A Bio-Inspired Robust Routing Protocol for Mobile
Ad Hoc Networks
Kenji Leibnitz, Naoki Wakamiya, Masayuki Murata
Osaka University, Graduate School of Information Science and Technology
1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan{leibnitz,wakamiya,murata}@ist.osaka-u.ac.jp
Abstract—In this paper we discuss robustness issues of abiologically inspired routing protocol for mobile ad hoc networks
and the influence it has on the Quality of Service (QoS) in thesystem. Contrary to fixed network structures, ad hoc networks
are susceptible to frequent topology changes due to the mobility
and churn of the participating nodes. Our goal is therefore toprovide fast recovery from connectivity failures, as well as a fast
reaction to path changes due to node mobility or churn.

I. I NTRODUCTION
Mobile ad hoc networks (MANETs) have seen widespread
popularity due to their high flexibility in providing users
with network access. In contrast to conventional wired accesstechnologies, they do not require a fixed infrastructure, but by
using multi-hop communication, each node acts not only as
host, but also as router and forwards packets to a destinationnode, see Fig. 1. However, this is the reason why MANETs
behave entirely different from conventional wired networks
when it comes to considerations of quality of service (QoS)
issues. QoS can be briefly described as the set of service
requirements that need to be maintained or guaranteed by the
network while transmitting the packets of a flow from a source
node to a destination node [1]. As connections between nodes
are set up on demand and transmitted over volatile radio channels, conventional metrics such as guarantees of bandwidthor packet loss rate are harder to maintain in MANETs thanin wired networks. In fact the topology is prone to changes
when the hosts are fully mobile or enter and leave the network
at a high rate, making resilience to failures and robustnessof the connections among the key issues. Additional aspects,
such as the hidden and exposed terminal problem or security
issues caused by transmissions over radio channels impose
further constraints on the seamless and secure operation of
the network.

m1
m2
m3

Fig. 1.

Packet forwarding in MANETIn this paper we present a robust routing protocol for ad
hoc networks which is inspired by the attractor selection
mechanism found in gene expression in biological systems[2]. Our goal is to be able to cope with sudden changes of the
network topology by using a simple and self-adaptive method.
While in previous work, we considered the application of
attractor selection for choosing appropriate overlay routes [3]or the hops in ad hoc networks [4], our focus in this paperlies on the robustness issues of the proposal. Especially, we
consider an application which requires a high packet delivery
ratio and where no retransmission mechanism is applied onhigher layer. A possible example application would be video
streaming over UDP.
The rest of this paper is organized as follows. In Section II
we discuss some important issues on QoS routing in ad hoc
networks and briefly review related work. Then, we present the
proposed model and its mathematical background in Section
III. This is followed by a description of how to apply themechanism within an ad hoc routing protocol in Section
IV and we evaluate its performance by some exemplary
simulation results which are presented in Section V. Finally,
the paper is concluded with a short outlook on possible future
extensions in Section VI.
II. I SSUES ON Q O S IN MANET ROUTING
In general, there are three major routing concepts for ad
hoc networks [5]: table-driven (proactive), source-initiated(reactive), or hybrid methods. Purely proactive methods havea low latency when the connection between source and destination node is set up, but requires a high overhead by constantly updating the routing tables. Therefore, more methods in
MANETs focus on reactively setting up the connection when
it is required. Some well known examples are dynamic source
routing (DSR) [6], ad hoc on-demand distance vector (AODV)[7], and the temporally-ordered routing algorithm (TORA) [8].
In AODV, which is perhaps the most studied MANET
routing protocol, each node uses routing tables to maintain theinformation of forwarding nodes. The routing tables are set upby flooding the network with probe packets. Several variantsof DSR and AODV have been proposed to consider multiple
paths between source and destination to increase transmission
reliability [9]. A comparison of the performance of different
multi-hop MANET routing protocols can be found in [10].

Invited paper at 16th International Conference on Computer Communications and Networks (ICCCN 2007), Honolulu,
HI, August 2007.

