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Synergy between manet and biological swarm systems


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  • 1. Synergy Between MANET And Biological Swarm Systems Arunabh Mishra Sikkim Manipal Institute Of Technology
  • 2.
    • Swarm Intelligence
    • Existing routing protocols
    • Swarm based routing
    • Problem characterization
    • Network Model
    • Conclusion
  • 3. 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
  • 4. Biological Swarm Systems
    • Nest building in termite or honeybee societies
    • Foraging in ant colonies
    • Fish schooling
    • Bird flocking
  • 5. 1. Nest building in termite or honeybee societies Biological Swarm Systems
  • 6. 2. Foraging in ant colonies Biological Swarm Systems
  • 7. 3. Fish Schooling Biological Swarm Systems
  • 8. 4. Bird Flocking in V formation Biological Swarm Systems
  • 9.
    • 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?
  • 10. 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.
  • 11. 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.
  • 12. 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
  • 13. 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
  • 14. Routing protocols
    • 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 .
  • 15. Problem with traditional routing
    • Routing systems frequently depend upon global information for their
    • efficient operation.
    • Problem with global information
    • 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.
  • 16. 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
  • 17. 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
  • 18.
    • (1)Throughput
    • Proportional to BER H σ
    • The Quantity of service that the network delivers over a time interval.
    • (2) Packet Delay
    • Proportional to transit time Ht
    • Transit Time gives the QoS of the network.
    • Pheromone Trail(Y) = H σ * 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.
  • 19.
    • 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
    • Expired time-to-live (TTL) (b) Lack of buffer space
  • 20. Network Modelling TX B RX C D E A
  • 21. Routing table for a node ‘E’ PREVIOUS NODES BIT ERROR RATE (Ht) PACKET DELAY (H σ ) Y= Ht* H σ B C D 0.4 0.2 0.3 0.55 0.30 0.40 0.22 0.06 0.12
  • 23. 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
  • 24. 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
    • (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
    • 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)
  • 27. Dumb parts, properly connected into a swarm, yield smart results. Kevin Kelly
  • 28. Thank You