Optimization Based Self-localization for IoT Wireless Sensor Networks
| Autor: | John Lygeros, Paul N. Beuchat, Alexander Domahidi, Henrik Hesse |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Optimization problem
Computer science Iterative method Computation Real-time computing 020206 networking & telecommunications 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences 0202 electrical engineering electronic engineering information engineering Computer Science::Networking and Internet Architecture Code generation 0101 mathematics Wireless sensor network |
| Zdroj: | WF-IoT |
| Popis: | In this paper we propose an embedded optimization framework for the simultaneous self-localization of all sensors in wireless sensor networks making use of range measurements from ultra-wideband (UWB) signals. Low-power UWB radios, which provide time-of-arrival measurements with decimeter accuracy over large distances, have been increasingly envisioned for realtime localization of IoT devices in GPS-denied environments and large sensor networks. In this work, we therefore explore different non-linear least-squares optimization problems to formulate the localization task based on UWB range measurements. We solve the resulting optimization problems directly using non-linear-programming algorithms that guarantee convergence to locally optimal solutions. This optimization framework allows the consistent comparison of different optimization methods for sensor localization. We propose and demonstrate the best optimization approach for the self-localization of sensors equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for the plug-and-play deployment of the optimal localization algorithm. Numerical results indicate that the proposed approach improves localization accuracy and decreases computation times relative to existing iterative methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|