## 7 Conclusions and Future Work  
In this paper, a literature survey was presented for the most relevant related work that utilizes fuzzy logic to solve the main WSN challenges associated with the cooperative multi-robotic wireless sensor networks, namely, clustering, routing, power, and mobility management. To solve the cluster and cluster head selection process, most of proposed work presented a de-centralized approach to select the cluster head and form the clusters, where each node collects several parameters such as its residual energy, distance to the sink node, and the node's centrality, and runs fuzzy logic rules to decide whether it can be a cluster head or not. Then, on the basis of an election process, the cluster and cluster head are formulated. To solve the routing problem, the related work proposes that each node will collect certain information such as neighbouring nodes' residual energy, link quality, and distance to the sink node, and input them into the fuzzy logic system to find out the optimal route, which maximizes the packet delivery ratio, minimizes the end-to-end delay, and minimizes the nodes' energy consumption. Further, in order to optimize the nodes' energy consumption, several fuzzy-based power management and energy-saving algorithms were presented that aim at optimizing the sleep/wake cycle for the nodes and optimize the nodes' transmission efficiency by increasing the number of sink nodes and optimizing their locations concerning the other nodes. Finally, to address the mobility challenges associated with multi-robotic systems, fuzzy logic is utilized to maintain the nodes' connectivity during mobility. To address the aforementioned challenges, several input parameters were used in the fuzzy logic system such as the data, its sampling time, node remaining energy, throughput-to-workload, the number of dead nodes in the network, and the received signal strength, among others, in order to find optimal clustering, routing, and power-saving strategies, and maintain the mobile nodes' connectivity. In future work, the potential of fuzzy logic in mitigating other WSNs challenges such as interference and spectrum sharing will be investigated. Furthermore, the various approaches for  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solution** & **Reduction Percentage** \\ \hline Wireless CAN & 3.41\% \\ FLMC & 8.62\% \\ Adaptive-FLMC & 60.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Power consumption comparison with an RSSI-based solution.  
Figure 18: Number of handoffs comparison.  
solving CMR-WSN challenges will be compared with each other, focusing on the nodes' computational and memory resources, as well as implementation complexity and the practicality of each approach.  
Conceptualization, A.K.; methodology, A.K.; investigation, A.K., K.R., O.A., K.A.D., A.M.K., Z.Z.; resources, A.K., K.R., O.A., K.A.D., A.M.K., Z.Z.; writing--original draft preparation, A.K., K.R., O.A., K.A.D., A.M.K., Z.Z.; writing--review and editing, A.K., K.R., O.A., K.A.D., A.M.K., Z.Z.  
This work is supported by the North Atlantic Treaty Organization (NATO) Science Peace and Security (SPS). Project title: \"Hybrid Sensor Network for Emergency Critical Scenarios\". Project no. SPS G4936.  
The authors declare no conflict of interest.