## 1 Introduction
The goal is to design an anchor mobility movement model, by which the MA can dynamically form its own path in a way that a maximum number of UNs are able to receive sufficient localization information to become localized. The MA path will be formed based on the real-time information from the topology of the network. The decision of each movement will be determined case-by-case. However, if the MA were to form its path based on a single input, it might affect the outcome of the model. For example, forming the movement path based on the signal strength only could result in a low localization ratio. In a similar way, forming the movement path based on the number of neighbours only might result in increasing the localization error. Thus, it is important to consider multiple criteria in order to balance the different objectives of the model. In this paper, we present a novel dynamic Fuzzy-Logic based Path Planning for mobility-assisted Localization in WSNs (FLPPL) that applies various criteria for the movement decision to form the path of MA. The novelty of this model lies in employing multiple individual inputs in a fuzzy-logic approach for path planning that are important to minimizing the localization error and maximizing the localization ratio. By using a fuzzy-logic, an optimized movement path will be designed in order to achieve the objectives of the process and taking into account the limited movement of the mobile anchor. To the best of our knowledge, we are the first to use a fuzzy-logic based model in path planning for localization in WSNs. The proposed model offers superior results in many metrics in comparison to existing models. Our contribution lies on designing a model for mobility-assisted localization in WSNs that:  
1. For the first time, is formed based on multiple inputs. Using fuzzy-logic for processing the various inputs helps to balance the movement decision, which also helps to improve the localization ratio and the accuracy of the localization process. 2. Ensures that a maximum number of the unknown nodes in the network are able to get the localization information when the distance of movement increases, while considering the limitation of the MA movement. By doing so, a larger quantity of unknown nodes can estimate their own positions in comparison to other models. 3. Offers a competitive localization error. Implementing both the RSSI (Received Signal Strength Indicator) and the distance metrics in a fuzzy logic approach helps to improve the accuracy of the localization. 4. Uses the precision metric for a better evaluation. Precision represents how many specific localization error values are achieved as in [8]. The proposed model offers very high precision in comparison to the other existed models. This paper is organized as follows. In Section 2, we provide an overview of related work. In Section 3, a brief review on Fuzzy-logic (FL) and some of the existing FL's studies in WSNs. Section 4 shows the assumption for the system model. Section 5 presents our proposed model starting with an analysis of the constraints and the objectives, then shows our proposed FL system in details, and finally describe the localization process. In Section 6, we explain the simulation and performance settings, and discuss the evaluation results in Section 7. Section 8, discusses the potential extension of the current model and the future work as well as the limitations of the proposed work. Then, the conclusion part in Section 9 is provided outlining the future works as well.