BeeAdHoc: An Energy Efficient Routing Algorithm for
Mobile Ad Hoc Networks Inspired by Bee Behavior
Horst. F. Wedde, Muddassar Farooq, Thorsten Pannenbaecker, Bjoern Vogel,
Christian Mueller, Johannes Meth and Rene Jeruschkat
Informatik III
University of Dortmund
44221, Germany
ABSTRACT
limited battery capacity of the nodes. Mobility of nodes results in continuously evolving new topologies and the routing algorithms have to adapt the routes according to these
changes. The limited battery capacity poses yet another
challenge for the routing algorithms: to distribute the packets on multiple paths in such a manner that the battery of
different nodes deplete at an equal rate, as a result, the life
time of the network could be increased [16] [8]. The metrics
for energy efficient routing are also introduced in [8] and it is
evident that an energy aware routing algorithm is expected
to degrade the traditional performance metrics of a routing
algorithm i.e. throughput and packet delay [11]. The real
dilemma in MANETs is: how to to design a routing algorithm which is not only energy efficient but also provides the
same performance as that of the existing state-of-the-art algorithms.
The routing algorithms for MANETs can be broadly classiCategories and Subject Descriptors
fied as proactive algorithms or reactive algorithms. Proactive algorithms periodically launch control packets which
C.2.1 [Network Architecture and Design]: [Distributed
collect the new network state and update the routing tanetworks, Wireless communication]; C.2.2 [Network Probles accordingly. On the other hand, reactive algorithms
tocols]: [Protocol architecture, Routing protocols]
find routes on-demand only. Reactive algorithms look more
promising from the perspective of energy consumption in
General Terms
MANETs. Each category of the above-mentioned algorithms
Algorithms, Design, Theory
is further classified based on the routing scheme as source
routing or next hop routing algorithms. In source routing
algorithms, the complete route to a destination, which conKeywords
sists of a sequence of nodes leading to the destination, is
Swarm Intelligence, Mobile Ad Hoc Networks, Self-Organization, added as a header to each data packet. In next hop routEnergy Efficient Routing
ing a packet is forwarded to a neighbor node, based on the
information in the routing table, lying on the route leading
toward the destination.
1. INTRODUCTION
DSR (Dynamic Source Routing) is a reactive source routing
Mobile Ad Hoc Networks (MANETs) is becoming an acalgorithm [7] while AODV (Ad-Hoc On-demand Distance
tive area of research [13]. All nodes in such networks take
Vector Routing) is a reactive next hop routing algorithm [9].
two roles: producer/consumer of data packet streams, and
DSDV (Dynamic Destination-Sequenced Distance-Vector) is
routers for data packets destined for the other nodes. The
a proactive next hop routing algorithm [10]. AODV and
most important challenges in MANETs are: mobility and
DSR are considered to be state-of-the-art routing algorithms
developed by the networking community for MANETs. However, all of these algorithms are not designed for energy efficient routing. Feeney reported in [4] the energy consumption
Permission to make digital or hard copies of all or part of this work for
behavior of DSR and AODV and concluded that the algopersonal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
rithms are not optimized for energy consumption. She bebear this notice and the full citation on the first page. To copy otherwise, to
lieves that metrics for energy efficient routing are completely
republish, to post on servers or to redistribute to lists, requires prior specific
different than traditional performance metrics (packet delivpermission and/or a fee.
ery ratio and packet delay). The energy aware algorithms
GECCO’05, June 25–29, 2005, Washington, DC, USA.
Copyright 2005 ACM 1-59593-010-8/05/0006 ...$5.00.
In this paper we present BeeAdHoc, a new routing algorithm
for energy efficient routing in mobile ad hoc networks. The
algorithm is inspired by the foraging principles of honey
bees. The algorithm mainly utilizes two types of agents,
scouts and foragers, for doing routing in mobile ad hoc networks. BeeAdHoc is a reactive source routing algorithm and
it consumes less energy as compared to existing state-of-theart routing algorithms because it utilizes less control packets
to do routing. The results of our extensive simulation experiments show that BeeAdHoc consumes significantly less
energy as compared to DSR, AODV, and DSDV, which
are state-of-the-art routing algorithms, without making any
compromise on traditional performance metrics (packet delivery ratio, delay and throughput).
153
reported in [16] [8] also use DSR or AODV as an underlying
route discovery and maintenance mechanism, and then use
energy as a cost metric for routing. To our knowledge, little attention has been paid in developing an energy efficient
routing algorithm from scratch with one primary objective:
optimizing energy consumption without any degradation of
the performance.
