ACODV ant colony optimisation distance vectoring routing in Ad hoc networks /
Abstract (Summary)
A mobile ad hoc network is a collection of wireless mobile devices which dynamically
form a temporary network, without using any existing network infrastructure or centralised
administration. Each node in the network effectively becomes a router, and forwards packets
towards the packet’s destination node. Ad hoc networks are characterized by frequently
changing network topology, multi-hop wireless connections and the need for dynamic,
efficient routing protocols.
This work considers the routing problem in a network of uniquely addressable sensors.
These networks are encountered in many industrial applications, where the aim is to relay
information from a collection of data gathering devices deployed over an area to central points.
The routing problem in such networks are characterised by:
The overarching requirement for low power consumption, as battery powered sensors may
be required to operate for years without battery replacement;
An emphasis on reliable communication as opposed to real-time communication, it is
more important for packets to arrive reliably than to arrive quickly; and
Very scarce processing and memory resources, as these sensors are often implemented on
small low-power microprocessors.
This work provides overviews of routing protocols in ad hoc networks, swarm intelligence,
and swarm intelligence applied to ad hoc routing. Various mechanisms that are commonly
encountered in ad hoc routing are experimentally evaluated under situations as close to
real-life as possible. Where possible, enhancements to the mechanisms are suggested and
evaluated. Finally, a routing protocol suitable for such low-power sensor networks is defined
and benchmarked in various scenarios against the Ad hoc On-Demand Distance Vector
(AODV) algorithm.
Bibliographical Information:
Advisor:
School:University of Pretoria/Universiteit van Pretoria
School Location:South Africa
Source Type:Master's Thesis
Keywords:routing computer network management swarm intelligence neural networks science
ISBN:
Date of Publication: