## 5 Conclusions and Future Work  
This paper proposed an optimal algorithm for connecting nodes to their equivalent cluster heads. An applicable fitness function is designed considering essential factors of the network. The factors used to evaluate the algorithm include the lifetime and power consumption. The results are compared depending on proposed scenarios according to the position of the BS; S1 (0,0), S2 (50,50), and S3 (100,100). Simulation results show that the proposed AO algorithm has an optimal value compared with the existing algorithms in terms of network lifetime, whereas LEACH and GA have the least network lifetime. This is an important observation of the proposed algorithm over the traditional and metaheuristic algorithms. Furthermore, the network performance in the proposed AO algorithm has the best value of energy consumption compared with the existing algorithms, whereas LEACH and genetic have the least network performance. Thus, the proposed AO algorithm outperforms the traditional and metaheuristic algorithms. In addition, the performance of AO on S2 is better than on S1 and S3 in terms of network lifetime and energy efficiency. Therefore, AO has a positive effect on system stability. In future work, for a secure model, the proposed clustering model will add the security feature to secure data transmission. This paper is the result of a collaboration between different authors. Conceptualization, A.A.T. and L.A.A. ; methodology, A.A.T., L.A.A., S.A.M. and H.O.A. ; software, S.A.M. and H.O.A. ; validation  
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