In this paper we present a new MANET routing algorithm,
BeeAdHoc, which is primarily designed for energy efficient
routing. The algorithm proposes a solution to the energyperformance dilemma. BeeAdHoc achieves similar/better
performance as that of DSR, AODV, DSDV but consumes
significantly less energy as compared to these state-of-theart algorithms. The algorithm achieves the objectives by
sending less control packets and distributing data packets
on multiple paths. Such a behavior is made possible by taking inspirations from the foraging behavior of honey bees
which is discussed in [17][15].
will describe our routing algorithm, BeeAdHoc, in Section 3.
We will first explain the complete experimental framework
in Section 4 and then discuss the results obtained from the
extensive simulations. Finally, we conclude the paper with
an outlook to our future research.
2. BEE AGENT MODEL
Our Bee Agent Model is inspired from the foraging principles of a honey bee colony. Our agent model consists of four
types of agents: packers, scouts, foragers, and swarms. In
the rest of the paper we use the term scout for scout agent,
forager for forager agent etc. until otherwise specified.
2.1 Packers
Packers mimic the task of a food-storer bee. They always
reside inside the node, receive and store the data packets
from the transport layer. Their major job is to find a forager
for their data packet and they die once they hand over it to
the foragers.
1.1 Related Work
2.2 Scouts
The first algorithm which presented a detailed scheme for
MANET routing based on ant colony principles is ARA [6].
The algorithm has its roots in ABC [14] and AntNet [2]
routing algorithms for fixed networks, which are inspired by
the pheromone laying behavior of ant colonies. The algorithm floods ants to the destinations while establishing reverse links to the source nodes of the ants. Nodes launch ant
agents in a reactive manner in order to limit the overhead
caused by them. AntHocNet has been recently proposed in
[3] which is a hybrid algorithm having both reactive and
proactive components. The algorithm tries to keep most of
the features of the original AntNet and shows promising results in the simulation tests over AODV. Termite is another
MANET routing algorithm inspired from termite behavior
[12]. In this algorithm, no special agents are needed for
updating the routing tables rather data packets are delegated this task. Each data packet follows the pheromone
for its destination and leaves the pheromone for its source.
Pheromone is a quality metric representing the goodness of
a link. The data packets are biased toward the paths that
have higher pheromone values. An exponential pheromone
decay is introduced as a mean of a negative feedback to prevent old routes from remaining in the routing tables.
Recently, Wedde, Farooq and Zhang have proposed a novel
routing algorithm for fixed networks which is inspired by foraging principles of honey bees [18]. The algorithm is simple
but delivers the same/better performance as that of AntNet
[2]. The success of BeeHive motivated us to take the foraging principles of bees as an inspiration for designing our new
routing algorithm, BeeAdHoc, for MANETs. A honey bee
colony has many features that are desirable in MANETs: efficient allocation of foraging force to multiple food sources,
different type of foragers for each commodity, foragers evaluate the quality of visited food sources and then recruit optimum number of foragers for their food source by dancing
on a dance floor inside the hive, no central control, foragers
try to optimize the energetic efficiency of nectar collection
and foragers take decisions without any global knowledge of
the environment. The principles discussed in [17] [15] form
the basis for our BeeAdHoc algorithm. We skip the details
for the sake of brevity.
The rest of the paper is organized as follows. In Section 2
we will introduce our bee agent model and on its basis we
Scouts discover new routes from their launching node to
their destination node. A scout is transmitted using the
broadcasting principle to all the neighbors of a node with
an expanding time to live timer (TTL), which controls the
number of times a scout could be re-broadcasted. Each scout
is uniquely identified with a key based on its id and source
node. Once a scout reaches at the destination then it starts
the backward journey on the same route that it followed
to the destination. A destination node sends back all of
the received scouts to ensure discovery of multiple paths.
Once a scout returns to its source node then it recruits the
foragers for its route by using the metaphor of dance (as
scout bees do in Nature). A dance is abstracted as the
number of clones that could be made of a scout (equivalent
of recruiting forager bees in Nature).
2.3 Foragers
Foragers are the main workers in our BeeAdHoc algorithm.
