## 1 Introduction  
Realizing the importance of evolving educational and pedagogical conditions, universities and colleges are aiming to include extensive upgrades and to develop exciting technological services [1; 2; 3; 4]. The concept of Smart Campuses in Smart Cities seeks to integrate beneficial technological interventions such as localization and navigation, asset monitoring, smart attendance, and smart parking systems into the fabric of universities in order to improve learning, research, and managerial and administrative efficiency [5; 6; 7]. These applications require a robust positioning framework to precisely locate objects in indoor and outdoor environments [8; 9]. A significant portion of the campus is disorganized, which can be difficult for not only students, but for faculty, staff, and particularly visitors. Students and visitors can use seamless indoor and outdoor positioning systems to navigate the campus. Outdoor location-based services can be easily achieved by employing global positioning system (GPS). However, in an indoor environment, non-line-of-sight communication, complex building structures, and other obstacles degrade the performance of satellite-based positioning. Thus, there is a strong need for reliable indoor positioning systems (IPSs). Indoor environments are usually highly cluttered, with many obstacles causing signal attenuation and creating blind spots that degrade the localization performance of the IPS [10]. Indoor positioning mechanisms identify the geographical coordinates of objects residing inside complex indoor structures. IPSs can be applied to track children and elderly users with wearable devices inside crowded malls and hospitals [11; 12]. These positioning systems can also be used for military applications, mass rapid transit systems, or indoor areas where finding the location of a device user is mandatory. Numerous approaches have been proposed for IPS without the aid of GPS. Such scenarios take help from anchor nodes. ANs already know their locations; they are either GPS equipped or manually deployed at points with known locations [13]. These localization techniques can be broadly classified into two categories: range-based and range-free [14]. Range-based localization works on radio transmission-based distance estimation, using these distances for position estimations. On the contrary, range-free localization algorithms use network connectivity, previous measurements, or other features that are not dependent on distance estimation [15]. These IPS frameworks mainly rely on three location-dependent parameters: angle, distance, and strength of the received anchor node signal [16]. Angle-based methods depend on the angle-of-arrival (AoA), which is estimated by the AoA of the incoming signals. Time-based methods, including time-of-arrival (ToA) and time-difference-of-arrival (TDoA), depend on source-to-destination signal propagation time. Strength-based methods depend on received signal strength (RSS). AoA-based localization relies on the azimuth angle relative to the target nodes. AoA-based localization accuracy is highly vulnerable to non-line-of-sight multipath effects, in which reflected signals are received at a wrong azimuth angle and degrade the precision of the AoA method. Moreover, AoA requires complex hardware with proper calibration and synchronization for precise position estimation. The ToA method depends on the propagation time duration of the packet from the anchor node to the target node [17]. ToA-based indoor localization is less complex than the AoA method but is dependent on synchronization of the source and the destination node. The ToA value is extracted from the packet at the destination from the labeled timestamps [16]. Synchronization requires the integration of more hardware in the indoor positioning system, which increases the cost of the IPS. The TDoA framework's positioning accuracy is also sensitive to synchronization of the anchor and target nodes.