Akuen Akoi Deng

Akuen Akoi Deng

MSc Student in Computer Science at Stockholm University

Publications

Journal Articles

Adaptive AI Task Partitioning and Offloading in Heterogeneous Edge-Cloud Networks

Published in , 2026

This master’s thesis presents REAP, a runtime energy-aware framework for dynamically partitioning and offloading deep neural network inference across heterogeneous edge, fog, and cloud devices. The framework profiles computation and network conditions, evaluates possible model split points, and periodically adapts the execution strategy based on latency, communication overhead, and energy consumption. Evaluated on a physical three-node testbed using VGG-16, AlexNet, and MobileNetV2, REAP reduced total energy consumption by up to 35.82% and end-to-end latency by up to 22.92% compared with static partitioning.

Recommended citation: A. A. Deng and E. Butkus, “Adaptive AI task partitioning and offloading in heterogeneous edge-cloud networks: REAP—Runtime Energy-Aware Adaptive Partitioning and Offloading Framework,” M.Sc. thesis, Dept. Comput. Syst. Sci., Stockholm Univ., Stockholm, Sweden, 2026.
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Gamers’ Information Security Risks Awareness and their Actual Online Behavior

Published in , 2025

This exploratory study examines the relationship between gamers’ awareness of information-security threats and their actual behaviour in online gaming communities. Using an online questionnaire and semi-structured interviews, the research investigated gamers’ experiences with phishing, account theft, authentication practices, credential sharing, and other security risks. The findings suggest that gamers with greater security knowledge tend to show more concern and caution, while previous experience with attacks improves their ability to identify and respond to similar threats. However, even knowledgeable participants engaged in risky practices, such as sharing account credentials with trusted friends or family, demonstrating that awareness does not always result in secure behaviour.

Recommended citation: A. A. Deng and E. Butkus, “Gamers’ information security risks awareness and their actual online behavior: An exploratory study of the relationship between gamers’ understanding of information security risks and their actual online behavior within gaming communities,” Dept. Comput. Syst. Sci., Stockholm Univ., Stockholm, Sweden, 2025.
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Enhancing a Deep Learning Camera-Based Approach for Heart Rate Detection in Vehicles Using Non-Functional Requirements

Published in , 2024

This bachelor’s thesis investigates how non-functional requirements can be used to evaluate and enhance a deep learning model for contactless heart-rate detection in vehicles. Using the PhysNet convolutional neural network, the study focuses on explainability, robustness, and reliability, and evaluates the model through architecture analysis, feature-map visualization, hyperparameter tuning, cross-validation, and noise-intensive testing. The results highlight the importance of assessing machine-learning systems beyond accuracy and demonstrate the trade-offs between noise reduction, generalizability, and model performance.

Recommended citation: A. Golubenko and A. A. Deng, “Enhancing a deep learning camera-based approach for heart rate detection in vehicles using non-functional requirements,” B.Sc. thesis, Dept. Comput. Sci. Eng., Univ. Gothenburg and Chalmers Univ. Technol., Gothenburg, Sweden, 2024.
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Conference Papers

Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum

Published in , 2026

This paper presents an adaptive framework for dynamically partitioning DNN inference across heterogeneous edge, fog, and cloud devices. Evaluated on a physical testbed using VGG16, AlexNet, and MobileNetV2, the framework reduced energy consumption by 27.09–35.82% and end-to-end latency by 6.34–22.92% compared with static partitioning. The work was based on our masters thesis and was submitted and accepted in the 21st International Conference on Availability, Reliability and Security (ARES 2026) Workshop on Security, Availability, and Fault-Tolerance in Edge AI Systems, held August 24 – 27, 2026 in Linköping, Sweden.

Recommended citation: Deng, A. A., Butkus, E., Lapkovskis, A., & Donta, P. K. (2026). Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum. arXiv preprint arXiv:2605.09623.
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