They receive the data packets from packers and then transport them to their destination. Each forager has a special
type: delay or lifetime. The delay foragers collect the delay
information from the network while the lifetime foragers collect the remaining battery capacity of the nodes that they
visit. The first ones try to route packets along a path that
has a minimum delay while the second ones try to route
packets in such a manner that the life time of the network
is increased.
A forager gets the complete route, in the form of a sequence
of nodes leading to a destination, from a scout or another
forager. A forager follows point-to-point mode of transmission till the destination and collects the information about
the network state depending upon its type. Once a forager reaches at the destination then it remains there until it
could be piggybacked on the network traffic from the destination node to its source node. This optimization reduces
the overhead of control packets and hence saves energy as
well. A reliable transport protocol, like TCP, acknowledges
the received packets and BeeAdHoc piggybacks in the acknowledgments the waiting foragers. The foragers also use
the metaphor of dance once they return to their source node
in a similar way as scouts do.
154
2.4 Swarms
application layers (TCP, UDP, etc.)
An unreliable transport protocol, like UDP, sends no explicit acknowledgments for the received data packets. Such
a protocol may not be able to provide an implicit return
path to a waiting forager and therefore it could never return
to its source node. Consequently, its source node might run
out of the foragers and unable to continue the communication. We solved this problem with the help of swarms. Once
the difference between the incoming foragers from a certain
node i and the outgoing foragers to the same node i reaches
above a threshold value at a node j then the node j launches
a swarm of foragers to the node i. We put one forager in the
header of the swarm while the others are put in the payload
part of the swarm. Once the swarm arrives at the node i
then the foragers are extracted from the payload part and
they are stored like they would have arrived at the node in
a normal fashion.
send
TCP, UDP::receive
create packer
hand data
waitingPackers
danceFloor::addForager
packer
in buffer
get forager
forager
send buffer
danceFloor::addScout
scout
danceFloor::getForager
add to buffer
no forager
forager availbale
create scout
hand data
set TTL, ID
timer expired,
scout not returned
set timer
entrance::letOut
receive
packing floor
3.
ARCHITECTURE OF BEEADHOC
entrance
Each node in MANET has a hive, which consists of three
parts: packing floor, entrance and dance floor. The structure of the hive is shown in Figure 1. The entrance is an interface to MAC (Medium Access Control) layer while packing floor is an interface to transport layer. All packets depart/enter the hive through the entrance. The dance floor
contains the foragers (routing information) for routing of
data packets originated at the node.
BeeHive
Figure 2: The packing floor
arrived at the destination. The information about the id of
the scout and its source node is stored in a table. If another
replica of an already received scout arrives at an entrance of
a hive then the new replica is killed here. If a forager with
a same destination as that of the scout already exists in the
dance floor then the route to the destination is given to the
scout by appending the route in the forager to its current
route.
If the current node is the destination of a forager then it is
forwarded to the packing floor else it is directly forwarded
to the MAC interface of the next hop node.
application layers (TCP, UDP, etc.)
packing floor
dance floor
entrance
3.3 The Dance Floor
BeeHive
The dance floor is the heart of the hive because it takes
important routing decisions. Once a forager returns after
its journey it recruits new foragers by dancing according to
the quality of path that it traversed. However, the quality
metric for each forager is different. As mentioned before,
a lifetime forager evaluates the quality of its route based
on the average remaining battery capacity of the nodes on
its route. A lifetime forager might allow itself to be cloned
many times (forager bees in Nature dance enthusiastically
and consequently recruit more foragers) in two scenarios:
one, the nodes on the route have enough remaining battery
capacity (good route), two, if large number of packers are
waiting for it even though its route might be having nodes
with little battery capacity. In second case, it is sensible
to send the packets through less good routes as well. On
the other hand, if none of the packers are waiting then a
forager with a very good route might not dance because its
colleagues are doing a nice job in transporting the data packets. This concept is directly borrowed from the behavior of
scout/forager bees in Nature, and it helps in regulating the
number of foragers for each route.
The dance floor also sends a matching forager to the packing
floor in response to a request from a packer. The foragers
whose life time has expired are not considered in the matching function. If multiple foragers match the criteria then a
network layers (MAC, i.e. IEEE 802.11)
Figure 1: Overview of the BeeAdHoc architecture
3.1 Packing Floor
The packing floor is an interface to higher level transport
layer like TCP or UDP. Once a data packet arrives from
the transport layer, a packer is created in the packing floor
which stores the data packet. After that the packer tries
to locate a suitable forager for the data packet from dance
floor. If it finds one then it hand overs the data packet to
the forager and dies. Otherwise, it waits for a time (may be
a returning forager is on its way toward the current hive)
and if no forager arrives within this time, then it launches a
scout which is responsible for discovering new routes to the
destination of the data packet. Figure 2 explains the series
of actions performed at a packing floor.