In order to provide any QoS guarantees in MANETs,
we must make certain assumptions concerning the network
dynamics. At first, since nodes only have limited transmission
radius, a minimal node density must exist that connectivity
among the nodes can be made and the destination node
can be reached by some route from the source node. The
higher the mobility of the nodes is, the more difficult it is tomaintain connectivity. This is defined in [11] that the network
should be combinatorially stable, i.e., the changes in topology
occur sufficiently slowly to allow successful propagation of all
topology updates as necessary.
Mohapatra et al. [1] discuss important issues of QoS in
MANETs and describe methods on enhancing QoS at different
layers of the protocol stack, for example, by using improved
channel techniques on physical layer or by using IEEE 802.11
DCF mode to overcome the hidden terminal problem. However, we will focus in the following on methods operating on
network layer. There are several proposals for QoS support,
e.g. [12], [13], [5], [14], which are built on methods for
estimating and reserving bandwidth or using a combinationof several metrics [15]. In our approach we will focus on
the packet delivery ratio as main metric which provides anindication of how well the destination node can be reachedin the presence of topology changes. The idea is to use a
simple decision scheme for determining the next hop which
can quickly and autonomously overcome sudden connectivity
failures in the network.
III. ATTRACTOR S ELECTION M ODEL I NSPIRED FROMC ELL B IOLOGY
Our proposed routing method is based on each network node
applying adaptive response by attractor selection (ARAS) to
select its next hop. ARAS is a biologically inspired method
for adaptively selecting one among several candidates which
best reflects the current situation in a dynamic environment.
ARAS is originally a model for its host E. coli cells toadapt to changes in the availability of a nutrient for which
no molecular machinery is available for signal transduction
from the environment to the DNA [2].
Two key principles are used in ARAS. The first is theconcept of attractors to describe the multiple states of gene
expression. An attractor is the region to which the orbit ofa dynamic system recurrently returns regardless of the initialconditions [16]. Even if a state is perturbed by fluctuations, thesystem state will be drawn over time to an attractor. Therefore,
the second principle which is utilized in ARAS is to introduce
a small inherent noise term to the system which permits
adaptation to new states and increases the robustness of thesystem to externally introduced fluctuations. In nature, noise
always exists and no process or entity is purely deterministic.
For example, if we consider cells of the same type, the valuesof the quantities describing them will vary from cell to celland for a single cell, these values will also fluctuate over time
[16].
Basically, we can outline the attractor selection method as䏌䏑䏓䏘䏗䏙䏈䏆䏗䏒䏕

䏄䏗䏗䏕䏄䏆䏗䏒䏕䏖䏕䏄䏑䏇䏒䏐
䏚䏄䏏䏎䏈䏛䏗䏈䏕䏑䏄䏏
䏌䏑䏉䏏䏘䏈䏑䏆䏈䏆䏒䏑䏗䏌䏑䏘䏒䏘䏖䏌䏑䏓䏘䏗䎃䏖䏓䏄䏆䏈

Fig. 2.

䏇䏌䏖䏆䏕䏈䏗䏈
䏒䏘䏗䏓䏘䏗䎃䏖䏓䏄䏆䏈

General concept of ARAS

follows. Using a set of differential equations of the formdmi
= f (m1 , . . . , mM ) × α + ηii = 1, . . . , Mdt
we describe the dynamics of an M -dimensional system. Eachdifferential equation has a stochastic influence from an inherent Gaussian noise term. Additionally, we introduce anactivity α ∈ [0, 1] which changes the influences from thenoise terms. For example, if α is large, the system behavesrather deterministic and converges to attractor states definedby the structure of the differential equations. However, forsmall α the noise term dominates the behavior of the systemand essentially a random walk is performed. When the input
values (nutrients) require the system to react to the modified
environment conditions, activity α changes accordingly causing the system to search for a more suitable state, see Fig. 2.
This may involve that α causes a previously stable attractor
to become unstable.
A. Mathematical Model
Consider a set of M alternatives among which one will beselected. Let mi be the proportion of selecting i as mi andm = [m1 , . . . , mM ]T

as the vector over all mi . The dynamic behavior of each miis characterized by the stochastic differential equation systemgiven in Eqn. (1).
s(α)
dm
− d(α) m + η=
dt
1 + max(m)2 − m2

