Architecture for decentralising intelligence in dynamic cloud-fog-edge compute continuum at Edinburgh Napier University on FindAPhD.com

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Dr Amjad Ullah Applications accepted all year round Self-Funded PhD Students Only

Edinburgh United Kingdom Internet Of Things Machine Learning Software Engineering

About the Project

AI-enabled IoT systems are becoming an essential and integral part of our daily lives. For example, systems like e-health, smart homes, and smart manufacturing have already demonstrated a huge impact. This breed of IoT applications raises challenges in regard to efficient data collection and processing strategies, location awareness, reliance on specific and heterogenous computational, adherence to complex data privacy and security objectives, compliance to the ability of IoT devices to provide quality service, social trust (ST) among the owners of IoT devices at different layers, and Low latency [1]–[3]. These challenges raise serious concerns in regard to the commonly used centralized cloud model for such systems. Hence, the dynamic compute continuum spanning across cloud-fog-edge infrastructure has emerged as the new promising model to support the requirements of on-demand access to a shared pool of configurable, heterogenous and dynamic set of computational resources [4], that can be used for the execution of the next generation of IoT systems.

The coordinated management and optimization of these resources is the responsibility of an orchestration solution, which is usually governed by a set of deployment and runtime reconfiguration strategies [5]. These strategies must address the key versatile requirements of IoT applications that include but are not limited to, simultaneous access to a distributed set of resources, dealing with heterogeneity, high dynamicity, diversity of resource types and most importantly uncertainties (e.g., volatile connectivity, mobility etc.) of the underlying distributed environment [6], [7]. Furthermore, the structural topology of an application may also change due to the tasks' completion or changes in the executing environment [8], [9] such as changes in traffic patterns, and resource availability [10], [11]. In such a highly dynamic environment, resources and application components (microservices) need to be deployed and reconfigured to ensure the uninterrupted execution of the IoT applications according to the system's stated QoS objectives. To deal with this highly dynamic and distributed nature of the compute continuum, existing centralized solutions, frequently suffer from scalability, latency, and privacy issues, while typical distributed methods fail in regard to the system's heterogeneity, variability, and uncertainty.

The objective of this research is to seek the development of new solutions based on the decentralisation of intelligence across a dynamic compute continuum. More particularly, this research will be the confluence of three key aspects: Use and retrieval of contextual information across the entire spectrum of the compute continuum, decentralizing architecture for distributing the execution of intelligence in the compute continuum, and use of ML techniques for decision making regarding application deployment, and reconfiguration.

Academic qualifications

A second class honour degree or equivalent qualification in computer science.

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

Experience in fundamental software engineering
Competent in one (or some) programming languages
Knowledge of Cloud, IoT and Microservices architecture
Good written and oral communication skills
Strong motivation, with evidence of independent research skills relevant to the project
Good time management

Application process

Prospective applicants are encouraged to contact the supervisor, Dr Amjad Ullah ([Email Address Removed]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application.

The application must include:

Research project outline of 2 pages (list of references excluded). The outline may provide details about

Background and 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.
Research questions or
Methodology: types of data to be used, approach to data collection, and data analysis methods.
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.
Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
Supporting documents will have to be submitted by successful candidates.
Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here.

Download a copy of the project details here.

References

[1] L. Fotia, F. Delicato, and G. Fortino, “Trust in Edge-based Internet of Things Architectures: State of the Art and Research Challenges,” ACM Comput Surv, vol. 55, no. 9, pp. 1–34, Sep. 2023, doi: 10.1145/3558779.
[2] C. H. Hong and B. Varghese, “Resource management in fog/Edge computing: A survey on architectures, infrastructure, and algorithms,” ACM Comput Surv, vol. 52, no. 5, Sep. 2019, doi: 10.1145/3326066.
[3] J. Wang and L. Wang, “A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment,” J Grid Comput, vol. 19, no. 2, Jun. 2021, doi: 10.1007/s10723-021-09558-y.
[4] P. Pradeep, S. Krishnamoorthy, and A. v. Vasilakos, “A holistic approach to a context-aware IoT ecosystem with Adaptive Ubiquitous Middleware,” Pervasive Mob Comput, vol. 72, Apr. 2021, doi: 10.1016/j.pmcj.2021.101342.
[5] O. Tomarchio, D. Calcaterra, and G. di Modica, “Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks,” Journal of Cloud Computing, vol. 9, no. 1, Dec. 2020, doi: 10.1186/s13677-020-00194-7.
[6] D. Petcu, “SERVICE DEPLOYMENT CHALLENGES IN CLOUD-TO-EDGE CONTINUUM,” vol. 22, no. 3, pp. 313–320, 2021, doi: 10.12694:/scpe.v22i3.1941.
[7] L. Bittencourt et al., “The Internet of Things, Fog and Cloud continuum: Integration and challenges,” Internet of Things (Netherlands), vol. 3–4. Elsevier B.V., pp. 134–155, Oct. 01, 2018. doi: 10.1016/j.iot.2018.09.005.
[8] S. Svorobej, M. Bendechache, F. Griesinger, and J. Domaschka, “Orchestration from the Cloud to the Edge,” 2020, pp. 61–77. doi: 10.1007/978-3-030-41110-7_4.
[9] Z. Wen et al., “Fog Orchestration for Internet of Things Services,” 2017. [Online]. Available: www.computer.org/internet/
[10]T. Yeh and S. Yu, “Realizing dynamic resource orchestration on cloud systems in the cloud-to-edge continuum,” J Parallel Distrib Comput, vol. 160, pp. 100–109, Feb. 2022, doi: 10.1016/j.jpdc.2021.10.006.
[11]A. Mijuskovic, A. Chiumento, R. Bemthuis, A. Aldea, and P. Havinga, “Resource management techniques for cloud/fog and edge computing: An evaluation framework and classification,” Sensors, vol. 21, no. 5. MDPI AG, pp. 1–23, Mar. 01, 2021. doi: 10.3390/s21051832.