Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing. - Eric Samikwa; Thiemo Voigt; Joakim Eriksson (iThings/GreenCom/CPSCom/SmartData/Cybermatics, 2020)

Adaptive Early Exit of Computation for Energy-Efficient and Low-Latency Machine Learning over IoT Networks. - Eric Samikwa; Antonio Di Maio; Torsten Braun (CCNC, 2022)

ARES: Adaptive Resource-Aware Split Learning for Internet of Things. - Eric Samikwa; Antonio Di Maio; Torsten Braun (Comput. Networks, 2022)

Towards Personality Detection and Prediction using Smartphone Sensor Data. - Dimitris Xenakis; Eric Samikwa; Jesutofunmi Ajayi; Antonio Di Maio; Torsten Braun; Katja Schlegel (MedComNet, 2023)

Machine Learning-based Energy Optimisation in Smart City Internet of Things. - Eric Samikwa; Jakob Schärer; Torsten Braun; Antonio Di Maio (MobiHoc, 2023)

Machine Learning-based Energy Optimisation in Smart City Internet of Things. - Eric Samikwa; Jakob Schärer; Torsten Braun; Antonio Di Maio (Zenodo, 2023)

Machine Learning With Computer Networks: Techniques, Datasets, and Models. - Haitham Afifi; Sabrina Pochaba; Andreas Boltres; Dominic Laniewski; Janek Haberer; Leonard Paeleke; Reza Poorzare; Daniel Stolpmann; Nikolas Wehner; Adrian Redder; Eric Samikwa; Michael Seufert (IEEE Access, 2024)

DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT. - Eric Samikwa; Antonio Di Maio; Torsten Braun (IEEE Internet Things J., 2024)

Deep learning methods for poultry disease prediction using images. - George Hope Chidziwisano; Eric Samikwa; Chisomo Daka (Comput. Electron. Agric., 2025)

CSTAR-FL: Stochastic Client Selection for Tree All-Reduce Federated Learning. - Zimu Xu; Antonio Di Maio; Eric Samikwa; Torsten Braun (IEEE Trans. Mob. Comput., 2025)