Docent lecture: Data-Driven and Efficient Algorithms for Enhanced Communication at the Edge

Abstract: Communication is the cornerstone element of many distributed, cyber-physical, or IoT systems (e.g., vehicular networks, smart grid, sensor networks). Transmitting data in the most efficient manner is often a requirement for many applications to reduce costs and boost performance. What is needed in this context are dedicated (i.e., data- or use-case-driven) and efficient algorithms that must be designed, analysed, and experimentally validated, taking into account both the communication network and the distribution and continuous aspect of the generated data.

My research centers on the design, study, and evaluation of those distributed and application-driven algorithms to perform optimisation on a continuous basis and decrease computational burden and/or communication costs. Within this presentation, I will showcase recent advancements using several examples of such data-driven and efficient algorithms in the context of different large-scale distributed applications, such as data management in vehicular networks, sharing energy resources among households at the smart grid's edge, and improving the privacy of video streaming.

The lecture is hybrid:  Zoom link: https://chalmers.zoom.us/j/63233771273?pwd=aDBrZGNIRGFsT2R5Wmd5VHlOTEFFUT09
(zoom meeting ID: 632 3377 1273, password: 333947)