(1)

The functions s(α) and d(α) are the rate coefficients of mRNA
synthesis and degradation in the original biological model,
respectively. They are both functions of α, which represents
cell activity or vigor.
s(α) = α [β αγ + ϕ∗ ]d(α) = α(2)(3)

The parameters β and γ in Eqn. (2) are factors which influence
the mapping of activity to the output probabilities and we use
β = 50 and γ = 3 throughout this study. The constant ϕ∗ is a

special offset point which we will discuss below. For the sakeof simplicity we also define the effective growth rate ϕ.
ϕ(α) =

s(α)
d(α)

(4)

Furthermore, the vector given byTη = [η1 , . . . , ηM ]in Eqn. (1) consists of independent and identically distributed
Gaussian random variables which represent the inherent noise
found in gene expression.
When we define the functions s(α) and d(α) as given in
Eqn. (2), we obtain M equilibrium solutions m̄(k) of Eqn. (1)in the form of
h
iT
(k)
(k)
m̄(k) = m̄1 , . . . , m̄M
k = 1, . . . , M

Fig. 3.

Influence of α on output probabilities

IV. A D -H OC ROUTING WITH ATTRACTOR -S ELECTION(k)with components m̄i , see Eqn. (5).
(
ϕ(α)
(k)
i
m̄i = 1 hp
4 + ϕ(α)2 − ϕ(α)
2i=k(H value)i 6= k

(L values)(5)

So far we only discussed about the basic properties of
attractor selection model to choose the next hop among theneighboring nodes and to react to updates of the network
topology. Now, we will elaborate on how the selection method
can be implemented within a stable and robust ad hoc routing
protocol.

The interpretation of the constant term1ϕ∗ = √
2can be given as follows. For ϕ(α) = ϕ∗ the high and low
values are equal, and therefore there is no preference for any
solution in particular. This occurs, however, when α = 0, so all
mi will be nearly equal and the adaptation toward a solution
is done by random walk.
In summary, the general behavior of ARAS can be describedas follows. The system in Eqn. (1) converges to solutions
which have a single high value (H) and all other values are low(L). The dynamics of activity α influences the selected values.
When α is high, the high value H also approaches 1.0, i.e.,the selection becomes more deterministic. On the other hand,for small α, H and L become equal and the probabilities forselecting the next hop is controlled by the noise term, seeFig. 3.
B. Activity Dynamics
In the original biological model, activity performs a mapping of the availability of the nutrients to a single real value.
Thus, it controls the influence of the noise terms ηi on the
dynamic behavior of the system. If α ≈ 0, the equations are
dominated by ηi and the system essentially performs a random
walk. On the other hand, for α ≈ 1, the random influence
recedes and the system converges to an attractor. In this paper,
we use the packet delivery ratio of a flow measured at thedestination node as activity, although other types of mappings
are also feasible, e.g., number of hops, path length, available
bandwidth, or combinations of several factors [4].

A. AODV-like Reference Model
Basically, the main focus of our mechanism is to operate in a
fully self-adaptive way while reducing the amount of overhead
from flooding the network with probe packets to find newroutes. We use an AODV-like method [17] as reference modelin order to compare the efficiency of our proposed mechanism.
This reference method operates as follows. When a newconnection from a source node to an unknown destination
node is requested, it broadcasts route request packets (RREQ)
to its neighbors. On receiving a RREQ, a node responds to itwith a route reply (RREP) if it has a route to the destinationin its routing table, otherwise it rebroadcasts the RREQ to its
neighbors. A node only processes the first RREQ and discards
all subsequent RREQ of the same flooding attempt. This wholeprocess is repeated until the destination node is found whichthen replies with a RREP to the source node along the reverse
path. Upon reception of a RREP, all intermediate node store
the next hop for that destination in their routing table.
In case that one of the forwarding nodes becomes unavailable, the previous hop node responds with a route error(RERR) to the source node, which then initiates anotherflooding attempt like described above.
B. Route Setup Phase of Proposal
In our proposed method we want to limit the number ofbroadcasts and keep a simple method for selecting the next
hop node in case a route breaks. Initially, the source needs tofind the destination node, which is done in the exact way as
described above for the AODV mechanism. However, insteadof keeping routing table entries, node n now maintains a vector㫅㫆㪻㪼㩷n㩷㫉㪼㪺㪼㫀㫍㪼㫊㩷㫇㪸㪺㫂㪼㫋㪽㫆㫉㩷㪻㪼㫊㫋㫀㫅㪸㫋㫀㫆㫅㩷destn == dest㪖

