## 5 Conclusions and Future Work  
FISs are the best choice for building effective clustering algorithms/techniques for energy-efficient routing protocols in WSN, due to its high ability of combining and effectively blending input parameters to produce proper decisions about CH selections.  
To achieve the best possible results of energy-efficient routing protocols in WSN, it is recommended to utilize every parameter having an effect on the energy efficiency of the WSN routing protocol. Furthermore, it is recommended to integrate them in a way that reflects the extent to which each affects the energy efficiency of the WSN. In this work, we introduced the FL-EEC/D clustering technique for energy-efficient routing protocols. Furthermore, we proposed an efficient fuzzy logic used by this clustering technique to perform CH election. This fuzzy logic utilizes five parameters to determine the strength of each sensor's chance to be a CH. These parameters are: remaining energy of the given sensor node, distance of sensor nodes from the BS, density of other surrounding sensor nodes around the candidate CH, compaction of nodes around the sensor node, and the average of the local consumed energy. We set a condition to control the distribution of CHs over the WSN area by forcing an adaptive minimum separation distance between CHs to guarantee their even distribution. FL-EEC/D was comparatively evaluated by simulating various WSN scenarios against LEACH [12], \(K\)-means-LEACH [25], and FL[1]/D [34] for the metrics of total energy consumption, energy balancing, and network lifetime in terms of FND, 10 PND, QND, and HND. Simulation results  
Figure 19: Percentage of total remaining energy per round for FL-EEC/D, LEACH, \(K\)-means-LEACH, and FL[1]/D.  
Figure 20: The Gini Index of the remaining energy per round for FL-EEC/D, LEACH, \(K\)-means-LEACH, and FL[1]/D.  
show that FL-EEC/D significantly outperforms these approaches in the metrics of network lifetime and energy consumption efficiency in various simulated scenarios. Furthermore, we introduced the idea of adopting the Gini index measurement mean for measuring the extent to which the WSN clustering algorithm has the ability to balance energy consumption through all WSN sensor nodes. We used the Gini index as a fair measurement tool for evaluating the energy efficiency of routing protocols in WSN for the metric of balancing of energy distribution.  
Conceptualization, A.H., M.S. and O.A.-J.; methodology, A.H. and M.S.; software, A.H. and O.A.-J.; supervision, O.A.-J.; validation, M.S. and E.T.; writing, original draft preparation, A.H.; writing, review and editing, E.T.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSN & Wireless Sensor Network \\ CH & Cluster Head \\ BS & Base Station \\ FL-EEC/D & Fuzzy Logic-based Energy-Efficient Clustering based on minimum separation Distance enforcement \\  & between CHs \\ LEACH & Low Energy Adaptive Clustering Hierarchy \\ FIS & Fuzzy Inference System \\ HRP & Hierarchical Routing Protocols \\ PEGASIS & Power-Efficient Gathering in Sensor Information Systems \\ DPSO & Discrete Particle Swarm Optimization \\ FND & First Node Dead \\
10 PND & 10\% of Nodes Dead \\ QND & Quarter of Nodes Dead \\ HND & Half of Nodes Dead \\ \end{tabular}