Localization based on received signal strength measurement in smart cities using constrained least squares

The global positioning system (GPS) is essential for many internet-of-things applications but is vulnerable to spoofing and jamming attacks that can lead to incorrect location and timing information. This paper proposes a GPS-compromise detection and localization method using received signal strength (RSS) from wireless networks as a low-cost alternative. Although RSS measurements are inherently noisy, they can provide useful location estimates when processed effectively. We formulate a localization problem using noisy RSS data and propose three estimation methods based on a constrained least squares (CLS) criterion. The Cramér–Rao lower bound for mean squared error is also derived to evaluate the performance limits. Simulations based on real-world LoRaWAN data show that the proposed CLS methods achieve lower estimation error, measured by root mean squared error, than the conventional least squares method, albeit at a higher computational cost.