Nature-inspired routing protocols for MANETs

http://www.iaeme.com/IJCET/index.asp 118 editor@iaeme.com
International Journal of Computer Engineering & Technology (IJCET)
Volume 7, Issue 3, May-June 2016, pp. 118–126, Article ID: IJCET_07_03_011
Available online at
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=7&IType=3
Journal Impact Factor (2016): 9.3590 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6367 and ISSN Online: 0976–6375
© IAEME Publication
STUDY AND PERFORMANCE
EVALUATION OF ANTHOCNET AND
BEEHOCNET NATURE INSPIRED
MULTIHOP ROUTING PROTOCOLS FOR
EFFECTIVE ROUTING IN MANET
Neha Bhatia
Ph.D. Scholar, Department of Electronics and Communication Engineering
SunRise University, Alwar (Rajasthan), India
Dr. Anil Kumar Sharma
Professor and Principal
Institute of Engineering and Technology, Alwar-301030 (Rajasthan), India
ABSTRACT
There are multiple algorithms and protocols which are present in MANeT
but no one is perfect for each situation because of the presence of high
topology changes and dynamicity of number of nodes. That’s the reason of
opting biological algorithm like Ant based or Bee based algorithms which are
swarm intelligence based nature inspired algorithms which finds out the best
route as per the real time status of network. In communications network
research, there is currently an increasing interest for the paradigm of
autonomic computing. The idea is that networks are becoming more and more
complex and that it is desirable that they can self-organize and self-configure,
adapting to new situations in terms of traffic, services, network connectivity,
etc. To support this new paradigm, future network algorithms should be
robust, work in a distributed way, be able to observe changes in the network,
and adapt to them. Nature’s self-organizing systems like insect societies show
precisely these desirable properties. Making use of a number of relatively
simple biological agents (e.g., the ants) a variety of different organized
behaviours are generated at the system-level from the local interactions
among the agents and with the environment. The robustness and effectiveness
of such collective behaviours with respect to variations of environment
conditions are key-aspects of their biological success. This kind of systems is
often referred to with the term Swarm Intelligence. Swarm systems have
recently become a source of inspiration for the design of distributed and
adaptive algorithms, and in particular of routing algorithms.

Study and Performance Evaluation of Anthocnet and Beehocnet Nature Inspired Multihop
Routing Protocols for Effective Routing in MANeT
Key words: AntHocNet, Bee Based Algorithm, Biological Protocols, Nature
Inspired Topology, Cryptography Swarm Intelligence.
Cite this Article: Neha Bhatia and Dr. Anil Kumar Sharma, Study and
Performance Evaluation of Anthocnet and Beehocnet Nature Inspired
Multihop Routing Protocols for Effective Routing in MANeT, International
Journal of Computer Engineering and Technology, 7(3), 2016, pp. 118–126.
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=7&IType=3
1. INTRODUCTION
MANeT Ad-Hoc networks are defined as networks formed by users or devices
wishing to communicate, without the necessity for the help or existence of any
infrastructure or previously established relationship between the potential network
members. Ad-hoc communication can take place in different scenarios and is
independent of any specific device, wireless transmission technology, network or
protocol. Some examples of the possible uses of ad hoc networking include sensor
networks, search and rescue operations, vehicle communication networks, and
possible military applications, etc.
In particular, we expect that ad hoc networks will be formed in situations where
no infrastructure is available. As for the mode of operation, they are basically peer-to-
peer multi-hop wireless networks where information packets are transmitted in a store
and forward manner from a source to an arbitrary destination, via intermediate nodes.
The network topology changes dynamically and in an unpredictable manner since the
nodes can move freely. Therefore, out-dated topology information must be updated or
removed. Since there is no centralized entity to keep the topology up-to-date, a
distributed algorithm is required. Finding a route to a destination requires exchange of
control information among the nodes. Thus, the amount of update traffic can be quite
high when the number of highly mobile nodes is large. Thus, the highly dynamic
nature of ad hoc networks motivates the study of routing protocols, which aim at
achieving routing stability.
Again the wireless communication media has a limited bandwidth, which is
susceptive to various interferences that can lead to establishment of useless routes,
low throughput and other problems. Some of the protocols assume that the
communication links are symmetric. Although this assumption is not always valid, it
is usually made because routing in asymmetric networks is a relatively hard task. In
certain cases, it is possible to find routes that could avoid asymmetric links, since it is
quite likely that these links imminently fail. The issue of symmetric and asymmetric
links is one among the several challenges encountered in ad hoc networks. Mobile
hosts are powered by battery. Hence, energy efficient routing protocols are required to
minimize power consumption.
2. SWARM INTELLIGENCE
In communications network research, there is currently an increasing interest for the
paradigm of autonomic computing. The idea is that networks are becoming more and
more complex and that it is desirable that they can self-organize and self-configure,
adapting to new situations in terms of traffic, services, network connectivity, etc. To
support this new paradigm, future network algorithms should be robust, work in a
distributed way, be able to observe changes in the network, and adapt to them.
Nature’s self-organizing systems like insect societies show precisely these desirable
properties. Making use of a number of relatively simple biological agents (e.g., the

