Specifically, it has been commonly used by external proprietary real-time localization systems (RTLS) such as Ubisene and Sewio [10] to be implemented in a centralized device at the network edge. SDMob can be integrated into such systems and exploit the information gathered through the distributed operation similar to RPL. However, the involvement of an external SDN controller can also raise new challenges such as additional control packets, leading to control overhead and reservation of system resources to robustly handle them [11]. This paper extends our previous work [12], where we had compared SDMob--_Software Defined Mobility management_, powered by either particle filter or unscented Kalman filter, with the baseline RPL and mRPL [13]. Our previous work only partially addressed mobility patterns (linear and circular trajectories), and scalability of the system in different aspects including the number of MNs, anchors, and their topology (density and distance) were not analyzed. In this work, we introduce new mechanisms to handle congestion in dense networks and to support multiple mobile nodes for enabling seamless handoffs in more realistic scenarios. The new mechanisms include smart buffer management to support multiple MNs, new timers for congestion and restructured route-installation packet format. We also evaluate the impact of other system parameters such as path loss variance and the velocity of the MNs. In this work, we have also included the analytical evaluation of the system in comparison with the simulation results. The main contributions of this work are listed below:  
* Enhancement of the SDMob architecture by introducing new timers and route installation format to achieve a better quality of service (QoS) in networks with varying topologies, realistic mobility patterns, and supporting multiple MNs. * Comparison of SDMob with the benchmark ARMOR and baseline RPL. * Modelling of the SDMob architecture through probabilistic analysis and comparing the analytical results with the simulation results. * Evaluation of SDMob in various conditions, considering different mobility patterns, link quality fluctuations as well as network scalability in terms of hop distance from the MN to the controller, number of neighbors, and number of mobile nodes. * Implementation of SDMob in the Cooja/Contiki environment, where the code is available online [[https://github.com/iliar-rabet/sdmob](https://github.com/iliar-rabet/sdmob)]([https://github.com/iliar-rabet/sdmob](https://github.com/iliar-rabet/sdmob)), (accessed on 9 December 2022). The rest of the paper is organized as follows: Section 2 provides a brief description of RPL limitations upon mobility, and outlines some efforts to overcome them. Section 3 describes the SDMob architecture, and the employed filter for data processing. Section 4 takes an analytical approach to evaluate the system and measure the probability of packet loss during handoff. Section 5 describes the simulation environment and performance  
Figure 1: Illustration of mobility management strategies—**(a)** Distributed strategy—Mobile node (\(M\)) does the routing function by processing the Received Signal Strength (\(RSS\)) values from neighbouring Static nodes (S); and **(b)** Centralized strategy—SDN controller at network edge picks the next best parent for \(M\) to relay data packets. evaluation, comparing SDMob with the selected benchmark (ARMOR). Finally, in Section 6 conclusions are drawn.