RESEARCH

Research Interests

• Distributed Systems
• Artificial Intelligence

Current Research Projects

• AI in healthcare
  • Real-Time Elderly Fall Detection

• Distributed Systems and Big Data
  • Adaptive Resource Allocation for Streaming Applications in Big Data Infrastructures

Journal Reviews | Web of Science

• ACM Computing Surveys, ISSN 1557-7341, ACM
• IEEE Transactions on Vehicular Technology, ISSN 1939-9359, IEEE
• IEEE Access, ISSN 2169-3536, IEEE
• Journal of Parallel and Distributed Computing, ISSN 0743-7315, Elsevier
• Expert Systems with Applications, ISSN 0957-4174, Elsevier
• Computing, ISSN 1436-5057, Springer
• Peer-to-Peer Networking and Applications, ISSN 1936-6450, Springer
• Wireless Personal Communications, ISSN 1572-834X, Springer
• Sensors, ISSN 1424-8220, MDPI

Technical Program Committees

• MISC '22 (7th International Symposium on Modelling and Implementation of Complex Systems). Oct 30-31, 2022, Mostaganem, Algeria.
• ACM Q2SWinet '21 (17th ACM International Symposium on QoS and Security for Wireless Mobile Networks). Nov 22-26, 2021, Alicante, Spain.
• ACM Q2SWinet '20 (16th ACM International Symposium on QoS and Security for Wireless Mobile Networks). Nov 16-20, 2020, Alicante, Spain.
• MISC '20 (6th International Symposium on Modelling and Implementation of Complex Systems). Oct 24-26, 2020, Batna, Algeria.
• ACM Q2SWinet '19 (15th ACM International Symposium on QoS and Security for Wireless Mobile Networks). Nov 25-29, 2019, Miami Beach, USA.
• ACM Q2SWinet '18 (14th ACM International Symposium on QoS and Security for Wireless Mobile Networks). Oct 28-Nov 2, 2018, Montreal, Canada.

Some Previous Research

• A novel general-purpose big data platform for smart cities. This project focused on the main architectural challenges and solutions that must be considered to ease the continuous design, development, deployment, and operations of cloud-native services for smart cities. The employed technologies included OpenStack, OpenShift (Docker/K8s, Helm), Kafka, MongoDB, Neo4J, Spark, and other DevOps and machine learning tools.
• Big data architecture for connected vehicles (based on the experience and feedback of Groupe PSA/Stellantis). This work is a collaboration between academia and industry. Technical support has been provided by senior data architects at Groupe PSA.
• Optimized usage of MQTT in the context of connected vehicles (MQTT-CV).
• Combining deep learning and IoT technologies to achieve efficient in-city vehicle parking.
• Lightweight deep learning: classical optimized vs. new lightweight architectures.
• Ensuring long-lifetime coverage with circumferential WMSNs.
• Boundary traversal in WSNs.
• Sequential anti-void processing of spatial-window queries in WSNs.  
• Efficient in-network data aggregation from large-scale WSNs.
• Efficient distributed graph traversal. The proposed novel algorithms (namely, Peeling Algorithm, Spreading Aggregation, and Geometric Serial Search) approximate the optimal number of hops in most of the cases (they require n - 1 communications to traverse a network of n nodes). The key idea consists of using a single packet that can jump from node to node and traverse/query any connected network.
• Load-balancing and cluster-based data communication in WSNs.
• Algorithm-architecture adequation.