3.2 Entrance
The functions performed in entrance are shown in Figure 3. The entrance is an interface to lower level MAC
layer. The entrance handles all incoming/outgoing packets.
A scout received at the entrance is broadcasted further if
its time to live (TTL) timer has not expired or if it has not
155
packing floor
packing floor
letOut
packing floor::receive
forager
add/getForager
deleteScout
scout
in seenScoutList
to seenScoutList
return to packingFloor
get
add
TTL expired
not in list
destListNode
available
TTL not expired
route isn’t complete
insert address
route complete
no destListNode
return NULL
available
destListNode
available
no foragers available
return forager
destListNode not
available
foragers availble
create destNode
chose random
remove forager
decrease TTL
route completing
forager
broadcast
lifetime
elapsed
don’t arrived yet
reverse route
create convertBee
still scouting
home again
updateNextHop
scout
at destination
on way home
returning
on way
forager
forward
calculate dances
no dances left
dances left
add to foragerList
arrived at destination
MAC::send
dancetime elapsed
dancetime not elapsed
scout
updateInfo
delete forager
lifetime not elapsed
letIn
copy forager
dance floor
entrance
BeeHive
BeeHive
Figure 4: The dance floor
network layers (MAC, i.e. IEEE 802.11)
Figure 3: The entrance
to a square one provided the node density (nodes per unit
area) remain the same. The nodes move according to the
”random waypoint” model [7]: each node randomly selects
a destination point and then moves to that point with a certain randomly selected speed. Once the node arrives at the
destination point then it stops there for a certain pause time
and then again randomly selects a new destination point and
moves toward it with a new speed. The speed is selected
from a uniform distribution between a minimum speed of 1
m/s (walking speed) and the maximum speed of 20 m/s (car
speed within cities). All the nodes generate a constant bit
rate (CBR) peer-to-peer data traffic with x packets/s. The
size of a data packet is kept constant at 512 bits. We use the
same models of physical and MAC layers as the authors of
[1] did. The reported results are an average over five independent different runs to factor out any stochastic elements
in the environment or in the algorithms. The simulation
time for the algorithms is set to 1000 seconds.
forager is stochastically chosen among them. This helps in
distributing the packets over multiple paths that serves two
purposes: avoid congestion under high loads and battery of
different nodes are depleted at an equal rate. A clone of the
selected forager is sent to the packing floor and the original
forager is stored in the dance floor after reducing its dance
number. If the dance number is zero then the original forager is sent to the packing floor and its entry is deleted from
the dance floor. Using the above-mentioned principle, young
foragers, which represent latest routes and which are likely
to remain valid in future, are favored over the older ones.
If the last forager for a destination leaves a hive then the hive
does not have a route to the destination. We believe that if
a route to the destination exists then soon a forager would
be returning toward the hive and if no forager comes within
a certain time then the node has probably lost route to the
destination node. This mechanism eliminates the need for
explicitly monitoring the validity of the routes by using special hello packets and then informing other nodes through
Route Error Messages (RERR). This results in transmitting
less control packets, as a result, the algorithm has less energy expenditure. Figure 4 explains in detail the actions
taken at a dance floor.
4.
4.1 Metrics
We now define the metrics which we used in the comparison of the algorithms.
• Energy per user data. The total energy consumed,
including the energy consumed by the control packets,
to transport one kilobyte of data to its destination.
This metric is minimum when the same number of
bytes could be delivered at the destinations in less hops
and with small number of control packets. We used the
model presented in [5] to estimate the send/receive energy of broadcasting or point-to-point mode of transmitting packets. This metric is also referred to as energy expenditure in rest of the paper.
SIMULATION FRAMEWORK
We evaluated the performance of our algorithm BeeAdHoc using mobility enhancements made to ns-2 simulator
by the authors of [1]. The authors also evaluated the performance of different state-of-the-art algorithms like AODV,
DSR, DSDV and TORA in their work. Our test scenarios
are derived from the base scenario used in [1]. We use the
same implementations of DSR, AODV and DSDV, which are
distributed with the ns-2 simulator to factor out any implementation related error in the algorithms.
