AI-based Secure Cluster Formation and Reliable Data Transmission for Wireless Sensor Networks
Clustering in wireless sensor networks (WSNs) offers numerous desirable properties, including load balancing, energy conservation, and distributed key management. Secure Clustering requires it to detect compromised nodes and remove them from clusters during setup. Suppose some nodes are attacked and pass the filtering. In that case, they can modify some nodes to adopt a different clustering perspective, as well as initiate new clusters to degrade the overall cluster quality. To address these issues, a new method, Secretary Bird with Self-Organizing Maps (SBWSOM), has been designed to detect and eliminate malicious nodes while efficiently providing data. First, the appropriate sensor nodes were constructed in Python. Second, the malicious node was located and destroyed, and the Cluster Head (CH) was picked based on parameters such as remaining energy, network level, and base station (BS) location. Furthermore, the data rates of chosen CHs have been confirmed and sent to empty nodes. Lastly, the values compared and studied were Latency, throughput, packet delivery ratio (PDR), energy consumption, and transmission loss. The evaluation of this proposal demonstrated improved data transfer, with a throughput of 0.91, an energy consumption of 0.46 mJ, and a packet delivery ratio of 96.3%. Also, the transmit loss was 4.20%, and Latency was 6.04 ms. Overall, this method performed well, with significant improvement over previous models.