Citation: Eshagh, M. Optimal
Configuration for Monitoring
Stations in a Wireless Localisation
Network Based on Received Signal
Strength Differences. Sensors 2023, 23,
1150. https://doi.org/10.3390/
s23031150
Academic Editor: Elena Simona
Lohan
Received: 25 November 2022
Revised: 5 January 2023
Accepted: 15 January 2023
Published: 19 January 2023
Copyright: © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Optimal Configuration for Monitoring Stations in a Wireless
Localisation Network Based on Received Signal
Strength Differences
Mehdi Eshagh
Department of Engineering Science, University West, 461 86 Trollhättan, Sweden; mehdi.eshagh@hv.se
Abstract:
A smart city is a city equipped with many sensors communicating with each other for
different purposes. Cybersecurity and signal security are important in such cities, especially for
airports and harbours. Any signal interference or attack on the navigation of autonomous vehicles and
aircraft may lead to catastrophes and risks in people’s lives. Therefore, it is of tremendous importance
to develop wireless security networks for the localisation of any radio frequency interferer in smart
cities. Time of arrival, angle of arrival, time-difference of arrivals, received signal strength and
received signal strength difference (RSSD) are known observables used for the localisation of a signal
interferer. Localisation means to estimate the coordinates of an interferer from some established
monitoring stations and sensors receiving such measurements from an interferer. The main goal
of this study is to optimise the geometric configuration of the monitoring stations using a desired
dilution of precision and/or variance-covariance matrix (VCM) for the transmitter’s location based on
the RSSD. The required mathematical models are developed and applied to the Arlanda international
airport of Sweden. Our numerical tests show that the same configuration is achieved based on
dilution of precision and VCM criteria when the resolution of design is lower than 20 m in the
presence of the same constraints. The choice of the pathloss exponent in the mathematical models
of the RSSDs is not important for such low resolutions. Finally, optimisation based on the VCM is
recommended because of its larger redundancy and flexibility in selecting different desired variances
and covariances for the coordinates of the transmitter.
Keywords:
jamming; least-squares method; quadratic optimisation; spoofing; variance-covariance
matrix
1. Introduction
These days the world is experiencing global warming, and one of the ways of tackling
this issue is to create smart cities and autonomous vehicles. A smart city is a city equipped
with different types of active and passive sensors communicating with each other. Cyber
and signal security are tremendously important to keep a smart city safe and prevent any
cyber and signal attacks. This is even more important for autonomous vehicles and their
navigation. Therefore, a smart city should be equipped well with security networks for the
localisation of any signal interference device. In this article, the design of such a security
network based on the received signal strength difference (RSSD) is presented.
Two known types of signal interference are jamming and spoofing. The former means
to transmit a signal into the same or near band as the satellite navigation band to disable
navigation and spoofing stands for transmitting a fake signal [
1
]. Studies have shown that
a simple and cheap spoofer can overtake for e.g., a ship navigation without being detected;
see [
2
,
3
]. Since the power level of the global navigation satellite systems (GNSS) signal is
low, therefore, a weak interference signal can jam a receiver [
1
], see some real examples
in [4–11].
Localisation of an interferer means estimating its coordinates in a pre-defined coordi-
nate system. Some points with known coordinates, which are so-called anchor nodes or
Sensors 2023, 23, 1150. https://doi.org/10.3390/s23031150 https://www.mdpi.com/journal/sensors
