Ant colony optimisation for dynamic routing in telecommunication


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MSc Project Proposal

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  • In a telecommunication network we have limited resources to carry calls e.g. if a certain town or city for instance becomes an origination or destination point of large amount of calls, than the available routers or switches will not be able to entertain all the calls due to limited set of resources. In such a case, a proportion of calls will be lost because of unavailability of routes. My project deals with such a scenario in telecom networks which use Ant colony optimisation to deal with congestion and use alternate routes available to direct the call to its destination point.
  • These are the contents of my presentation which includes the main objectives of my project, define ant colony optimsation and have I chosen this method or algorithms
  • The main objective is to deal with congestion. And to deal with congestion I will be using dynamic routing.A dynamic routing scheme changes the routes from time to time as a function of previous and ongoing congestion. Dynamic routing uses nodes in such a way that any node which is free for traffic is utilised, in order to cope with the congestion problem e.g. If a call which was made previously, follows a certain route, faces congestion, then, a second call routed through the same route will be directed through another route. In this way we can manage congestion and decrease the number of calls dropped in case of heavy traffic.My project is mainly focused on landline network.
  • If we consider a single ant, it has limited memory and its behavior is random. But if we consider ants collectively, they can perform variety of different tasks with great reliability and consistency. This consistency in behavior results from continuous communication of ants with one another and with the environment. If an ant’s action changes the envoirment of a particular location, the ants will follow this change in that location, this process of communication with the envoirnment is called Stigmergy. This change in envoirnment is caused by a chemical substance released by ants called the Pheromone. This chemical substances is laid by every ant following the route directing other ants to take that particular route.The trails laid by each ant which is the chemical substance acts as a foot print. This foot print laid by earlier passing through ants direct the other ants to follow that route depending upon the intensity of the pheromone laid. The intensity of the pheromone tells other ants whether to take that route or not. If the intensity is higher the route should be taken.In case of networks
  • Inherent parallelismPositive Feedback accounts for rapid discovery of goodsolutionsEfficient for Traveling Salesman Problem and similarproblemsCan be used in dynamic applications (adapts to changessuch as new distances, etc)The Travelling Salesman Problem (TSP) is a deceptively simplecombinatorial problem. It can be stated very simply: A salesman spends his timevisiting N cities (or nodes) cyclically. In one tour he visits each city just once, andfinishes up where he started. In what order should he visit them to minimize thedistance traveled? TSP is applied in many different places such as warehousing,material handling and facility planning.
  • Ant colony optimisation for dynamic routing in telecommunication

    1. 1. Ant Colony Optimisation For Dynamic Routing in Telecommunication Networks<br />Supervisor: NYAMAPFENE, Abel <br />Name: HUSSAIN, Haroon<br />
    2. 2. abstract<br />The purpose of this proposal is to find the shortest path between the origination and the destination points in a telecommunication network regardless of congestion in the network using Ant Colony Optimisation. This algorithm uses alternate routes to reach the destination point if there is congestion in the normal route of a call.<br />
    3. 3. Contents<br />Objectives.<br />Ant Colony Optimisation.<br />Why Ant Colony Optimsation.<br />
    4. 4. Objectives<br />Problems<br />Congestion<br />Dynamic Routing<br />Ability to change routes<br />Utilisation of free nodes<br />Aims<br />Landline Network<br />
    5. 5. Ant colony optimisation<br />Ants <br />Continuous communication with one another<br />Ability to respond to environment <br />Trails<br />Act as footprints for other ants to follow<br />
    6. 6. Why we choose it?<br />Competing Algorithm<br />Adaptivity<br />Make use of multiple paths<br />
    7. 7. Drawbacks<br />Theoretical analysis is difficult<br />Sequences of random decisions (not independent)<br />Probability distribution changes by iteration<br />Research is experimental rather than theoretical<br />Time to convergence uncertain (but convergence is gauranteed!)<br />
    8. 8. Work Plan<br />Literature Review (15 February till 15 April)<br />Travelling Salesman Problem(23 Feb – 20 March)<br />Ant Colony optimisation(28 Feb – 1st April)<br />Algorithms(1st March – 10 April)<br />MATLAB coding (18 April till August)<br />Travelling Salesman Problem(18 April – 20 May)<br />Ant Colony Optimisation(15 May – 30 June)<br />Dynamic Routing(1st July – August)<br />Report Writing (10 July till 1st September)<br />Proof reading (1st September – 5 September)<br />
    9. 9. Questions<br />