Efficient Location-Based Tracking for IoT Devices Using Compressive Sensing and Machine Learning Techniques

Ramy Aboushelbaya (), Taimir Aguacil, Qiuting Huang and Peter A. Norreys
Additional contact information
Ramy Aboushelbaya: University of Oxford
Taimir Aguacil: ETH Zurich
Qiuting Huang: ETH Zurich
Peter A. Norreys: University of Oxford

A chapter in High-Dimensional Optimization and Probability, 2022, pp 373-393 from Springer

Abstract: Abstract In this chapter, a scheme based on compressive sensing (CS) for the sparse reconstruction of down-sampled location data is presented for the first time. The underlying sparsity properties of the location data are explored and two algorithms based on LASSO regression and neural networks are shown to be able to efficiently reconstruct paths with only ∼20% sampling of the GPS receiver. An implementation for iOS devices is discussed and results from it are shown as proof of concept of the applicability of CS in location-based tracking for Internet of Things (IoT) devices.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-00832-0_12

Ordering information: This item can be ordered from
http://www.springer.com/9783031008320

DOI: 10.1007/978-3-031-00832-0_12

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().