Neha Bhatia and Dr. Anil Kumar Sharma
ants) a variety of different organized behaviours is generated at the system-level from
the local interactions among the agents and with the environment. The robustness and
effectiveness of such collective behaviours with respect to variations of environment
conditions are key-aspects of their biological success. This kind of systems is often
referred to with the term Swarm Intelligence. Swarm systems have recently become a
source of inspiration for the design of distributed and adaptive algorithms, and in
particular of routing algorithms. Routing is the task of directing data flows from
sources to destinations maximizing network performance. It is at the core of all
network activities. Several successful routing algorithms have been proposed taking
inspiration from ant colony behaviour and the related framework of Ant Colony
Optimization (ACO). Examples of ACO routing algorithms are AntNet. One type of
networks where the need for autonomic control is intrinsically necessary is Mobile Ad
Hoc Network (MANETs). These are networks in which all nodes are mobile and
communicate with each other via wireless connections. Nodes can join or leave at any
time. There is no fixed infrastructure. All nodes are equal and there is no centralized
control or overview. There are no designated routers: nodes serve as routers for each
other, and data packets are forwarded from node to node in a multi-hop fashion. Ant
Based Algorithms are nature inspired adaptive routing algorithm for mobile ad hoc
networks (MANETs) inspired by ideas from Ant Colony Optimization (ACO). In
common MANET terminology, Ant Algorithms are defined as hybrid algorithm, as it
combines both reactive and proactive routing strategies. Specifically, the algorithm is
reactive in the sense that it does not try to maintain up-to-date routing information
between all the nodes in the network, but instead concentrates its efforts on the pairs
of nodes between which communication sessions are taking place. It is proactive in
the sense that for those ongoing communication sessions, it continuously tries to
maintain and improve existing routing information. While the ant-based path
sampling is the typical mode of operation of ACO routing algorithms, the pheromone
diffusion process is in its working more similar to Bellman-Ford routing algorithms.
AntHocNet combines both processes in order to obtain an information gathering
process that is at the same time efficient, adaptive and robust. The way path sampling
and information bootstrapping are combined here is very different from other
combinations of these approaches to learning that exist in the reinforcement learning
literature and is specifically targeted at working highly dynamic non-stationary
environments.
In nature several animals tend to live in large swarms like insect colonies, bird
flocks or fish schools. Many social insects like ants, bees, termites, or wasps live in
colonies or hives. They exhibit an astonishingly well-developed social behaviour and
are able to self-organize, even in the absence of a central leader like a queen. Honey
bees communicate locations of food sources by the language of dance that is
understood by all nearby honey bees. On the other hand, many insects use a form of
indirect communication called stigmergy. Stigmergy works by leaving traces in the
environment that can be understood by other insects. Termites use stigmergy to build
complex nests by simple rules. A termite constructing a nest deposits material like a
mud ball and invests it with pheromones, a chemical that can be smelled by other
termites. The smell of pheromones encourages other termites to deposit their material
close to freshly deposited pheromones. This way, a group of termites can manage to
synchronize so that they all work on the same spot.
Swarm Intelligence (SI) is an Artificial Intelligence technique based on the study
of collective behaviour in decentralized, self-organized systems. Swarm intelligence
is “The emergent collective intelligence of groups of simple agents”. It gives rise to

