LASCNN is a Localized Algorithm for Segregation of Critical/Non-critical Nodes [1] The algorithm worked on the principle of distinguishing between the critical and non-critical nodes for the network connectivity based on limited topology information [2]
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 [3]
Pseudocode
LASCNN (MAHSN) (1) For∀𝐴∈𝑀𝐴𝐻𝑆𝑁 (2) If (𝐴→𝐶𝑜𝑛𝑛𝐿𝑖𝑠𝑡.getSize()==1)then (3) 𝐴→SetNonCritical()=LEAF (4) Else (5) Continue = TRUE (6) While (Continue==TRUE) (7) Continue = FALSE (8) For∀𝐴𝑐𝑡𝑖V𝑒𝐶𝑜𝑛𝑛∈𝐶𝑜𝑛𝑛𝐿𝑖𝑠𝑡 (9) If (𝐴∉𝐴𝑐𝑡𝑖V𝑒𝐶𝑜𝑛𝑛)then (10) If (𝐴→𝐶𝑜𝑛𝑛𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠.getSize()==0) (11) 𝐴→𝐶𝑜𝑛𝑛𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠.add(𝐴𝑐𝑡𝑖V𝑒𝐶𝑜𝑛𝑛) (12) Continue = TRUE (13) else (14) If (𝐴𝑐𝑡𝑖V𝑒𝐶𝑜𝑛𝑛∩𝐶𝑜𝑛𝑛𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠==TRUE) (15) 𝐴𝑐𝑡𝑖V𝑒𝐶𝑜𝑛𝑛∪𝐶𝑜𝑛𝑛𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠 (16) Continue = TRUE (17) Endif (18) Endif (19) Endif (20) End For (21) End While (22) Endif (23) If (𝐴→𝐶𝑜𝑛𝑛𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠.getSize()<𝐴→𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠.getSize()) (24) 𝐴→SetCritical()=TRUE (25) else (26) 𝐴→SetNonCritical()=INTERMEDIATE (27) Endif (28)End For
See also
References
- ^ Imran, Muhammad, 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.
- ^ 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.
- ^ 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.
- ^ Alnuem, Mohammed, Nazir Ahmad Zafar, Muhammad Imran, Sana Ullah, and Mahmoud S. Fayed. "Formal specification and validation of a localized algorithm for segregation of critical/noncritical nodes in MAHSNs." International Journal of Distributed Sensor Networks 10, no. 6 (2014): 140973