Localization by directional antennas in industrial Internet of Things

One of the benefits expected by digitalization of production environments is a tremendous increase in flexibility. The same also applies to other applications of the Internet of Things (IoT) such as environmental monitoring or home appliances Localization will become a key topic to reduce engineering efforts but also to give a sensor location awareness, i.e., to assign sensor data a context within a factory environment. This applies both to mobile devices such as AGVs or product identifiers as well as machinery and machine components, which will face the need to be (physically) reconfigured and moved frequently during their life-time.

This research project should investigate novel localization algorithms that can perform localization using directional antennas. Directional antennas offer advantages in suppressing multipath propagation and other disturbances in today’s localization schemes. The basic idea is to find a position estimate that matches the received RSS values at all directed antennas best. This can be done in an iterative algorithm, where initially the error between a set of sampling points in the most probable solution area compared to the measured RSS values is calculated. From the resulting set of error values a gradient field is calculated that can be followed to the global error minima used to further refine the localization. Additionally, by performing a clustering of nodes including quality parameters such as jitter or other variance metrics information is additionally weighted to achieve a higher localization precision.

**This project is partially co-funded by NÖ Forschungs- und Bildungsges.m.b.H. (NFB) within the programme science calls 2017 digitization (FTI 2017).

Details

Duration 01/01/2019 - 30/09/2021
Funding Bundesländer (inkl. deren Stiftungen und Einrichtungen)
Program nfb
Department

Department for Integrated Sensor Systems

Center for Distributed Systems and Sensor Networks

Principle investigator for the project (Danube University Krems) Dipl.-Ing. Albert Treytl
Project members Dipl.-Ing. Thomas Bigler

Publications

Nagy, A.; Bigler, T.; Treytl, A.; Stenzl, R.; Wilker, S.; Sauter, T. (2020). RSS-based Localization for Directional Antennas. In: IEEE, 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): 774-781, IEEE, Wien

Stenzl, R.; Wilker, S.; Sauter, T.; Nagy, A.; Bigler, T.; Treytl, A. (2020). Work-in-Progress: Usage of clustering algorithms for analysis of radio maps for localization using directed antennas. In: IEEE, 2020 16th IEEE International Conference on Factory Communication Systems (WFCS): 1-4, IEEE, Porto, Portugal

Nagy, A.; Bigler, T.; Treytl, A.; Sauter, T. (2019). A Radio-Map clustering Algorithm for RSS based Localization using Directional Antennas. In: Proceedings of 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS): 1-4, IEEE, USA

Mironov, K.; Trishin, S.; Makhmutov, A.; Kartak, V.; Sauter, T. (2019). On Application of Distributed Ledgers for Internet of Things in Russia. Atlantis Press, Advances in Intelligent Systems Research: Proceedings of the VIth International Workshop 'Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Cloud Computing and Cyber Security' (IWCI 2019), 169: 240-245, Atlantis Press, Frankreich

Lectures

RSS-based Localization for Directional Antennas

ETFA 2020, 10/09/2020

A Radio-Map clustering Algorithm for RSS based Localization using Directional Antennas

IEEE Workshop on Factory Communication Systems (WFCS 2019), Sundsvall, Schweden, 29/05/2019

Team

Project partner

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