㫐㪼㫊

㫅㫆

㪻㪼㫋㪼㫉㫄㫀㫅㪼㩷㫊㪼㫋㫊Nn㩷㪸㫅㪻㩷Cn

Fig. 4.

㪽㫆㫉㫎㪸㫉㪻㩷㫇㪸㪺㫂㪼㫋㩷㫋㫆㩷
dest

Decision of next hop with ARAS

㫐㪼㫊

dest

Cn㪖

㪺㪸㫃㪺㫌㫃㪸㫋㪼㩷㪸㪺㫋㫀㫍㫀㫋㫐㫋㪼㫉㫄㩷㫇㫉㫆㪺㪼㫊㫊㩷㫇㪸㪺㫂㪼㫋㩷㪸㫅㪻㩷㫌㫇㪻㪸㫋㪼㩷㪸㫃㫃㩷㫅㫆㪻㪼㫊㩷㪸㫃㫆㫅㪾㩷㫇㪸㫋㪿㩷㫎㫀㫋㪿㩷㫅㫆㫐㪼㫊

of hop probabilities to each of its neighbors
pn = [pn,1 , . . . , pn,M ]

㫅㫆

(6)

when node n has M neighboring nodes. Probing neighboring
nodes can be done by exchanging HELLO messages as inother routing protocols and the entries of neighbors which do
not reply for a given period are removed and new neighbors areadded when they appear within the transmission range. These
probabilities are maintained for each connection between asource and destination pair and set up reactively when a routeis requested. We choose the same route setup mechanism as
in AODV. When the destination node sends a route reply(RREP) message back to the source on the reverse path andan intermediate node n receives a RREP, it sets up pn withpn,k = 1 if the next hop of n toward the destination is nodek and pn,i = 0 for all i 6= k.
C. Route Maintenance Phase
Once the route has been set up, data packets are transmitted
over this route from the source to destination. Each intermediate node n chooses its next hop according to the probability
vector pn . Whenever, the destination node receives a packet,
it evaluates the current quality of this connection in terms of
the activity term α and piggybacks this information on theacknowledgment packets back to the source. All intermediate
nodes update their hop probability vector according to the
new activity. Basically, this means that the proposed method
operates at first similarly to AODV and remains this way aslong as the packet delivery ratio is near one. However, having
a node disappear along the path causes that the delivery ratio
drops and the lower it becomes, the more randomly the next
hop selection is done. Nodes which receive data packets fora connection for the first time, set up the vector pn . This
will cause that in the event of a route failure, many nodes
will be setting up these vectors. In order to eliminate theunnecessary overhead of keeping unused routing vectors withstale connections in memory, the ARAS state levels mi decay
over time at a rate δ.
dmi= δ (0 − mi )i = 1, . . . , M(7)dt
If all entries remain below a threshold for a certain periodof time, the node has not been used for this connection and thevector is removed from memory. The vector pn evolves overtime and is obtained from the system in (1) after normalization.

