A Utilisation of AI Techniques to Manage Energy Consumption in Wireless Sensor Networks

About the Project

Energy consumption in Wireless Sensor Networks (WSNs) is of utmost importance given the low battery resources available in these networks. Existing techniques such as duty-cycling within nodes, clustering and mobility, of both nodes and the sink, have been utilised in order to both reduce energy consumption and more evenly spread consumption across the network. The spread of energy consumption being of particular concern in combatting 'energy holes' in WSNs. Where certain nodes take on a greater network load and, subsequently, run out of power sooner.

This project would aim to investigate how AI can be combined with existing techniques to target these issues. With the ultimate aim being to affect networks in real-time, such that there is an ongoing reaction to the potential of energy holes and excessive energy consumption resulting in longer network lifetime.

Academic qualifications

Have, or expect to achieve by the time of start of the studentship a first-class honours degree, or a distinction at master level, ideally in Subject Group or equivalent with a good fundamental knowledge of Cyber Security, Computer Networking, any subject where algorithmic efficiency features.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

Demonstrated capacity for independent research within a collaborative academic environment.

Good basis in research.

Passionate about network protocols

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact c.thomson3@napier.ac.uk

Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc