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Increasing the location accuracy of terminal devices that have the capability of transmitting and receiving radio signal has received considerable research interest in recent times. Device location applications are useful in diverse areas such as agriculture, smart cities, navigation systems, battlefield surveillance, environmental monitoring and control, medical diagnostics, fleet management, etc. as seen in (Hu et al., 2014; Chang et al., 2012; Nguyen et al., 2011; Meng et al., 2011; Yassin et al., 2016; Lee et al., 2017; Ardakani et al., 2020; Aernouts et al., 2020). Most of these applications if not all, apply the same principle in determining the position of an unknown device: fusing location parameters into a positioning estimation algorithm. What differentiates one localization algorithm from another is the way each algorithm handles the measurement error. Location parameters which include angle-of-arrival (AoA), received signal strength (RSS), time-of-arrival (ToA) and its variant can be employed by any location determination applications (Hu et al., 2014; Yassin et al., 2016; Ardakani et al., 2020; Aernouts et al., 2020; Ezema & Ani, 2020; Ezema & Ani, 2017; Janssen et al., 2020; Ezema & Ani, 2016). Using AoA and ToA parameters can lead to small measurement errors. However, the algorithms that employ these parameters require additional hardware on either ends of the node. Also, in AoA-based and ToA-based localization methods, the antenna system requires synchronization in order to increase localization accuracy (Ardakani et al., 2020; Janssen et al., 2020). Most of the available radio devices measure RSS information. Consequently, RSS parameter plays a vital role in formulating many RSS-based localization algorithms found in recent literature.
Recently, using Long Range Wireless Area Network (LoRaWAN) in communicating, locating, and monitoring various Internet of Things (IoT) devices is rapidly increasing (Buurman et al., 2020; Raza et al., 2017). This lays credence to the fact that LoRaWAN standard is highly scalable and is designed to consumed low power (Janssen et al., 2020). The major location dependent parameter that is available for use during localization process is RSS values. When a radio terminal device transmits a message signal to nearby gateways, such signal can be used to locate the terminal device. One of the challenges of using RSS information in formulating RSS-based localization algorithm is the effects of physical environment on the RSS values as distance varies. It has been observed that physical environment such as trees, buildings, meteorological phenomena, among others have been a contributory factors that affects the RSS values as distance varies (Nwawelu et al., 2012; Nwawelu et al., 2014; Ahaneku et al., 2014; Ahaneku et al., 2015). This factor if not well managed during the localization algorithm formulation will contribute significantly to the mean location error of any proposed radio device location estimation algorithm.