## 1 Introduction
In addition to these challenges, mobility management is another challenge that is associated with mobile sensor nodes that are witnessing increased growth in usage and adoption in several applications and scenarios [4]. The existence of mobile nodes creates some new challenges in wireless sensor networks such as power consumption, dynamic network topology, mobility of the sink, and localization. The new challenges are strongly connected to a critical issue of mobile sensor networks, that is, network connectivity. Artificial intelligence mechanisms have been utilized in previous WSN research, particularly in routing protocols; clustering algorithms; and power management including fuzzy logic [19], artificial neural network (ANN) [20], neural fuzzy inference system (ANFIS) [21], and optimization algorithms, such as particle swarm optimization (PSO) [22], genetic algorithms [23], gravitational search algorithm (GSA) [24], and bacterial foraging algorithm (BFA) [25]. The innovation of fuzzy and neuro-fuzzy systems in WSNs utilizes soft computing concepts to not only deal with the increment execution of WSNs, but also make them increasingly intelligent. Interestingly, fuzzy logic helps in settling on constant choices without requiring information about the environment [26]. Then again, regular control instruments or approaches, for the most part, need precise and complete knowledge about the environment. Fuzzy logic can likewise be used for settling on a choice (i.e., making a decision), which is dependent on various environmental parameters that are required to be combined as indicated by predefined rules [27; 28]. In particular, fuzzy logic is utilized in clustering for blending distinctive clustering parameters, as indicated by predefined standards or rules and, afterward, choosing cluster heads is dependent on the outcomes acquired. Moreover, as widely reported in the literature, the cluster head election overhead is expected to be substantially reduced through the efficient use of fuzzy logic. The aim of this paper was to highlight the main contributions that utilize fuzzy logic in addressing the major challenges of CMR-WSNs, which were used as a base for the conducted literature review and selected papers. The main contributions of this paper can be summarized as follows:  
* Identification of the main challenges for WSN used in cooperative multi-robotic scenarios, namely, clustering, routing, power management, and mobility management. * Conducting a comprehensive literature review for the papers that propose fuzzy logic-based solutions to the CMR-WSNs' main challenges and providing comparison tables for various fuzzy-based approach solutions and algorithms discussed in the literature. In the literature, several works describe different fuzzy-based solutions for tackling CMR-WSNs' challenges. However, there are very few papers that group these solutions together, summarize them, as well as conduct comparisons between them. The remainder of the paper is organized as follows. Section 2 provides a general technical overview of fuzzy logic. Section 3 summarizes the most recent related works for solving the clustering problem utilizing fuzzy logic. Section 4 presents the related works that utilize fuzzy logic as a reason for addressing the WSN routing challenge. Section 5 discusses how fuzzy logic can be used in optimizing nodes' power management. Section 6 describes the potential of fuzzy logic in managing mobility for CMR-WSNs. Finally, Section 7 concludes the paper and proposes future research directions.