Material

Presentation/Event

  • G. Boulougaris, ‘A QoS-aware, Proactive Tasks Offloading Model for Pervasive Applications’, in The 9th International Conference on Future Internet of Things and Cloud (FiCloud), 22-24 Aug 2022, Rome, Italy. link
  • P. Fountas, ‘Query Driven Data Subspace Mapping’, in 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2022), 17 – 20 June, 2022. link
  • P. Fountas, ‘Proactive, Correlation Based Anomaly Detection at the Edge’, in 33rd International Conference on Tools with Artificial Intelligence (ICTAI), November 01-03, 2021. link
  • K. Kolomvatsos, ‘Proactive Data Allocation in Distributed Datasets based on an Ensemble Model’, in 12th International Conference on Information and Communication Systems (ICICS 2021), 24-26 May 2021 Valencia – Spain (Virtual). link
  • K. Kolomvatsos, ‘Landmark based Outliers Detection in Pervasive Applications’, in 12th International Conference on Information and Communication Systems (ICICS 2021), 24-26 May 2021 Valencia – Spain (Virtual). link
  • K. Kolomvatsos, ‘Probabilistic Data Allocation in Pervasive Computing Applications’, in 19th International Conference on Ubiquitous Computing and Communications (IUCC), Exeter, UK, December 17-19, 2020. link
  • K. Kolomvatsos, ‘A Proactive Uncertainty Driven Model for Data Synopses Management in Pervasive Applications’, in 6th IEEE International Conference on Data Science and Systems (DSS), December 14-16, Fiji, 2020. link
  • P. Fountas, K. Kolomvatsos, ‘Ensemble based Data Imputation at the Edge’, in 32nd International Conference on Tools with Artificial Intelligence (ICTAI), November 09-11, 2020. [Best Student Paper Award] link
  • T. Tziouvaras, K. Kolomvatsos, ‘Intelligent Monitoring of Virtualized Services’, in 8th European Conference on Service-Oriented and Cloud Computing, Sept. 2020. link
  • A. Karanika. P. Oikonomou, K. Kolomvatsos, C. Anagnostopoulos, ‘An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge’, in International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) – CD-MAKE 2020, August 25-28, 2020. link
  • P. Fountas, K. Kolomvatsos, ‘A Continuous Data Imputation Mechanism based on Streams Correlation’, in 10th Workshop on Management of Cloud and Smart City Systems, in conjunction with IEEE Symposium on Computers and Communications (ISCC), 2020. link
  • Karanika, A., Oikonomou, P., Kolomvatsos, K., Loukopoulos, T., ‘A Demand-driven, Proactive Tasks Management Model at the Edge’, in IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE World Congress on Computational Intelligence (WCCI), Glasgow, UK, 2020. link
  • Karanika, A., Soula, M., Anagnostopoulos, C., Kolomvatsos, K., Stamoulis, G., ‘Optimized Analytics Query Allocation at the Edge of the Network’, in 12th International Conference on Internet and Distributed Computing Systems, Naples, Italy, Oct. 10-12, 2019. link

Datasets/Code

  • Dataset representing the management of local queues in edge nodes adopted for deciding the offloading of tasks. The dataset can be found here
  • Code for the management of heterogeneity of schemas that IoT devices adopt to report data into a sink node – Find the code here
  • Code for damages detection in buildings upon images – The code is developed in partial fulfillment of the requirements of Nikolaos Grigoropoulos BSc Thesis – Find the code here
  • Code implementing a simulator for the detection of synopses matching between different datsets – Find the code here
  • Code implementing deep learning models for cross roads detection upon camera/video streams – The code is developed in partial fulfillment of the requirements of Pavlos Aplakidis BSc Thesis – Find the code here
  • Code for the implementation of a a robotic arm with wireless control capabilities that executes  sorting algorithms – The code is developed in partial fulfillment of the requirements of Christos Bournazos BSc Thesis – Find the code here
  •  Dataset produced by Dht11 sensors using them inside a minature Greenhouse. Fault values have been removed. Every tuple represents measurements for air Temperature, humitidy, soil temperature retrieved every 3 minutes in a real environment. The physical location of the GreenHouse was in an open space with good airflow and sunlight. The project was performed in partial fulfillment of the requirements for the BSc Thesis of Mr Achilleas Matsoukas. The dataset can be found here
  • Datasets and code for a simple simulator that applies Fuzzy Logic System combined with a Support Vector Machine (SVM) model to handle the uncertainty in deciding the allocation of queries to a set of processing nodes link