Research and develop a system of data collection from network nodes that involves preprocessing data to allow the node classify the data it generates and identify the most important and irregular data for submission to network management while filtering routine and regular data. This is an important step in the development of scalable network management as it dramatically reduces the scale of data required to be processed centrally.

While working on the principles of a self organising network, research and develop, within existing policy management frameworks, a system to allow network nodes to self manage based on their available data while escalating higher importance issues to central network management.

Apply Machine Learning algorithms to develop a system of service demand prediction and provisioning which allows the network to resize and resource itself, using virtualisation, to serve predicted demand according to parameters such as location, time and specific service demand from specific users or user groups. This is achieved while optimising performance and use of available network and VM resources while minimising overall energy requirements and costs.

Apply Machine Learning algorithms to address network resilience issues. This includes using Supervised ML to identify network errors, faults or conditions such as congestion at both a network wide and a local level and automatically taking mitigating actions to minimise overall impact.

Use anomaly detection algorithms to identify serious security issues such as unauthorised intrusion or fraud and liaise with autonomic network management & policies to formulate and take appropriate action.

Develop a number of demonstrable applications using real-world data gathered via current 4G network nodes which demonstrate the core project innovations, and serve to highlight the exploitation potential of CogNet. The applications will include tests to demonstrate the potential improved performance and capacity that can be achieved by utilising the CogNet algorithms over conventional approaches used in today’s Network Management Systems.