An investigation of mobile ad-hoc network performance with cognitive attributes applied

Mobile Ad-Hoc Networks (MANETs) are known for their versatility, which is they are capable of supporting many applications. In addition to this versatility MANETs are quick to deploy without need for an existing predefined communications infrastructure. However, although the lack of infrastructure allows for the quick deployment of the data communications network, it adds many factors that hinder packet delivery. Such hindrances occur because of the dynamic topology caused by the mobility of the nodes which results in link breakages. Routing protocols exist that attempt to refresh available routes; however, this is after link breakages have occurred. The nodes also usually have constrained resources (i.e. energy source and limited bandwidth). This thesis presents a novel approach of network behaviour and management by implementing cognitive attributes into a MANET environment. This allows an application to better meet its mission objectives, decreases the end-to-end delay, and increases packet delivery ratio. The network is able to make observations, consider previous actions and consequences of the actions, and make changes based on the prior knowledge and experience. This work also shows how the network can better utilise limited resources such as bandwidth allocation by applying cognitive attributes. Simulations conducted show promising results and prove that an increase in network performance is possible if adopting a cross-layered approach and allow the network to manage and to ‘think’ for itself. Various simulations were run with various scenarios and results are presented without cognition applied, with partial cognition applied and with full cognition applied. A total of 52 simulations were run and from this the results were compared and contrasted. The analysis shows that cognitive attributes does increase network performance in the majority of applications.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Liverpool John Moores University

Year dc:date.issued

2015

Author dc:creator

• Blakeway, S J

Contributors dc:contributor

• Askwith, B
• Merabti, M

• QA75 Electronic computers. Computer science
• QA76 Computer software

DOI dc:identifier.doi

https://doi.org/10.24377/LJMU.t.00004336

OAI identifier oai:identifier

oai:researchonline.ljmu.ac.uk:4336