Cn㩷㪼㫄㫇㫋㫐㪖An = Nn

An = Cn㫇㪼㫉㪽㫆㫉㫄㩷㪘㪩㪘㪪㫆㫅㩷㫊㪼㫋㩷An㫆㪹㫋㪸㫀㫅㩷㫇㫉㫆㪹㪸㪹㫀㫃㫀㫋㫀㪼㫊㩷pn,i㩷㪽㫉㫆㫄㩷㫊㫋㪸㫋㪼㫊㩷mi㪽㫆㫉㫎㪸㫉㪻㩷㫇㪸㪺㫂㪼㫋㩷㫉㪸㫅㪻㫆㫄㫃㫐㩷㪸㪺㪺㫆㫉㪻㫀㫅㪾㩷㫋㫆㩷㫍㪼㪺㫋㫆㫉㩷pn

Fig. 5.

Flowchart of basic ARAS-based routing mechanism

The reason for using a decay instead of a simple timeout isthat this can be seamlessly integrated in the calculation of the
ARAS state values.
The performance of this method can be further improved if
the neighboring nodes are considered in two sets, a neighbor
set Nn and a candidate set Cn of nodes which lie in the
direction toward the destination, see Fig. 4. This, however,requires either some kind of position information obtained
through GPS or the information of the relative position ofa node to the destination, which can be obtained by additionalsignaling. In this work, we assume that the candidate set canbe identified within the neighbor set. This can be done forexample by considering the hop level at which a RREQ packet
is received from the source node in the initial flooding stage.
Nodes with a higher hop level than their neighbors are thus
more likely to lie closer toward the destination.
We can now summarize the basic algorithm for packet
forwarding with MARAS when node n receives a packet fordestination node dest, see Fig. 5. After receiving the packet,
the node checks if it is the intended destination. In this case, it
determines the new activity α (in our case the packet delivery
ratio) which is then propagated to all nodes in the reverse
direction along the path. If the node is not the destination, it
queries its neighboring nodes and obtains from the replies theneighbor set Nn and candidate set Cn . With this knowledge
the node n can see if the destination can be directly reached,i.e., the destination node lies in the candidate set. Otherwise,
the candidate set, if not empty, is chosen as the set on which1-q㫀㫅㪸㪺㫋㫀㫍㪼
1-q
Fig. 6.

State model of node activity

ARAS operates. However, due to the irregularity of the node
distribution, it may occur that no suitable candidate exists inthe direction to the destination. In order to avoid getting stuckin dead ends, the requirement of the next hop being nearer tothe destination is relaxed and the entire neighbor set is used
for ARAS. The ARAS equation in (1) is continually computed
over time and based on the current state values mi at a packet
arrival, the selection algorithm is performed on the current setof available neighboring nodes. Finally, the resulting ARAS
state vector is normalized to yield a probability distribution andthe next hop is selected randomly following this probability
distribution and the packet is forwarded to its next hop.
V. N UMERICAL E VALUATIONIn this section, we investigate the performance of the proposed method using ARAS for routing by simulation experiments. In order to focus on the behavior of the routing method,
we assume idealized conditions for the MAC and PHY layer,i.e., no collisions, hidden nodes, etc. As a reference we use the
AODV model as described in Section IV-A for comparison.
Nodes are randomly distributed in a window W of unit size
according to a homogeneous spatial Poisson process [18] withdensity λ. This means that on average there will be λ nodes in
W . Obviously, the relationship between λ and the transmission
range r of each node impacts the connectivity of the system.
So, if there are too few nodes, the probability of being able toreach the destination and hence obtaining a sufficient packet
delivery ratio will be very low, regardless of the consideredrouting mechanism. A brief theoretical discussion of this issue
can be found in [4]. We use r = 0.2 for all of our experiments
and each simulation runs for a duration of 10000 time steps
and is repeated 1000 times with a new random layout overwhich average values are taken.
In order to investigate the robustness and the reactive behavior of the routing method, each node has an active state ofoperation and an inactive (sleeping) state at which it is unable
to forward any incoming packets. For the sake of simplicity,
we consider discrete time steps, and the node activity is
characterized by a probability q with which it remains in
its current state at each time step, see Fig. 6. Thus, theactive and inactive phases are characterized by geometrically
distributed random variables. Note that since initially the same
flooding mechanism is used to find the destination node in bothmethods, the results for q = 1 (no activity change) would benearly equal in both cases. In the following, we will consider
the two values q = 0.9995 and q = 0.995, which correspondpacket delivery ratio

q

1
0.8
0.6
0.4
0.2
ARAS
AODV
0
0

2000

Fig. 7.