complex and often intelligent behaviour through simple, unsupervised interactions
between a total numbers of autonomous swarm members. Usually there is no
centralized control structure dictating how the individual agents should behave, but
local interactions between such agents often lead to the emergence of a global
behaviour.
Swarm is considered as biological insects like ants, bees, wasps, fish etc. The quick
coordinated flight of a group of birds with very little visual communication and the
concerted effort of an ant colony in gathering food, building nests etc. are some of the
vivid examples of emergence in natural world. SI has found immense applicability in
fields like Robotics, Artificial Intelligence, process optimization, telecommunications,
routing, software testing, networking etc.
Our objective is to Study and Performance Evaluation of AntHocNet and Our
Introduced Extension BeeHocNet Nature Inspired Multihop Routing Protocols for
Effective Routing in MANETs using NS-2 which is emerging and open source
software to test and simulate various network protocols.
3. RELATED WORK
D. Karthikeyan and M. Dharmalingam [4] in his paper, Ant based Intelligent Routing
Protocol for MANET propose an energy efficient routing algorithm for MANETs
based on ACO for minimizing energy consumption of the nodes and prolong the life
of the overall communication system. The performance of the proposed algorithm is
simulated on the network tool NS2 and is also compared with existing algorithm’s
performance.
Mohammad Arif1 and Khalid Imam Rahmani2 [5] in his paper, Adaptive ARA
(AARA) for MANETs proposed a more efficient and modified version of ant colony
based routing algorithm for routing in mobile ad hoc networks.
Praveenkumar G Hoolimath, Kiran M, and G Ram Mohana Reddy [6] in his paper,
Optimized TERMITE: A Bio-inspired Routing Algorithm for MANET’s implemented
in ns-2 and its performance is compared with traditional routing protocol AODV.
Opt-Termite’s performance has been promising.
Vivekanand Jha, Kritika Khetarpal and Meghna Sharma in his paper, A Survey of
Nature Inspired Routing Algorithms for MANETs provide a comprehensive overview
of the nature inspired routing algorithms for mobile adhoc networks and compare
them and also bring out their main merits and demerits.
Eslam Al Maghayreh, Salam Abu Al-Haija, Faisal Alkhateeb and Shadi
Aljawarneh [8] in his paper, Bees Ants Based Routing Algorithm propose a novel
routing algorithm called Bees Ants algorithm. This algorithm is a combination of Ant
colony based Routing Algorithm (ARA) and Beehive based Routing Algorithm. The
proposed routing algorithm depends on splitting the network into two parts; one is a
fixed network and the other is a mobile ad hoc network (MANET), then applying the
Ant colony based Routing Algorithm on the mobile part and the Beehive based
Routing Algorithm on the fixed one. After comparing the proposed algorithm with the
ARA algorithm, it shows promising results in terms of propagation delay, queue
delay, and number of hops.
L.J.G. Villalba D.R. Can˜as A.L.S. Orozco [9] in his paper , Bio-inspired routing
protocol for mobile ad hoc networks design for the protocol lies in a heuristic, based
on swarm intelligence, which takes into account the limited resources and highly
dynamic environment, as well as the restriction on the exchange of routing
information. So, the key aspects of the proposed protocol are the disjoint-link and

disjoint-node routes, separation between the regular pheromone and the virtual
pheromone in the diffusion process and the exploration of routes, taking into
consideration the number of hops in the best routes which the authors have previously
found out.
M.M.Goswami, R.V. Dharaskar and V.M.Thakare[10] in his paper, Fuzzy Ant
Colony Based Routing Protocol For Mobile Ad Hoc Network proposes a novel
approach called fuzzy ant colony based routing protocol (FACO) using fuzzy logic
and swarm intelligence to select optimal path by considering optimization of multiple
objectives while retaining the advantages of swarm based intelligence algorithm.
Simulation results show that the proposed protocol is superior over existing swarm
intelligence based routing protocols for routing in MANET.
Sanaz Asadinia,Marjan kuchaki Rafsanjani and Arsham Borumand Saeid [11] in
his paper , A Novel Routing Algorithm Based-on Ant Colony in Mobile Ad hoc
Networks propose a new routing algorithm for MANETs, which combines the idea of
ant colony optimization with zone based hierarchical link state (ZHLS) protocol. The
algorithm is based on ants jump from one zone to the next zones which contains of the
proactive routing within a zone and reactive routing between the zones. The proposed
algorithm will improved the performance of the network such as delay and packet
delivery ratio than traditional routing algorithms.
4. PROPOSED SYSTEM
The protocol is based on swarm intelligence and especially on the ant colony based
Meta heuristic. The routing algorithm consists of three phases. In the first one, Route
Discovery Phase, new paths are discovered. The creation of new routes requires the
use of a forward ant (FANT), which establishes the pheromone track to the source
node, and a backward ant (BANT), which establishes the track to the destination
node. FANTs are broadcasted by the sender to all its neighbours. Each FANT has a
unique sequence number to avoid duplicates. A node receiving a FANT for the first
time creates a record (destination address, next hop, pheromone value) in its routing
table. The node interprets the source address of the FANT as destination address, the
address of the previous node as next hop, and computes the pheromone value
depending on the number of hops the FANT needed to reach the node. Then the node
relays the FANT to its neighbours. When the FANT reaches destination, it is
processed in a special way. The destination node extracts the information and then
destroys the FANT. A BANT is created and sent towards the source node. In that
way, the path is established and data packets can be sent. BeeHocNet, the proposed
algorithm is an on-demand multi-path routing algorithm for mobile ad hoc networks
inspired from the foraging principles of honey bees. BeeHocNet works with four
types of agents: packers, scouts foragers and swarms. The packers locate a forager
and hand over the data packet to the discovered forager. Scouts discover new routes
from the launching node to the destination node through broadcasting principle and an
expanding time to live (TTL) timer. Foragers, the main workers of BeeHocNet,
receive the data packets from the packers and transport them to the destination.
Transportation of foragers back to the source node, in case of unreliable transport
protocol, is the key role of swarms.
5. SIMULATION AND RESULTS
To evaluate the effectiveness of proposed scheme, we simulate the scheme using
network simulator version 2 (NS2). In simulation, we used the number of nodes as 50
and carried out simulation 5 times on every scenario at different time intervals and get

