Synergy between manet and biological swarm systems

Synergy Between MANET And Biological Swarm Systems

Arunabh Mishra

Sikkim Manipal Institute Of Technology

• Swarm Intelligence

• Existing routing protocols

• Swarm based routing

• Problem characterization

• Network Model

• Conclusion

Swarm Intelligence

• Swarm Intelligence is a property of systems of unintelligent agents of limited individual capabilities exhibiting collectively intelligent behavior.

• An agent is an entity capable of sensing its environment and undertaking simple processing of environmental observations in order to perform an action chosen from those available to it

Biological Swarm Systems

• Nest building in termite or honeybee societies• Foraging in ant colonies• Fish schooling• Bird flocking

1. Nest building in termite or honeybee societies

Biological Swarm Systems

2. Foraging in ant colonies

Biological Swarm Systems

3. Fish Schooling

Biological Swarm Systems

4. Bird Flocking in V formation

Biological Swarm Systems

•Bio-inspiration

– social insect societies

– flocking, shoaling in vertebrates

• Fully distributed control

– usually non-hierarchical control

– individual autonomy

• Activity coordination

– Self-organization

– Explicit, local communication (peer-to-peer)

– Communication through the environment(stigmergy)

• Scalability

• Robustness

The overall response of a system is quite robust and

adaptive wrt changes in environment.

• System cost effectiveness

– simple individuals

– mass production

What is common in these behaviours?

Example : Ant System

• A single ant isn't smart, but their colonies are. • Key to an ant colony, is that no one's in charge. • Even with half a million ants, a colony functions just

fine with no management at all—at least none that we would recognize.

• It relies upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.

Example: A bee hive

• Bee hive is a strong hierarchial entreprise model exists with drones,workers and Queen bee.

• Take for example a relocation of hive, which exhibits the democratic and autocratic hierarchy in bees.

• The scout bees come to a decision by an intelligent voting mechanism, and the site that reaches the majority votes in minimum time gets selected.

Mobile Ad Hoc Network(MANET)

A Mobile Ad Hoc network (MANET) is a collection of wireless mobile

nodes, which dynamically form a temporary network, without using any

existing network infrastructure or centralized administration.

Routing in mobile ad hoc networks:

• Each node is host and router,

• No infrastructures or centralized control

• Nodes might move and join and leave the network at any time

• One shared communication medium

• Short range and noisy transmissions

• Very dynamic and spatial-aware problem

Existing routing protocols

There are three different ways to evaluate and compare performance of mobile ad hoc routing protocols:

1)Based on analysis

2)Based on simulation results

3)By analyzing data from real world

Routing protocols

WIRELESS NETWORKS:1. Wireless Routing Protocol

-A table driven or proactive routing protocol, avoids temporary routing loops.

-It maintains a four routing tables hence uses a significant memory and bandwidth.

2. Optimized Link State Routing(OLSR)- A proactive routing approach. Each node propagates its link

state information to all other nodes in the network, using periodical beacons

3. Ad hoc On Demand Distance Vector Routing(AODV)- It uses periodic beaconing .- It has potentially less routing overheads as destination carry

only destination address and not the whole routing information .

Problem with traditional routing

Routing systems frequently depend upon global information for their

efficient operation.

Problem with global information

(1) Frequently out of date

(2) transmission of the information required from one node to all others consumes considerable network bandwidth

Ant systems do not need such global information, relying instead upon

pheromone traces that are laid down in the network as the ant, or agent,

moves through the network.

What is a swarm based routing?

A key characteristic of swarm intelligence is the ability of agents

(ants) to find optimal (or near optimal) routing (in food gathering

operations for example), where intelligent behavior arises through

indirect communications between the agents, a phenomenon known

as stigmergy.

• Allocating computing resources to a number of

• relatively simple units

• No centralized control

• Units interact in a relatively simple and localized way

Swarm based communication network model

• The routing problem is approached through ‘stigmery’ in biological ants system.

• A set of similar concurrent agents analogous to biological artificial ants called “Bit Ants” work in cooperative manner to solve a routing problem.

• Routing algorithms have the goal of directing traffic from sources to destinations

STANDARD PERFORMANCE METRICS

(1)ThroughputProportional to BER HσThe Quantity of service that the network delivers over a time interval.

(2) Packet DelayProportional to transit time Ht Transit Time gives the QoS of the network.

Pheromone Trail(Y) = Hσ * Ht

We have two packets (Routing Agents)1. routing packet (more priority)2. data packet At regular interval every network nodes emits packets with randomly selected destination. All packets select their next hop proportional to information stored in routing table ie probabilities of selecting a link.

The routing will be determined by through complex interactions of network exploration agents called Bit-Ants.

• We have two packets (Routing Agents) 1. routing packet (more priority) 2. data packet • At regular interval every network nodes emits packets with randomly selected destination.

Step-1 The transmitter launches Bit-Ants to all destinations at regular time interval according to predetermined function.

Step-2 Bit-Ants find a route to the destination based on routing tables.Step-3 They update the routing table in real time.

A packet may discarded at a node due to (a) Expired time-to-live (TTL) (b) Lack of buffer space

Network Modelling

TX B RX

C

D

E

A

Routing table for a node ‘E’

PREVIOUS

NODES

BIT ERROR RATE(Ht)

PACKET DELAY (Hσ)

Y=

Ht* Hσ

B

C

D

0.4

0.2

0.3

0.55

0.30

0.40

0.22

0.06

0.12

B

A

TXRX

B

C

A

TX RXTHREE INTERMEDIATE NODES

TWO INTERMEDIATE NODE

Node dependent path calculation

NUMBER OF

INTERMEDIATE

NODES

NODES IN PATH

POSSIBLE

PATHS

1

2

3

1

2

1,2,3

1

2

3,6,2

Advantages Of Swarm based routing

• Robust to individual failures – the mission/task still succeeds

• Naturally Scalable – can dynamically add/remove units

• Naturally fits many distributed problems

• Best algorithmic performances with problems intrinsically dynamical

LIMITATIONS

(a)The flexibility of the protocol with the other protocols.

(b)The relevant changes will have to be made for implementation on wired and wireless system.

(c) The security concerns.

(d) The implementation of the actual flow, error and congesation control mechanism

CONCLUSION

• In this paper the author has described a novel application of intelligent swarm system.

• The application presented here is unique, the implementation of routing protocols based for MANET .

• The MATLAB implementation of this model• Also the author is looking for if the work can be

extended to wireless mesh networks. (IEEE 802.11s)

Dumb parts, properly connected into a swarm, yield smart results.

Kevin Kelly

Thank You