Range-Based Localization of a Wireless Sensor Network for Internet of Things Using Received Signal Strength Indicator and the Most Valuable Player Algorithm

Wireless sensor networks (WSNs) systems based on Internet of Things (IoT) have developed rapidly in recent years. However, interference is a major obstacle to relatively long-distance communications in such networks. It is also very complicated and challenging to fix the exact location of tags in the IoT sensor networks. To overcome these problems, in this paper, an electronic steering parasitic array radiator (ESPAR) antenna used as a beamformer to handle the interference and extend the communication range from the sensors or tags is suggested. In addition, an efficient method, namely beam scanning (BS), is proposed to find the directions of tags. The beam scanning method (BSM) can be used for the selective beam switching (SBS) system by designing an ESPAR or array of ESPAR antennas with the help of CST studio. The antennas exhibit higher gain (8.17 dBi, 11.40 dBi) and proper radiation pattern at a particular direction. In addition, the MATLAB simulation findings indicate that the proposed BSM algorithm provides longer communication range, i.e., 25 m. In order to maximize range while avoiding interference, it is necessary to determine the direction and precise orientation of the tag in the WSN communication systems. Consequently, this work could be applied to an IoT sensor network such as an electrocardiogram system by providing better advantages such as higher localization accuracy and longer operating range.

where

is the power received by the reader antenna,

is the power transmitted by the sensor side antenna,

and

are the tag and reader antennas gain, λ is the wavelength at the respective operating frequency,

is the path loss of the system, and n is the path loss factor for free space its value is 2.…”

Section: Sir Improvement and Range Extensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding

Rahman

Ryu

2022

“…Bo et al [ 33 ] studied the configuration of sensors for the cooperative localisation of autonomous underwater vehicles using the RSS. Alanezi et al [ 34 ] proposed a method to accurately determine the position and distance of the wireless sensors linked in a local network. The method utilises the RSS at the target node to identify its location in the localised grid system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Configuration for Monitoring Stations in a Wireless Localisation Network Based on Received Signal Strength Differences

Eshagh

2023

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.

“…IoT localization can be categorized in several ways, such as based on measurement methods, range-based localization, and range-free localization, which can be carried out in both indoor and outdoor settings. Range-based IoT localization refers to methods that leverage an IoT device’s distance from one or more reference points to pinpoint its location [ 6 ]. They could be specialized equipment deployed particularly for localization, or they could be other devices fixed in recognized locations such as cell towers or WiFi routers.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Shahbazian

Macrina

Scalzo

et al. 2023

The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper’s main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.