Master thesis: AI/ML based efficient data aggregation in battery-less IoT device network

RISE RESEARCH INSTITUTES OF SWEDEN
December 01, 2022
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Master thesis: AI/ML based efficient data aggregation in battery-less IoT

device network

We are looking for dedicated master's students to join us in the Connected Intelligence Unit at RISE.

The Connected Intelligence Unit is part of RISE Computer Science in Kista. The current research focus is on the Internet of Things and intermittent computing systems. Among the group's key technologies are the IoT network consisting of battery-less devices. The unit conducts projects together with industry and academic partners from Sweden and across the world.

Thesis Description A dense IoT network consisting of many batteryless devices supports numerous user applications. Effective data aggregation in such networks is needed in a timely and energy-efficient manner. This project will focus on developing efficient communication protocols based on machine learning models that are trained to predict the battery-less IoT device availability. The communication (transmit-receive) can be dynamically initiated using the predicted device availability.

Terms:

  • Start Time: As soon as possible
  • Scope: 30 hp
  • Location: RISE Computer Science, Kista, Stockholm
  • Who are you? We expect you to have good programming skills and knowledge of networking, in particular Internet protocols. An interest in machine learning models is also a prerequisite.

    Welcome with your application! If this sounds interesting and you would like to know more, please contact Chetna Singhal (email: chetna.singhal@ri.se). Applications should include a brief personal letter, CV, and recent grades. Candidates are encouraged to send in their application as soon as possible but at the latest on the 15th of December 2022. Suitable applicants will be interviewed as applications are received. We do not accept applications via email.

    Our union representatives are Lazaros Tsantaridis, SACO, 010 516 62 21 and Bertil Svensson, Unionen, 010-516 53 56.

    Master thesis, Machine-learning, IoT network, intermittent computing, data aggregation, RISE, Stockholm

    Om jobbet Ort

    Kista

    Anställningsform

    Visstidsanställning 3-6 månader

    Job type

    Student - examensarbete/praktik

    Kontaktperson

    Chetna Singhal chetna.singhal@ri.se

    Referensnummer

    2022/655

    Sista ansökningsdag

    2022-12-15

    Skicka in din ansökan

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