The scenario consists of 50 nodes which are moving in a
rectangular area of 2400 × 800m2 . The rectangular area ensures that longer paths exist between the nodes as compared
• Success rate. The ratio between the number of packets successfully received by the application layer of a
destination node and the number of packets originated
at the application layer of each node for that destination. This parameter is also referred to as packet
delivery ratio in rest of the paper.
156
looked into this problem and it appeared that the most important contributor for higher delay is the packet salvaging
technique used in DSR. Once a node finds out that the next
hop in the header is down then it looks at its routing table and if it finds a route to the destination in it then it
replaces the remaining part of the header with this route.
However, by the time, the packet arrives at the next node,
the route again needs to be repaired. Consequently, a packet
keeps on taking hops until it arrives at the destination. The
basic behavior of MANETs could then be summarized as
follows: if a node finds that the next hop to a destination is
no more available then it should not try to repair the route
with the old information in its routing table because there is
a high probability that this old route would be no more valid
as well. Therefore, BeeAdHoc simply deletes the packet if
it finds that the next hop is down. It is clear from Figure
5(a) that this simple approach results, at maximum, in loss
of about 0.3% in packet delivery ratio.
The simplicity of BeeAdHoc, which results because of its
simpler architecture and using smaller number of control
packets (see Table 2, please note that all of the 50 nodes are
transmitting packets), pays off once we look at its energy
expenditure (see Figure 5(d)) in transporting the packets
from their source to their destination. BeeAdHoc employs a
simple bee behavior to monitor the validity of the routes by
controlling the number of foragers, their dance and age parameter, rather than explicitly using hello/RERR messages.
This results in the smallest amount of energy expenditure
for BeeAdHoc (see Figure 5(d)).
Finally, Table 3 shows the average remaining battery ca-
• Delay. The difference between the time once the packet
is received by the application layer of a destination
node and the time when the packet was originated at
the application layer of a source node. This definition
takes care of the time that a packet has to wait at
the source node while the route to its destination is to
be found (reactive wait time). We always report the
100th percentile of the delays distribution because it
provides an insight on the spread of the delays which is
an important criterion for quality of service (QoS) applications, in which all packets should arrive at the destination within an acceptable variance from the mean.
• Throughput. If y number of bits are delivered within
t time at a node then the throughput at the node could
be defined as yt . This definition assigns a higher throughput value to an algorithm that delivers the same number of y bits in a smaller time. This definition of
throughput implicitly strikes a good balance between
the number of packets delivered at a node and their
delays.
• Network life. The average remaining battery capacity
of the nodes. A higher value means less depletion of
the batteries and hence is a desirable property of any
routing algorithm.
4.2 Node Mobility Behavior
The purpose of the experiments was to study the behavior
of the algorithms by varying the speed of the nodes. Higher
speeds reduce the stability of a network topology, as a result,
an algorithm has to adapt itself with the changes in topologies. In these experiments the packet rate was 10 packets/s
(CBR source) and the pause time was 60 seconds. Figure 5
shows the effect of mobility on different metrics. The packet
delivery ratio (see Figure 5(a)) reduces with the increasing
speed but BeeAdHoc is able to deliver approximately the
same number of packets as that of DSR, the best performing
algorithm. However, BeeAdHoc has a significantly smaller
delay (see Figure 5(b)). Consequently, BeeAdHoc is able to
maintain higher throughput (see Figure 5(c)) as compared
to all other algorithms. Please remember that our definition
of throughput favors one algorithm over the others if it is
able to deliver the same number of packets but with smaller
delays.
We investigated the problem of higher packet delays of DSR
by looking at 80th, 90th, 95th and 100th percentile of the
delays distribution (see Table 1). It is evident from Table
80th percentile
90th percentile
95th percentile
100th percentile
BeeAdHoc
105.64
153.84
191.36
280.97
DSR
167.73
278.58
396.29
969.39
AODV
156.85
220.52
269.31
387.96
Node Mobility
1-5 m/s
1-10 m/s
1-15 m/s
1-20 m/s
BeeAdHoc
83095
93895
99240
103119
DSR
122313
224335
310119
396885
AODV
592454
716235
836058
731279
DSDV
165454
211220
240234
253000
Table 2: Total number of control packets sent
pacity (%) of the nodes in the network at the end of the
above-mentioned simulations. BeeAdHoc has higher remaining battery capacity under all of the circumstances. The battery level of BeeAdHoc is better because it tries to spread the
data packets over different routes rather than always sending them on the best routes. Different routes could be established to the destination nodes at higher node speeds, as a
result, data packets are routed through different nodes and
this explains the increasing network life behavior of BeeAdHoc, with an increase in the speed of the nodes. AODV and
DSR utilize significantly larger number of control packets at
higher nodes speed (see Table 2) therefore the batteries of
the nodes are almost completely depleted.