the results. We implement the random way point movement model for simulation in
which nodes start at 0 position with simulation time 25 seconds, PDR values varies
from 0 to 1, delay values 0 to 500 ms, throughput 0 to 850 out of 1000 and energy
consumption from 0 to 800 if 1000 parts applied with the two cases i.e. case including
only implementing the ant based algorithm on NS2 network and after implementing
extended version of Ant based i.e. by implementing bee based extension (BARA). We
take one ant based algorithm i.e. Ant Routing Algorithm (ARA) by taking
consideration of different parameters like throughput, packet delivery ratio, energy
consumption and delay time taken by average packet to reach the destination. Here
are the simulation results. Which clearly shows the improvement after implementing
the extended version of bee based algorithm on ant based algorithme
Figure.1: Graph Shows the Decrease in Delay Values with Ant Based vs Bee and Ant Based
Algorithm
Figure.2: Graph Shows the Increase in Packet Delivery Ratio Values with Ant Based v/s Bee
and Ant Based Algorithm

Figure 3: Graph Shows the Increase in Throughput Values with Ant Based v/s Bee and Ant
Based Algorithm
Figure 4: Graph Showing Decrease in Energy Consumption with Ant Based v/s Bee and Ant
Based Algorithm
6. CONCLUSION
Nature inspired algorithms has a lots of capabilities because they are emerged with
time and facing a lot of problems like environments, natural calamities, geographical
differences etc. Methodologies for survival by protection against various attacks have
been proposed mainly for ad-hoc and sensor networks. It is new for infrastructure
based networks. However, it is not less significant. Therefore, even after working a lot
on different protocols and success up to a certain extent in different situation, swarm
intelligence i.e. nature inspired algorithms are proved to be best because of real time

