# Energy-Efficient LoRa Routing for Smart Grids
## 5 Conclusions  
In this paper, a novel cluster head selection protocol called Cum_LEACH was proposed. A novel QKE-ABO optimisation process was proposed to generate multiple optimal paths between sender and receiver nodes when they are placed far apart. Next, a modified MAX algorithm called the SMAx algorithm was proposed to select the best path among the paths specified by the QKE-ABO algorithm. The performance of the Cum_LEACH algorithm was assessed using parameters such as the energy value, dead nodes, and operating nodes as a function of the number of times the data were transmitted by the network, called runs. The performance of the proposed Cum_LAECH was compared with the standard LEACH, DEEC, and SEP algorithms. It was observed that the energy value of the nodes was nonzero in the case of Cum_LEACH, whereas the energy value was zero in the case of the LEACH, DEEC, and SEP algorithms when the number of runswas 5000. At 1000 runs, the energy values were 10.86 J, 1.08 J, 9.53 J, and 1.92 J for the Cum_LEACH, LEACH, DEEC, and SEP algorithms, respectively. The number of dead nodes is another parameter of interest that gives us information about the longevity of a network. It was also shown that the number of dead nodes was very low in the case of the Cum_LEACH protocol compared to the other standard protocols when the number of runs was 1000. At 5000 runs, all the nodes were dead in the case of the Cum_LEACH protocol, whereas in the case where the LEACH, DEEC, and SEP protocols were used, all the nodes were dead at 2390, 2046, and 1536 runs. In the case that the number of active nodes was considered as a performance parameter, Cum_LEACH performed better compared to the other standard protocols. Once the CH selection was performed, for the transmission process, multiple paths were created using the proposed QKE-ABO algorithm, whose performance was compared with the other optimisation algorithms using the fitness value parameter. The fitness value converged after 28 iterations in the case of the QKE-ABO algorithm, whereas the value converged after 35, 53, and 53 iterations for the POA, ALO, and ABO algorithms, respectively. Once the paths were created, to select the best path, the SMAx algorithm was proposed. Its performance was assessed using the fitness value and was compared with the MAX and SSA algorithms. For the SMAx algorithm, the number of iterations taken for the fitness value to converge was 15, whereas it was 18 in the case of MAX and 19 in the case of the SSA algorithm. There was no greatly significant improvement by the proposed SMAX algorithm when compared to the MAX and SSA algorithms. The computational complexity and speed of convergence involved in the proposed algorithms in comparison to the existing algorithms should be taken up as future work, as well as the feature extraction of the AMI sensor nodes using advanced neural networks to detect the potential for intrusion  
Figure 13: Comparative analysis of the proposed SMAx algorithm with existing methods.  
into the AMI, which is predicted over a novel neural network classifier, and for improved performance, along with the development of lightweight security schemes to ensure the integrity and confidentiality of the metring data in SGs.  
Conceptualisation, R.K.R.; methodology, R.K.R.; software, R.K.R.; formal analysis, J.P.D.; investigation, J.P.D.; writing--original draft preparation, R.K.R.; writing--review and editing, R.K.R.; supervision, J.P.D. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by VIT-AP University.  
Not applicable.  
The authors declare no conflict of interest.