DSDV
117.89
176.01
223.76
372.55
4.3 Congestion Control Behavior
Table 1: Different Percentile of the delays distribution for node speed of 1-5 m/s
The purpose of these set of experiments was to investigate the congestion control behavior of the algorithms. The
node’s speed was chosen in the range 1-20 m/s and the
packet send rate was gradually increased from 10 packets/s
to 100 packets/s and the other parameters remain the same
as in the previous experiments. All algorithms are able to
cope up with an increased load (see Figure 6(a)), however,
the performance of AODV is the worst. The throughput of
1 that BeeAdHoc is able to deliver majority of the packets with in an acceptable deviation from mean while DSR
delivers about 5% of packets with quite large delays, as a
result, 100th percentile of the delays distribution is significantly larger than that of the other algorithms. We further
157
Beehive
DSR
AODV
DSDV
Beehive
100
DSDV
1000
1−10
1−15
node velocity(m/s)
1−5
1−20
(a) Packet delivery ratio
Beehive
DSR
AODV
1−10
1−15
node velocity(m/s)
274.6
1001.4
0
1−5
256.6
296.1
946.4
296.1
318.3
231.2
196.8
90
339.1
200
273
280.9
400
92
372.5
969.3
600
1041.8
800
387.9
delay(ms)
97.83
98.35
95.93
98.29
97.98
98.56
95.94
98.31
98.07
98.88
96.36
98.56
98.37
99.13
96.72
96
98.85
success rate(%)
AODV
1200
98
94
DSR
1−20
(b) Packet delay
DSDV
Beehive
DSR
AODV
DSDV
10
0
8
7.72
5.96
9.09
5.07
7.9
6
8.53
5.12
7.51
5.98
7.87
5.23
2
6.22
7.97
4
7.56
6
5.4
energy per userdata(mJ/kB)
480.848
433.45
479.83
326.558
100
456.923
407.626
352.888
482.93
458.988
407.07
386.728
489.4
489.114
200
448.396
300
500.076
400
433.604
throughput(kbit/s)
500
0
1−5
1−10
1−15
node velocity(m/s)
1−5
1−20
(c) Throughput
1−10
1−15
node velocity(m/s)
1−20
(d) Energy Expenditure
Figure 5: Effect of varying the speed of the nodes
Node Mobility
1-5 m/s
1-10 m/s
1-15 m/s
1-20 m/s
BeeAdHoc
6.2
5.5
6.1
11.4
DSR
1.4
1.3
1.0
1.1
AODV
1.8
2.4
1.6
1.4
DSDV
2.4
2.6
2.6
3.3
4.4 Varying Pause Time
Another challenge in MANETS is to study the effect of
pause time on the performance of the algorithms. Smaller
pause time means that the nodes will stop for smaller times
and as a result, the routes will never be stable. In these experiments we kept the maximum speed at 20 m/s and packet
rate at 100 packets/s and all other parameters remained the
same as in the previous experiments. We show the results
for 1, 30 and 60 seconds of pause time, because according to
the authors of [1] any increase in pause time after 60 seconds
just improves the performance of the algorithms. Figure 7
shows the results for these experiments. Packet delivery ratio for BeeAdHoc, DSR and DSDV remains approximately
the same with a decrease in pause time, however, the AODV
delivers about 2% less packets at a pause time of 1 sec as
compared to a pause time of 60 seconds. The significant
degradation in packet delay is experienced by all of the algorithms with a decrease in the pause time; DSR being the
worst effected. All of the algorithms show an increase in the
range of 300% to 600% in the delay. One could see from
Figure 7(b) that AODV has the smallest delay but then it
delivers less packets as well (see Figure 7(a)). The routes
are unstable at small pause times, as a result, packets, both
on the average and in worst case scenarios, take more time
to reach their destination. Consequently, throughput metric
suffers as well (see Figure 7(c)) but BeeAdHoc manages to
maintain higher throughput in all of the scenarios.