decisions and tested over time by different species. Swarm Intelligence is best among
all other networks as shown by the results.
REFERENCE
[1] Sudarshan D Shirkande and Rambabu A Vatti, ACO Based Routing Algorithms
for Ad-hoc Network (WSN,MANETs):A Survey,IEEE 2013 International
Conference on Communication Systems and Network Technologies 978-0-7695-
4958-3/13 $26.00 © 2013 IEEE DOI 10.1109/CSNT.2013.56
[2] B.M.G. and P.V.S. Srinivas, SAMR: Swarm Adaptive Multipath Routing
Topology for Load balancing and Congestion Endurance in Mobile Ad hoc
Networks, 2013 Third International Conference on Advanced Computing &
Communication Technologies.
[3] E. Christopher Siddarth1
and K. Seetharaman2
, A Cluster Based QoS-Aware
Service Discovery Architecture for Mobile Ad hoc Networks, 2012 IEEE
International inference on Computational Intelligence and Computing Research.
[4] D. Karthikeyan and M. Dharmalingam,Ant based Intelligent Routing Protocol for
MANET, Proceedings of the 2013 International Conference on Pattern
Recognition, Informatics and Mobile Engineering (PRIME) February 21-22,978-
1-4673-5845-3/13/$31.00©2013 IEEE.
[5] Mohammad Arif1
and Khalid Imam Rahmani2
, Adaptive ARA (AARA) for
MANETs.
[6] Praveenkumar G Hoolimath, Kiran M, G Ram Mohana Reddy Department of
Information, Optimized TERMITE: A Bio-inspired Routing Algo. for MANET’s,
978-1-4673-2014-6/12/$31.00 ©2012 IEEE
[7] Vivekanand Jha, Kritika Khetarpal , Meghna Sharma, A Survey of Nature
Inspired Routing Algorithms for MANETs, 978-1-4244-8679-3/11/$26.00 ©2011
IEEE.
[8] Eslam Al Maghayreh, Salam Abu Al-Haija, Faisal Alkhateeb and Shadi
Aljawarneh , Bees Ants Based Routing Algorithm, 2010 International Conference
on Intelligent Systems, Modelling and Simulation.
[9] L.J.G. Villalba D.R. Can˜as A.L.S. Orozco , Bio-inspired routing protocol for
mobile ad hoc networks, Published in IET Communications, Received on 23rd
December 2009 Revised on 30th May 2010doi: 10.1049/iet-com.2009.0826
ISSN.
[10] M.M.Goswami, R.V. Dharaskar and V.M. Thakare, Fuzzy Ant Colony Based
Routing Protocol For Mobile Ad Hoc Network, 2009 International Conference on
Computer Engineering and Technology, 978-0-7695-3521-0/09 $25.00 © 2009
IEEE, DOI 10.1109/ICCET.2009.168
[11] Sanaz Asadinia, Marjan kuchaki Rafsanjani and Arsham Borumand Saeid, A
Novel Routing Algo. Based-on Ant Colony in Mobile Ad hoc Networks, 978-1-
4244-6709-9/10/$26.00 ©2010 IEEE.

[12] P.Deepalakshmi1, Dr. S. Radhakrishnans ,A Receiver initiated Mesh based
Multicasting for MANETs using ACO, 978-1-4244-9477-4/11/$26.00 ©2011
IEEE
[13] A K Daniel and R Singh, Swarm Intelligence Based Multicast Routing and
Bandwidth Management Protocol for AD-hoc Wireless Network Using
Backpressure Restoration, 978-1-4244-5540-9/10/$26.00 ©2010 IEEE.
[14] ZAFAR H., HARLE D., ANDONOVIC I., KHAWAJA Y.: ‘Performance
evaluation of shortest multipath source routing scheme’, IET Commun., 2009, 3,
(5), pp. 700–713
[15] WANG J., OSAGIE E., THULASIRAMAN P., and THULASIRAM R.K.:
‘HOPNET: a hybrid ant colony optimization routing algo. For mobile adhoc
network’, Ad Hoc Netw., 2009, 7, (4), pp.690–705.
[16] CLAUSEN T., JACQUET P.: ‘Optimized link state routing protocol (OLSR)’.
IETF RFC 3626, October 2003, http://www.ietf.org/rfc/rfc3626.txt
[17] PERKINS C.E., BELDING-ROYER E.M., DAS S.: ‘Ad hoc on-demand distance
vector (AODV) routing’. RFC3561, July 2003, http://tools.ietf.org/html/rfc3561
[18] JONSON D., HU MALTZ D.: ‘The dynamic source routing protocol (DSR) for
mobile ad hoc networks for IPv4’. RFC 4728, February 2007
[19] OGIER R., TEMPLIN F., LEWIS M.: ‘Topology dissemination based on reverse
path forwarding (TBRPF)’. IETF RFC 3684, February 2004,
http://www.ietf.org/rfc/rfc3684.txt
[20] DORIGO M., STU¨ TZLE T.: ‘Ant colony optimization’ (The MIT Press, 2004).
[21] Kusum Nara and Aman Dureja, A Dynamic Approach for Improving
Performance of Intrusion Detection System over MANeT, International Journal
of Computer Engineering and Technology, 4(4), 2013, pp. 61–81.
[22] N. Prathibha Bharathi and B. Mahesh, An Optimistic Sector Oriented Approach
to Mitigate Broadcast Storm Problem in MANeT, International Journal of
Computer Engineering and Technology, 5(8), 2014, pp. 138–143.

Nature-inspired routing protocols for MANETs

Recommended

Recommended

More Related Content

What's hot

What's hot (14)

Viewers also liked

Viewers also liked (9)

Similar to Nature-inspired routing protocols for MANETs

Similar to Nature-inspired routing protocols for MANETs (20)

More from IAEME Publication

More from IAEME Publication (20)

Recently uploaded

Recently uploaded (20)

Nature-inspired routing protocols for MANETs