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In graph theory, '''LASCNN''' is a '''L'''ocalized '''A'''lgorithm for '''S'''egregation of '''C'''ritical/'''N'''on-critical '''N'''odes <ref>Muhammad Imran, Mohamed A. Alnuem, Mahmoud S. Fayed, and Atif Alamri. "Localized algorithm for segregation of critical/non-critical nodes in mobile ad hoc and sensor networks." Procedia Computer Science 19 (2013): 1167–1172.</ref> The algorithm worked on the principle of distinguishing between the critical and non-critical nodes for the network connectivity based on limited topology information <ref>N. Javaid, A. Ahmad, M. Imran, A. A. Alhamed and M. Guizani, "BIETX: A new quality link metric for Static Wireless Multi-hop Networks," 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, 2016, pp. 784–789, doi: 10.1109/IWCMC.2016.7577157.</ref>The algorithm find the critical nodes with the partial information within a few hops. <ref>Kim, Beom-Su, Kyong Hoon Kim, and Ki-Il Kim. "A survey on mobility support in wireless body area networks." Sensors 17, no. 4 (2017): 797.</ref>
This algorithm can distinguish the critical nodes of the network with high precision, and the accuracy can reach 90%. The accuracy of this algorithm can reach 100% when identifying non-critical nodes <ref>Zhang, Y.; Zhang, Z.; Zhang, B. A Novel Hybrid Optimization Scheme on Connectivity Restoration Processes for Large Scale Industrial Wireless Sensor and Actuator Networks. Processes 2019, 7, 939. </ref> The performance of LASCNN is scalable and quite competitive compared to other schemes <ref>Kasali, F. A., Y. A. Adekunle, A. A. Izang, O. Ebiesuwa, and O. Otusile. "Evaluation of Formal Method Usage amongst Babcock University Students in Nigeria." Evaluation 5, no. 1 (2016).</ref>
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