ni &E.-Y. Shih (2003), Adaptive approaches to relieving broadcast storm in
a wireless multihop mobile ad hoc network. IEEE Transactions on Computers, 52(5).
Yao Yu, Yu Zhou, and Sidan Du (2009), Service Discovery in Mobile Ad Hoc Networks Using
Mobility-Aware Attenuated Bloom Filters. Proc. 2009 IITA International Conference on
Services Science, Management and Engineering, (July 2009), pp. 266-269.
Yoav Sasson, David Cavin & André Schiper (2003), Probabilistic broadcast for flooding in
wireless mobile ad hoc networks. Proc. of the IEEE Wireless Communications & Networking
Conference (WCNC 2003), pp. 1124–1130.
Yu-Chee Tseng, Sze-Yao Ni & En-Yu Shih (2003), Adaptive Approaches to Relieving
Broadcast Storms in a Wireless Multihop Mobile Ad Hoc Network. IEEE Transactions on
Computers, 52(5), pp. 545-557.
Zygmunt J. Haas, Joseph Y. Halpern, and Li Li (2002), Gossip-based ad hoc routing. Proc. of the
IEEE INFOCOM, IEEE Computer Society, New York.