'trickle' algorithm. In practice, the trickle algorithm is responsible for adapting the transmission frequency of control packets to the rate of the topological changes [2]. Increasing the control packet rate could result in better responsiveness to mobility, but at the cost of higher resource consumption in terms of communication and energy overheads. Nevertheless, since no predictive measure is taken, the mobile nodes' routes are only updated after a period of disconnection, leading to network inaccessibility periods that will cause packets loss and higher delays. A proactive approach to support seamless hand-off in MNs could rely on Bayesian filters (such as Kalman filters) or other predictive techniques to forecast the future position of MNs [3]. Here, a filter is referred to as the methods that estimate the state of a temporal variable, which is usually observed under noisy measurements [4]. It is common to have a fixed infrastructure of static nodes (in fixed and a priori known positions) that estimate the distance from the MNs. MNs are also usually assisted by an _Inertial Measurement Unit_ (IMU) to measure velocity and direction of movement. In such a scenario, it is practical to exploit Bayesian filters to fuse these two sources of information and, thereby, benefit from an accurate localization which leads to improvement in network responsiveness [5]. The Kalman filter has been proved to be unbiased (the average error across all the recursive runs is zero), consistent (the filter is neither overconfident nor under-confident) and optimal (it minimizes the estimation error) [4]. However, when using a Kalman filter, the posterior distribution (after the observations) can be computed in closed form only when the relationship between states and observations is described by a linear function, and the measurement and prediction noise follow a Gaussian distribution [6]. To address nonlinear system models, _Extended Kalman Filter_ (EKF) was introduced which uses _Taylor series_ to linearize the equations, trading for a negligible approximation error. On the other hand, Unscented Kalman Filter (UKF) and Particle filter have shown higher accuracy in prediction with the bi-modal distribution of the noise [4]. In the literature, these filters are integrated with low power routing protocols mostly in distributed environments [7, 8, 9]. The overall architecture of distributed routing algorithms with location prediction in the literature is depicted in Figure 0(a), where the anchors report back to the MN so it can predict its future parent on its own and modify its routing state accordingly. In this scenario, the accuracy of the location prediction significantly impacts the connectivity of the MN, and to implement accurate and predictive models, we require higher computation capacity than what mainstream IoT devices can afford. In practice, the resource-constrained devices barely manage to support simple data filters, such as Kalman filters. The next important limitation is flexibility in configuration, as the position information is required as an input for the localization of MN, and this can be done much easier with the SDN approach compared to a distributed situation where each node should be fed with this information. We argue that these limitations can be alleviated by offloading the computational burden of these data filters to a centralized external entity, such as an edge/fog device or an external Software-Defined Networking (SDN) controller, depicted in Figure 0(b). These centralized devices have sufficient resources while being accessible for IoT devices with reasonable latency. In our context, an edge or fog device is defined as a one-hop resource or service provider. It is placed in close proximity to the client devices to access their resources at a low delay and low accessibility cost. In recent years, there has been a roll-out of edge-based services to improve network reliability, services, and reactivity in the rapidly growing field of IoT applications.