Energy expenditure of BeeAdHoc, as expected, is the lowest
Table 3: Effect of varying speed on Network life
the algorithms increased with an increase in the send rate
of packets (see Figure 6(c)). No significant queue delays
were experienced even with a sending rate of 100 packets/s,
as a result, y bits took approximately the same time t as
in the previous experiments. According to our definition
of throughput (see Section 4.1), it should approximately remain the same even though ten time more packets are delivered at the destinations. BeeAdHoc has the highest throughput value, because it provides a good compromise between
packet delivery ratio and packet delay.
Figure 6(b) shows that the packet delay decreases with an
increase in the load, which at first, appears to be counter intuitive. But this could be easily explained by looking at the
definition of packet delay (see Section 4.1). The route discovery time, which is an overhead of discovering new routes,
is now shared by more data packets. The energy expenditure
of the algorithms also decrease with an increase in the send
rate of packets because more data packets are delivered at
their destination with the same number of control packets.
However, BeeAdHoc has the lowest energy expenditure.
158
Beehive
DSR
AODV
DSDV
Beehive
DSR
AODV
DSDV
100
1000
30
60
send rate(packet/s)
(a) Packet delivery ratio
Beehive
DSR
AODV
DSDV
Beehive
DSR
243.9
269.3
246.9
832.2
900.1
267.7
252.7
259.1
AODV
DSDV
0
8
7.49
5.74
8.53
5.07
7.5
5.74
8.67
5.11
7.43
5.75
8.57
5.11
2
5.96
9.09
4
7.72
6
5.07
energy per userdata(mJ/kB)
521.062
457.252
353.974
520.944
517.955
454.324
347.834
520.202
507.474
459.644
345.434
502.522
480.848
433.45
479.83
326.558
throughput(kbit/s)
400
100
100
10
500
200
30
60
send rate(packet/s)
(b) Packet delay
600
300
881
10
100
190.1
0
10
186.5
90
196.8
200
274.6
400
197.9
92
600
1001.4
98.48
96.31
98.71
98.57
98.41
96.3
98.7
98.52
98.22
96.43
98.55
98.48
97.83
98.35
95.93
94
256.6
delay(ms)
800
96
98.29
success rate(%)
98
0
10
30
60
send rate(packet/s)
10
100
(c) Throughput
30
60
send rate(packet/s)
100
(d) Energy expenditure
Figure 6: Effect of varying packet send rates
among all algorithms in all of the scenarios. The reason is
twofold: one, smaller number of control packets sent, and
two, delivering more packets with smaller number of hops.
5.
Contact information. The email addresses of the authors
are (wedde, farooq)@ls3.cs.uni-dortmund.de,
thorsten.pannenbaecker@uni-dortmund.de,
bjoernvogel@gmx.de, christian.mueller@uni-dortmund.de,
J.Meth@LANdata.de and jeruschkat@web.de respectively.
CONCLUSION
In this paper we presented a new routing algorithm for
MANET which is inspired by the honey bee behavior. The
algorithm is simple and mainly needs two types of messages
for routing: scouts, which on-demand discover new routes
to the destinations and forgers, which transport data packets and simultaneously evaluate the quality of the discovered routes. This simplicity results in substantially smaller
number of control packets sent, as a result, the algorithm is
energy efficient. We have verified through extensive simulations, which represent a wide spectrum of network conditions, that BeeAdHoc delivers the same/better performance
as that of the state-of-the-art algorithms but at a significantly smaller energy expenditure.
We have already started our research in implementing BeeAdHoc in the network stack of the Linux kernel. We will then
test and evaluate the algorithm on a mobile network of laptops. This effort is part of our Natural Engineering approach
in which we want to develop engineering solutions for the
real world problems under the resources of constraints (cost,
labor, time etc.). We are also modifying BeeAdHoc so that it
could scale to at least 1000 nodes MANETs. These enhancements will be the subject of our forthcoming publications.
6. REFERENCES
[1] Josh Broch, David A. Maltz, David B. Johnson,
Yih-Chun Hu, and Jorjeta Jetcheva. A performance
comparison of multi-hop wireless ad hoc network
routing protocols. In Proceedings of Fourth
ACM/IEEE Conference on Mobile Computing and
Networking (MobiCom), pages 85–97, 1998.
[2] G. Di Caro and M. Dorigo. AntNet: Distributed
stigmergetic control for communication networks.
Journal of Artificial Intelligence, 9:317–365, December
1998.
