E. Peltonen, A. Sojan and T. Päivärinta, "Towards Real-time Learning for Edge-Cloud Continuum with Vehicular Computing," 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 2021, pp. 921-926, doi: 10.1109/WF-IoT51360.2021.9595628
Towards real-time learning for edge-cloud continuum with vehicular computing
|Author:||Peltonen, Ella1; Sojan, Arun2; Päivärinta, Tero2|
1Center for Ubiquitous Computing, University of Oulu, Finland
2M3S Research Unit, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022012610328
|Publish Date:|| 2022-01-26
Sensor-driven IoT systems are well-known for their capacity to accelerate massive amounts of data in a comparatively short period of time. To have any use, the information delivery and decision making based on the data require efficient learning models together with dynamically deployed computing and network resources. The current cloud and high-performance computing infrastructures, as well as modern edge computing systems especially in the 5G and beyond networks, can be addressed to resolve these challenges. However, there are several application areas especially in vehicular and urban computing, where just harnessing more computational power does not solve computational and real-time requirements of the modern sensing systems that operate in mobile and context-dependent environments. For now, the mathematical challenges of distributed computing and real-time learning algorithms have not been profoundly addressed in the context of the IoT and real-world sensing applications. Data-driven systems also require giving full attention to information delivery, data management, data cleaning, and sensor fusion technologies that need to be equally distributed and real-time competent as the learning algorithms themselves. New software-defined computing and networking approaches and architectures are required to orchestrate the numerous connected resources dynamically, controllably, and securely along with the evolving needs. The key challenge here is to uniform collaboration between different aspects of the system, from data processing and delivery to the algorithms and learning models, not forgetting the computational capacity and networking capabilities, all this in real-time with real-world applications.
|Pages:||921 - 926|
7th IEEE World Forum on Internet of Things, WF-IoT, 14 June-31 July 2021, New Orleans, LA, USA
IEEE World Forum on Internet of Things
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This research is supported by the Academy of Finland 6Genesis Flagship (grant number 318927) and SMAD Project.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.