Physics

Aerospace engineering

Metric (unit)

Gene

Algorithm

Data mining

Machine learning

Engineering

Gaussian

Artificial intelligence

Chemistry

Range (aeronautics)

Operations management

Line-of-sight

Computer science

Quantum mechanics

Computer vision

Computer security

Biochemistry

Robustness (evolution)

Telecommunications

Internet of Things

Detector

Proceedings of the IEEE

Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.

Soft Information for Localization-of-Things