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  • Ongoing Project:
    FLY-PT

    Mobilize the national aviation industry to transform the future urban air transport
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  • Ongoing Project:
    InSecTT

    Intelligent Secure Trustable Things
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  • Ongoing Project:
    VALU3S

    Verification and Validation of Automated Systems’ Safety and Security
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  • Ongoing Project:
    ADACORSA

    Airborne data collection on resilient system architectures
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Latest Newsmore

25, Feb, 2022

Achievements in Academia

Shashank Gaur successfully defended his PhD dissertation on context-aware sensor networks

14, Dec, 2021

Fundamental Research Activities

Best Paper Award at ICESS 2021

16, Aug, 2021

Achievements in Academia

New ROS book published with Springer has chapter dedicated to CISTER's Cooperative Driving Framework (CopaDrive)

5, Aug, 2021

Achievements in Academia

CISTER uses AI technique in a new way to enable faster data collection using UAVs in large-area sensor networks

The Journal Paper entitled "LSTM-characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-assisted Sensor Network", authored by Kai Li, Wei Ni, Falko Dressler is published in IEEE Internet of Things Journal.

Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSN). This is useful in many real-life situations like agriculture monitoring, forest fire prevention and control, smart traffic management, and other similar situations where there is the need to gather information over very large areas but keeping costs and resource usage to the minimum while guaranteeing an adequate quality of service.

Due to UAV's maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor.

This paper proposes a new deep reinforcement learning based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on Deep Deterministic Policy Gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices.

To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to existing deep reinforcement learning solutions.

29, Jun, 2021

Fundamental Research Activities

Scholarships for Critical Computing Systems Engineering MSc Students

The CISTER Research Centre, located in Porto, Portugal, an international reference in the area of real-time and embedded computing systems, is opening 8-10 MSc-Student positions for candidates already enrolled or that will enroll in the 2021/2022 edition of the MSc Program on Critical Computing Systems Engineering at ISEP, Porto, Portugal.

Successful candidates will perform research activities within ongoing CISTER projects, in parallel with the specific MSc studies. Research grants require exclusivity and will be funded by ISEP, Vortex CoLab or one of the companies collaborating with the MSc program. It is therefore a great opportunity for the candidates to get introduced to what scientific research is, and to interact with professionals from big players (companies and research institutions) also participating in those projects.

Deadline is 9th July 2021, to apply and find out more information about these scholarships, please visit https://www.cister-labs.pt/mscccse/scolarship

11, Jun, 2021

Fundamental Research Activities

Best Work-In-Progress Paper Award at WFCS 2021

9, Apr, 2021

Achievements in Academia

PhD thesis developed at CISTER: Another step towards the applicability of multi-core platform to hard real-time systems

15, Feb, 2021

Achievements in Academia

Paper published in IEEE Sensors Journal

8, Feb, 2021

Achievements in Academia

Journal Paper "Cloud versus Edge Deployment Strategies of Real-Time Face Recognition Inference" published

26, Jan, 2021

Achievements in Academia

New Book on Unmanned Aerial Systems: Theoretical Foundation and Applications published with Elsevier Academic Press Publisher

The book titled "Unmanned Aerial Systems: Theoretical Foundation and Applications" edited by CISTER Researcher Anis Koubâa, and Ahmad Tahar Azar presents some of the latest innovative approaches to drones from the point-of-view of dynamic modeling, system analysis, optimization, control, communications, 3D-mapping, search and rescue, surveillance, farmland and construction monitoring, and more. With the emergence of low-cost UAS, a vast array of research works in academia and products in the industrial sectors have evolved. The book covers the safe operation of UAS, including, but not limited to, fundamental design, mission and path planning, control theory, computer vision, artificial intelligence, applications requirements, and more.

This book provides a unique reference of the state-of-the-art research and development of unmanned aerial systems, making it an essential resource for researchers, instructors and practitioners.

 

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