Trace of packet delivery ratio from single run

4000
6000
time steps

8000

10000

1

packet delivery ratio㪸㪺㫋㫀㫍㪼

q0.8
q = 0.9995
0.6
0.4

q = 0.995

0.2
ARAS
AODV
0

60

80
100
node density
Fig. 8.

120

Packet delivery ratio

to mean activity/inactivity durations of about 2000 and 200
time steps, respectively.
A. Efficiency of Packet Delivery
At first we compare the efficiency of the routing methodwith ARAS to that of AODV in terms of the packet delivery
ratio. We assume that no retransmissions of erroneous packets
are made, so the effective packet delivery ratio may be higher
when a retransmission mechanism on a higher layer (e.g. TCP)
is applied.
In Fig. 7 the trace of the packet delivery ratio is depicted
over time for an individual simulation run with identicalconditions for both routing methods. We can see that whennodes become inactive along the path, the delivery ratio with
AODV drops step-like over time. On the other hand, theproposed method shows a good, nearly constant behavior. The
parameters used in this run were a node density of λ = 120,
q = 0.995, and radius r = 0.2 with the same node layout andactivity behavior of each node.
Fig. 8 shows the packet delivery ratio of both methods
for q = 0.9995. We can see when q is high, both methods
have nearly equal results. For smaller q which leads to shorter
sojourn times in the activity/inactivity phases, the difference

4

14

x 10

ARAS
AODV

probing overhead

1210

ACKNOWLEDGMENTS

8
6This research work was supported by “Special Coordination
Funds for Promoting Science and Technology: Yuragi Project”
and a Grant-in-Aid for Scientific Research (A)(2) 16200003
of the Ministry of Education, Culture, Sports, Science andTechnology in Japan.

q = 0.995

4

q = 0.9995

20However, in this case the selection of the candidate set mustbe performed differently. In the future we wish to find morerobust methods on limiting the set of candidate nodes among
the neighbors which works well in mobile environments.

60

Fig. 9.

80
100
node density

120

Overhead from probe packets

between routing with ARAS and AODV becomes more evident. In both cases, the packet delivery ratio can be improved
compared to AODV.
B. Overhead from Probe Packets
When we look at the overhead due to probe packets until thepath is found, we can see in Fig. 9 that the proposed methodwith ARAS shows a significantly better performance than
the flooding based AODV. When the node density increases,
AODV can find a path faster than when there are only few
nodes. Note, however, that for AODV only those probe packets
are considered until a path is found. Therefore, in an actual
network, the total number of probe packets in AODV is muchhigher at large densities.
We can further see from Fig. 9 that the difference in nodeactivity has hardly any influence on the number of probe
packets needed for routing with the proposed method. In fact,
the absolute number of packets can be further reduced sincein this experiment the current neighborhood is evaluated atevery time step. This is a kind of a worst case assumption.
In practice, updating the neighbor set only at a certain timeinterval is sufficient enough. This would further reduce thenumber of probe packets in ARAS.
VI. C ONCLUSION
In this paper we presented a simple, yet efficient routing
mechanism which is very robust to routing failures. It is basedon the biological attractor selection mechanism and uses theconcept of attractors to select among all candidate next hop
nodes the one which is best in terms of a metric, in ourcase the packet delivery ratio. Our focus lies on robustness,but numerical results showed that the method also performs
similarly well as the AODV reference model used in thesimulations. However, when the node activity is subject tofrequent changes due to their duty cycle, churn, or mobility,
the number of required broadcasts can be reduced compared
to AODV.
The experiments in this paper dealt only with static nodes,
yet the method is also applicable when the nodes are mobile.

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