The Connected Intelligence Unit is part of RISE Computer Science in Kista. The current research focus is on the Internet of Things. Among the group's key technologies are the Contiki operating system, uIP stack, ContikiRPL, SICSLoWPAN, and lightweight implementation of IPsec and DTLS. The unit conducts projects together with industry and academic partners from Sweden and across the world.
Modern Internet of Things sensing devices come with a plethora of different sensors and actuators. Making sense out of the sensed values is often a non-trivial task and hence often impossible on resource-constrained sensing devices. The edge, on the other hand, has enough computing resources to execute modern machine learning tasks such as object or activity recognition and classification.
The task of this thesis is to implement a framework for combining edge computing with multi-model and possibly large-scale IoT sensing. The work includes the implementation of one or more applications that are enabled by the combination of IoT sensing and edge computing on a compute capable gateway such as Nvidia Jetson Nano or Google Coral. The evaluation will investigate different resource allocation strategies.
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 in Python and (embedded) C. Basic knowledge or a strong interest in machine learning and in particular deep learning is also a prerequisite.
Welcome with your application!
If this sounds interesting and you would like to know more, please contact Joakim Eriksson, tel. 010 228 43 64 or Thiemo Voigt, tel 010 228 43 48. 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 1st of January 2021. Suitable applicants will be interviewed as applications are received.
Our union representatives are Ingemar Petermann, Sveriges Ingenjörer, 010-228 41 22 and Linda Ikatti, Unionen, 010-516 51 61.
Master thesis, Embedded systems, Machine Learning, Edge computing, Internet of Things, RISE, Stockholm