[3] Gianni Di Caro, Frederick Ducatelle, and Luca Maria
Gambardella. AntHocNet: an ant-based hybrid
routing algorithm for mobile ad hoc networks. In
Proceedings of Parallel Problem Solving from Nature
(PPSN) VIII, LNCS 3242. Springer-Verlag, 2004.
[4] Laura Marie Feeney. An energy consumption model
for performance analysis of routing protocols for
mobile ad hoc networks. Mobile Networks and
Applications, 6(3):239–249, 2001.
[5] Laura Marie Feeney and Martin Nilsson. Investigating
the energy consumption of a wireless network interface
159
Beehive
DSR
AODV
DSDV
Beehive
DSR
AODV
DSDV
100
2500
delay(ms)
2000
30
pause time(s)
1
60
(a) Packet delivery ratio
Beehive
DSR
AODV
DSDV
Beehive
DSR
246
197
250
832
991
AODV
DSDV
0
8
7
5.79
8.53
5.07
5.65
7.64
6.73
5.17
2
5.49
3
6.07
4
6.28
5
7.33
6
5.7
energy per userdata(mJ/kB)
526.864
467.96
512.652
353.974
488.56
450.432
495.14
366.368
331.3
349.184
300
366.204
400
327.65
throughput(kbit/s)
60
9
500
100
30
pause time(s)
(b) Packet delay
600
200
331
0
1
306
90
277
500
1320
1636
1000
2379
98.39
96.65
98.67
98.57
98.16
98.74
1500
948
92
96.35
98.63
98.2
94.48
94
98.89
96
98.71
success rate(%)
98
1
0
1
30
pause time(s)
1
60
(c) Throughput
30
pause time(s)
60
(d) Energy expenditure
Figure 7: Effect of varying pause time
[6]
[7]
[8]
[9]
[10]
[11]
[12]
in an ad hoc networking environment. In Proceedings
of IEEE INFOCOM, 2001.
M. Genes, U.Sorges, and I.Bouazizi. ARA – the
ant-colony based routing algorithm for manets. In
Proceedings of ICPP Workshop on Ad Hoc Networks,
2002.
David B Johnson and David A Maltz. Dynamic source
routing in ad hoc wireless networks. In Imielinski and
Korth, editors, Mobile Computing, pages 153–181.
Kluwer Academic Publishers, 1996.
Christine E. Jones, Krishna M. Sivalingam, Prathima
Agrawal, and Jyh-Cheng Chen. A survey of energy
efficient network protocols for wireless networks.
Wireless Networks, 7(4):343–358, 2001.
C. Perkins and E. Royer. Ad-hoc on-demand distance
vector routing. In Proceedings of Second IEEE
Workshop on Mobile Computing Systems and
Applications, 1999.
Charles Perkins and Pravin Bhagwat. Highly dynamic
destination-sequenced distance-vector routing (DSDV)
for mobile computers. In Proceedings of ACM
SIGCOMM’94 Conference on Communications
Architectures, Protocols and Applications, pages
234–244, 1994.
L.L. Peterson and B.S. Davie. Computer Networks A
Systems Approach. Morgan Kaufmann Publishers,
2000.
Martin Roth and Stephen Wicker. Termite: Emergent
[13]
[14]
[15]
[16]
[17]
[18]
160
ad-hoc networking. In Proceedings of the Second
Mediterranean Workshop on Ad-Hoc Networks, 2003.
E. Royer and C. Toh. A review of current routing
protocols for ad-hoc mobile wireless networks. IEEE
Personal Communications, 1999.
R. Schoonderwoerd, O.E. Holland, J.L. Bruten, and
L.J.M. Rothkrantz. Ant-based load balancing in
telecommunications networks. Adaptive Behavior,
5(2):169–207, 1996.
T.D. Seeley. The Wisdom of the Hive. Harvard
University Press, London, 1995.
Suresh Singh, Mike Woo, and C. S. Raghavendra.
Power-aware routing in mobile ad hoc networks. In
Proceedings of Fourth ACM/IEEE Conference on
Mobile Computing and Networking (MobiCom), pages
181–190, 1998.
K. von Frisch. The Dance Language and Orientation
of Bees. Harvard University Press, Cambridge, 1967.
H.F. Wedde, M. Farooq, and Y. Zhang. Beehive: An
efficient fault-tolerant routing algorithm inspired by
honey bee behavior. In Proceedings of ANTS
Workshop, LNCS 3172, pages 83–94. Springer Verlag,
Sept 2004.